diff --git a/.dockerignore b/.dockerignore
index b56961fd699c330ea748f9652426e83b3c6a9d11..f214e2386c1ea856b76e2a27dd4bac545c0c4558 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -1,43 +1,43 @@
-# Ignore Python cache
-__pycache__/
-*.py[cod]
-*.so
-
-# Ignore Jupyter notebooks (if not used)
-*.ipynb
-.ipynb_checkpoints/
-
-# Ignore logs and temp files
-*.log
-logs/
-*.tmp
-*.DS_Store
-
-# Ignore version control and dev files
-.git/
-.github/
-.vscode/
-*.env
-.env*
-.gitignore
-
-# MLflow & DVC metadata (keep only if you need them at runtime)
-.mlflow/
-.dvc/
-.dvcignore
-
-# CI/CD config files
-tox.ini
-pytest.ini
-setup.cfg
-setup.py
-requirements-dev.txt
-
-# Ignore Docker build context bloat
-*.tar
-*.zip
-*.gz
-*.egg-info/
-
-# Ignore Hugging Face cache
-~/.cache/huggingface/
+# Ignore Python cache
+__pycache__/
+*.py[cod]
+*.so
+
+# Ignore Jupyter notebooks (if not used)
+*.ipynb
+.ipynb_checkpoints/
+
+# Ignore logs and temp files
+*.log
+logs/
+*.tmp
+*.DS_Store
+
+# Ignore version control and dev files
+.git/
+.github/
+.vscode/
+*.env
+.env*
+.gitignore
+
+# MLflow & DVC metadata (keep only if you need them at runtime)
+.mlflow/
+.dvc/
+.dvcignore
+
+# CI/CD config files
+tox.ini
+pytest.ini
+setup.cfg
+setup.py
+requirements-dev.txt
+
+# Ignore Docker build context bloat
+*.tar
+*.zip
+*.gz
+*.egg-info/
+
+# Ignore Hugging Face cache
+~/.cache/huggingface/
diff --git a/Dockerfile b/Dockerfile
index feb2775b9248b84632c764c1745626d071f50dab..30dff0a137d412d2c67c3a7a2345c31c4d73c061 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,43 +1,43 @@
-FROM python:3.11.11-slim-bookworm
-
-RUN apt-get update && \
-    apt-get install --no-install-recommends -y build-essential wget gettext-base && \
-    rm -rf /var/lib/apt/lists/*
-
-WORKDIR /app
-
-COPY . /app/src
-
-# Install dependencies
-RUN pip install --no-cache-dir --upgrade pip && \
-    pip install --no-cache-dir -r src/app/requirements.txt -r src/app/model/requirements.txt
-
-# Prometheus multiprocess directory
-RUN mkdir -p /tmp/prometheus_metrics && \
-    chmod 777 /tmp/prometheus_metrics
-
-# Install Prometheus
-RUN wget https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz && \
-    tar xvf prometheus-2.51.0.linux-amd64.tar.gz && \
-    mv prometheus-2.51.0.linux-amd64/prometheus /usr/local/bin/ && \
-    rm -rf prometheus-2.51.0.linux-amd64*
-
-# Copy startup script
-COPY start.sh /start.sh
-RUN chmod +x /start.sh
-
-# Create non-root user and fix permissions
-RUN useradd -m appuser && \
-    chown -R appuser:appuser /app /tmp/prometheus_metrics /start.sh
-USER appuser
-
-EXPOSE 7860
-
-ENV HOST=0.0.0.0 \
-    PORT=7860 \
-    PYTHONPATH=/app \
-    PYTHONUNBUFFERED=1 \
-    PROMETHEUS_MULTIPROC_DIR=/tmp/prometheus_metrics
-
-# Only ENTRYPOINT
+FROM python:3.11.11-slim-bookworm
+
+RUN apt-get update && \
+    apt-get install --no-install-recommends -y build-essential wget gettext-base && \
+    rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY . /app/src
+
+# Install dependencies
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r src/app/requirements.txt -r src/app/model/requirements.txt
+
+# Prometheus multiprocess directory
+RUN mkdir -p /tmp/prometheus_metrics && \
+    chmod 777 /tmp/prometheus_metrics
+
+# Install Prometheus
+RUN wget https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz && \
+    tar xvf prometheus-2.51.0.linux-amd64.tar.gz && \
+    mv prometheus-2.51.0.linux-amd64/prometheus /usr/local/bin/ && \
+    rm -rf prometheus-2.51.0.linux-amd64*
+
+# Copy startup script
+COPY start.sh /start.sh
+RUN chmod +x /start.sh
+
+# Create non-root user and fix permissions
+RUN useradd -m appuser && \
+    chown -R appuser:appuser /app /tmp/prometheus_metrics /start.sh
+USER appuser
+
+EXPOSE 7860
+
+ENV HOST=0.0.0.0 \
+    PORT=7860 \
+    PYTHONPATH=/app \
+    PYTHONUNBUFFERED=1 \
+    PROMETHEUS_MULTIPROC_DIR=/tmp/prometheus_metrics
+
+# Only ENTRYPOINT
 ENTRYPOINT ["/start.sh"]
\ No newline at end of file
diff --git a/README.md b/README.md
index e0b8e06c7de377abf62ecd71b668446c4aeadf8c..4516e1ded1761efd6d63851c75dffd0df3ab2542 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,15 @@
----
-title: Toxic Tweet Tagger
-emoji: 🤖
-colorFrom: indigo
-colorTo: purple
-sdk: docker
-app_port: 7860
-python_version: "3.11"
-app_file: app.py
-pinned: false
----
-
-# Toxic Tweet Tagger
-
+---
+title: Toxic Tweet Tagger
+emoji: 🤖
+colorFrom: indigo
+colorTo: purple
+sdk: docker
+app_port: 7860
+python_version: "3.11"
+app_file: app.py
+pinned: false
+---
+
+# Toxic Tweet Tagger
+
 A machine learning app that detects toxic tweets and explains predictions using LIME.
\ No newline at end of file
diff --git a/Toxic_TweetTagger.egg-info/PKG-INFO b/Toxic_TweetTagger.egg-info/PKG-INFO
new file mode 100644
index 0000000000000000000000000000000000000000..87b066ae260d548f8b28189ce1e2308347d5b970
--- /dev/null
+++ b/Toxic_TweetTagger.egg-info/PKG-INFO
@@ -0,0 +1,46 @@
+Metadata-Version: 2.4
+Name: Toxic-TweetTagger
+Version: 0.1.0
+Summary: End to end Hate-Tweet detection automation with MLOps implementation
+Home-page: https://github.com/SubinoyBera/Toxic-TweetTagger
+Author: Subinoy Bera
+Author-email: subinoyberadgp@gmail.com
+License: Apache-2.0
+Classifier: Topic :: Engineering/Automation :: ML/MLOps
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.11
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: numpy==2.2.6
+Requires-Dist: pandas==2.3.1
+Requires-Dist: scipy==1.13.1
+Requires-Dist: scikit-learn==1.7.0
+Requires-Dist: xgboost==3.0.2
+Requires-Dist: nltk==3.9.1
+Requires-Dist: python-box==7.3.2
+Requires-Dist: ensure==1.0.4
+Requires-Dist: PyYAML==6.0.2
+Requires-Dist: dvc==3.61.0
+Requires-Dist: mlflow==2.22.1
+Requires-Dist: dagshub==0.5.10
+Requires-Dist: fastapi==0.116.1
+Requires-Dist: pydantic==2.11.7
+Requires-Dist: tqdm==4.67.1
+Requires-Dist: requests==2.32.4
+Requires-Dist: pytest==8.4.1
+Requires-Dist: tox==4.11.3
+Provides-Extra: testing
+Requires-Dist: pytest>=8.0.0; extra == "testing"
+Requires-Dist: black>=25.0.0; extra == "testing"
+Requires-Dist: flake8>=6.0.0; extra == "testing"
+Requires-Dist: mypy>=1.5.0; extra == "testing"
+Requires-Dist: tox>=4.0.0; extra == "testing"
+Dynamic: license-file
+
+# Toxic-TweetTagger
+Hate speech detection
diff --git a/Toxic_TweetTagger.egg-info/SOURCES.txt b/Toxic_TweetTagger.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9ca9c03e3a217e61f0a14d4de15b71289ba4cbac
--- /dev/null
+++ b/Toxic_TweetTagger.egg-info/SOURCES.txt
@@ -0,0 +1,27 @@
+LICENSE
+README.md
+setup.cfg
+setup.py
+src/Toxic_TweetTagger.egg-info/PKG-INFO
+src/Toxic_TweetTagger.egg-info/SOURCES.txt
+src/Toxic_TweetTagger.egg-info/dependency_links.txt
+src/Toxic_TweetTagger.egg-info/requires.txt
+src/Toxic_TweetTagger.egg-info/top_level.txt
+src/components/__init__.py
+src/components/data_ingestion.py
+src/components/data_preprocessing.py
+src/components/feature_engineering.py
+src/components/model_evaluation.py
+src/components/model_training.py
+src/components/register_model.py
+src/constant/__init__.py
+src/constant/constants.py
+src/core/__init__.py
+src/core/config_entity.py
+src/core/configuration.py
+src/core/exception.py
+src/core/logger.py
+src/pipeline/__init__.py
+src/pipeline/ml_pipeline.py
+tests/test_app.py
+tests/test_model.py
\ No newline at end of file
diff --git a/toxictweet_tagger.egg-info/dependency_links.txt b/Toxic_TweetTagger.egg-info/dependency_links.txt
similarity index 100%
rename from toxictweet_tagger.egg-info/dependency_links.txt
rename to Toxic_TweetTagger.egg-info/dependency_links.txt
diff --git a/Toxic_TweetTagger.egg-info/requires.txt b/Toxic_TweetTagger.egg-info/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57c56b71c26074d2be0f1f5d260304f1a2bee338
--- /dev/null
+++ b/Toxic_TweetTagger.egg-info/requires.txt
@@ -0,0 +1,25 @@
+numpy==2.2.6
+pandas==2.3.1
+scipy==1.13.1
+scikit-learn==1.7.0
+xgboost==3.0.2
+nltk==3.9.1
+python-box==7.3.2
+ensure==1.0.4
+PyYAML==6.0.2
+dvc==3.61.0
+mlflow==2.22.1
+dagshub==0.5.10
+fastapi==0.116.1
+pydantic==2.11.7
+tqdm==4.67.1
+requests==2.32.4
+pytest==8.4.1
+tox==4.11.3
+
+[testing]
+pytest>=8.0.0
+black>=25.0.0
+flake8>=6.0.0
+mypy>=1.5.0
+tox>=4.0.0
diff --git a/Toxic_TweetTagger.egg-info/top_level.txt b/Toxic_TweetTagger.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..04c500609dc328f3117d9618e9fa213022de38ab
--- /dev/null
+++ b/Toxic_TweetTagger.egg-info/top_level.txt
@@ -0,0 +1,4 @@
+components
+constant
+core
+pipeline
diff --git a/__pycache__/__init__.cpython-311.pyc b/__pycache__/__init__.cpython-311.pyc
index eb7a0a8e1487940eb1f2c734d22d7bfeffc0367d..a45e9f1919a38762253429b9a8b29ba92acf2a33 100644
Binary files a/__pycache__/__init__.cpython-311.pyc and b/__pycache__/__init__.cpython-311.pyc differ
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diff --git a/app/api/__pycache__/dependencies.cpython-311.pyc b/app/api/__pycache__/dependencies.cpython-311.pyc
index b224918797eb87f9f85672f461be68f1f73f8e09..3ac39a79ad5bf7ea55881a65af0836017eab3380 100644
Binary files a/app/api/__pycache__/dependencies.cpython-311.pyc and b/app/api/__pycache__/dependencies.cpython-311.pyc differ
diff --git a/app/api/__pycache__/schemas.cpython-311.pyc b/app/api/__pycache__/schemas.cpython-311.pyc
index 58d7f3eb7ce06e94acba3e288df523b3c27acdbe..8f73e5143a45d94008f7cd7960085c2d89627989 100644
Binary files a/app/api/__pycache__/schemas.cpython-311.pyc and b/app/api/__pycache__/schemas.cpython-311.pyc differ
diff --git a/app/api/api_routes.py b/app/api/api_routes.py
index eca123f5ec3ecfbfe87445028ff89484c2d536a1..e7171445cc6853868fa81e25a150056973609a18 100644
--- a/app/api/api_routes.py
+++ b/app/api/api_routes.py
@@ -1,39 +1,39 @@
-from fastapi import APIRouter, Request, Depends
-from prometheus_client import generate_latest, CollectorRegistry, multiprocess, CONTENT_TYPE_LATEST
-from fastapi.responses import Response, HTMLResponse
-
-from src.app.services.inference import InferenceService
-from src.app.services.explainer import ExplainerService
-from src.app.services.feedback import FeedbackService
-from src.app.api.dependencies import get_inference_service, get_explainer_service, get_feedback_service
-from src.app.api.schemas import InferenceRequest, InferenceResponse, FeedbackRequest, ExplanationRequest
-
-router = APIRouter()
-
-@router.get("/health")
-def health_check():
-    return {"status": "ok"}
-
-@router.post("/predict", response_model=InferenceResponse)
-def predict(request: Request, payload: InferenceRequest, service: InferenceService = Depends(get_inference_service)):
-    request_id = request.state.request_id
-    return service.predict(request_id, payload.input_tweet, payload.text)
-
-@router.post("/explain")
-def explain(payload: ExplanationRequest, service: ExplainerService = Depends(get_explainer_service)):
-    return HTMLResponse(service.explain(payload.input_tweet))
-
-@router.post("/submit_feedback")
-def submit_feedback(request: Request, payload: FeedbackRequest, service: FeedbackService = Depends(get_feedback_service)):
-    request_id = request.state.request_id
-    return service.submit_feedback(request_id, payload.predicted_label, payload.feedback_label)
-
-@router.get("/metrics")
-def metrics():
-    registry = CollectorRegistry()
-    multiprocess.MultiProcessCollector(registry)
-    return Response(
-        generate_latest(registry),
-        media_type=CONTENT_TYPE_LATEST,
-        headers={"Cache-Control": "no-cache"}
+from fastapi import APIRouter, Request, Depends
+from prometheus_client import generate_latest, CollectorRegistry, multiprocess, CONTENT_TYPE_LATEST
+from fastapi.responses import Response, HTMLResponse
+
+from src.app.services.inference import InferenceService
+from src.app.services.explainer import ExplainerService
+from src.app.services.feedback import FeedbackService
+from src.app.api.dependencies import get_inference_service, get_explainer_service, get_feedback_service
+from src.app.api.schemas import InferenceRequest, InferenceResponse, FeedbackRequest, ExplanationRequest
+
+router = APIRouter()
+
+@router.get("/health")
+def health_check():
+    return {"status": "ok"}
+
+@router.post("/predict", response_model=InferenceResponse)
+def predict(request: Request, payload: InferenceRequest, service: InferenceService = Depends(get_inference_service)):
+    request_id = request.state.request_id
+    return service.predict(request_id, payload.input_tweet, payload.text)
+
+@router.post("/explain")
+def explain(payload: ExplanationRequest, service: ExplainerService = Depends(get_explainer_service)):
+    return HTMLResponse(service.explain(payload.input_tweet))
+
+@router.post("/submit_feedback")
+def submit_feedback(request: Request, payload: FeedbackRequest, service: FeedbackService = Depends(get_feedback_service)):
+    request_id = request.state.request_id
+    return service.submit_feedback(request_id, payload.predicted_label, payload.feedback_label)
+
+@router.get("/metrics")
+def metrics():
+    registry = CollectorRegistry()
+    multiprocess.MultiProcessCollector(registry)
+    return Response(
+        generate_latest(registry),
+        media_type=CONTENT_TYPE_LATEST,
+        headers={"Cache-Control": "no-cache"}
     )
\ No newline at end of file
diff --git a/app/api/dependencies.py b/app/api/dependencies.py
index c198d2127f2e47ce8098a6be6e7bdb53e2e61cdb..800cdc21816d2a1003a311fb2f94d87cbedc3bf9 100644
--- a/app/api/dependencies.py
+++ b/app/api/dependencies.py
@@ -1,17 +1,17 @@
-from fastapi import Request
-
-from src.app.services.inference import InferenceService
-from src.app.services.explainer import ExplainerService
-from src.app.services.feedback import FeedbackService
-
-
-def get_inference_service(request: Request) -> InferenceService:
-    return request.app.state.prediction_service
-
-
-def get_explainer_service(request: Request) -> ExplainerService:
-    return request.app.state.explainer_service
-
-
-def get_feedback_service(request: Request) -> FeedbackService:
+from fastapi import Request
+
+from src.app.services.inference import InferenceService
+from src.app.services.explainer import ExplainerService
+from src.app.services.feedback import FeedbackService
+
+
+def get_inference_service(request: Request) -> InferenceService:
+    return request.app.state.prediction_service
+
+
+def get_explainer_service(request: Request) -> ExplainerService:
+    return request.app.state.explainer_service
+
+
+def get_feedback_service(request: Request) -> FeedbackService:
     return request.app.state.feedback_service
\ No newline at end of file
diff --git a/app/api/schemas.py b/app/api/schemas.py
index 0143d5668252e9230ee621630638c45532049ad5..aa3b957747f80aaac872bda352ecff732a0ba977 100644
--- a/app/api/schemas.py
+++ b/app/api/schemas.py
@@ -1,44 +1,44 @@
-# Schema validation for the API requests and responses
-
-from pydantic import BaseModel, Field
-from typing import Annotated, Dict, Literal, Optional
-from datetime import datetime
-
-
-class InferenceRequest(BaseModel):
-    input_tweet: Annotated[str, Field(..., description="Input tweet or comment text for classification")]
-    text: Annotated[str, Field(..., description="Preprocessed text to be fed to the model for prediction")]
-
-class ExplanationRequest(BaseModel):
-    input_tweet: Annotated[str, Field(..., description="Input tweet or comment text for generating explanation")]
-
-class PredictionResult(BaseModel):
-    label: int
-    confidence: float = Field(..., ge=0.0, le=1.0, description="Prediction probability")
-    toxicity: Literal["strong", "high", "uncertain", "none"]
-
-class ModelInfoSchema(BaseModel):
-    name: str = Field(..., description="Model name")
-    version: int = Field(..., description="Model version")
-    vectorizer: str = Field(..., description="Vectorizer class name")
-
-class MetadataSchema(BaseModel):
-    latency: float = Field(..., ge=0, description="Response time in seconds")
-    usage: Dict[str, float] 
-    model: ModelInfoSchema
-    streamable: bool = Field(default=False)
-    environment: Literal["Standard", "Beta", "Production"]
-    api_version: str
-
-class InferenceResponse(BaseModel):
-    id: str
-    timestamp: datetime
-    object: Literal["text-classification"]
-    prediction: PredictionResult
-    warnings: Optional[dict] = None
-    metadata: MetadataSchema
-
-
-class FeedbackRequest(BaseModel):
-    predicted_label: Literal[0, 1]
+# Schema validation for the API requests and responses
+
+from pydantic import BaseModel, Field
+from typing import Annotated, Dict, Literal, Optional
+from datetime import datetime
+
+
+class InferenceRequest(BaseModel):
+    input_tweet: Annotated[str, Field(..., description="Input tweet or comment text for classification")]
+    text: Annotated[str, Field(..., description="Preprocessed text to be fed to the model for prediction")]
+
+class ExplanationRequest(BaseModel):
+    input_tweet: Annotated[str, Field(..., description="Input tweet or comment text for generating explanation")]
+
+class PredictionResult(BaseModel):
+    label: int
+    confidence: float = Field(..., ge=0.0, le=1.0, description="Prediction probability")
+    toxicity: Literal["strong", "high", "uncertain", "none"]
+
+class ModelInfoSchema(BaseModel):
+    name: str = Field(..., description="Model name")
+    version: int = Field(..., description="Model version")
+    vectorizer: str = Field(..., description="Vectorizer class name")
+
+class MetadataSchema(BaseModel):
+    latency: float = Field(..., ge=0, description="Response time in seconds")
+    usage: Dict[str, float] 
+    model: ModelInfoSchema
+    streamable: bool = Field(default=False)
+    environment: Literal["Standard", "Beta", "Production"]
+    api_version: str
+
+class InferenceResponse(BaseModel):
+    id: str
+    timestamp: datetime
+    object: Literal["text-classification"]
+    prediction: PredictionResult
+    warnings: Optional[dict] = None
+    metadata: MetadataSchema
+
+
+class FeedbackRequest(BaseModel):
+    predicted_label: Literal[0, 1]
     feedback_label: Literal[0, 1]
\ No newline at end of file
diff --git a/app/main.py b/app/main.py
index 7c6a98638f6992b77844e28427027b0cf3ccc2c1..1ad7e976bca28ff52927e89872063355ff776834 100644
--- a/app/main.py
+++ b/app/main.py
@@ -1,86 +1,86 @@
-import sys
-import yaml, json
-from pathlib import Path
-from fastapi import FastAPI
-from contextlib import asynccontextmanager
-
-import xgboost as xgb
-from lime.lime_text import LimeTextExplainer
-from src.core.constants import REGISTERED_MODELS_DIR
-from src.app.workers import BufferedEventConsumerWorker
-from src.app.services.inference import InferenceService
-from src.app.services.feedback import FeedbackService
-from src.app.services.explainer import ExplainerService
-
-from src.utils import load_obj
-from src.app.middleware import http_observability_middleware
-from src.core.constants import DATABASE_NAME, PRODUCTION_COLLECTION_NAME, FEEDBACK_COLLECTION_NAME
-from src.core.mongo_client import MongoDBClient
-from src.core.logger import logging
-from src.core.exception import AppException
-
-from src.app.api.api_routes import router
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    # application state setup
-    try:
-        mongo_client = MongoDBClient()
-        # load model
-        xgb_booster = xgb.Booster()
-        xgb_booster.load_model(Path(REGISTERED_MODELS_DIR, "artifacts", "booster.json"))
-        xgb_booster.set_param({"nthread": 1})
-        # load vectorizer
-        vectorizer = load_obj(Path(REGISTERED_MODELS_DIR, "artifacts"), "vectorizer.joblib")
-
-        with open(Path(REGISTERED_MODELS_DIR, "artifacts/metrics.json"), 'r') as f:
-            metrics = json.load(f)
-            eval_threshold = metrics.get("threshold", 0.5)
-
-        # get model version
-        with open(Path("src/app/model/registered_model_meta"), 'r') as f:
-            model_metadata = yaml.safe_load(f)
-        if not model_metadata:
-            raise FileNotFoundError("Failed to load file having model metadata")
-        model_version = int(model_metadata.get("model_version", 0))
-
-        # initialize workers
-        prediction_event_consumer = BufferedEventConsumerWorker(mongo_client, DATABASE_NAME, PRODUCTION_COLLECTION_NAME)
-        feedback_event_consumer = BufferedEventConsumerWorker(mongo_client, DATABASE_NAME, FEEDBACK_COLLECTION_NAME)
-        
-        # initialize services
-        app.state.prediction_service = InferenceService(xgb_booster, vectorizer, eval_threshold, 
-                                                        prediction_event_consumer, model_version)
-        
-        app.state.feedback_service = FeedbackService(feedback_event_consumer)
-
-        lime_explainer = LimeTextExplainer(class_names=["hate", "non-hate"], bow=False)
-        app.state.explainer_service = ExplainerService(lime_explainer, xgb_booster, vectorizer)
-
-        logging.info("Infernce API app server started successfully")
-
-    except Exception as e:
-        logging.critical(f"Startup Failed: {e}", exc_info=True)
-        raise AppException(e, sys)
-    
-    # run application
-    yield
-    
-    # application shutdown
-    prediction_event_consumer.shutdown()
-    feedback_event_consumer.shutdown()
-    mongo_client.close_connection()
-
-# Create FastAPI app
-app = FastAPI(
-    title="Hate Speech Detection API",
-    version="2.0.0",
-    description="Production-grade ML inference API with monitoring and feedback system.",
-    lifespan=lifespan
-)
-
-# Register API routes
-app.include_router(router, prefix="/api")
-
-# Register middleware
+import sys
+import yaml, json
+from pathlib import Path
+from fastapi import FastAPI
+from contextlib import asynccontextmanager
+
+import xgboost as xgb
+from lime.lime_text import LimeTextExplainer
+from src.core.constants import REGISTERED_MODELS_DIR
+from src.app.workers import BufferedEventConsumerWorker
+from src.app.services.inference import InferenceService
+from src.app.services.feedback import FeedbackService
+from src.app.services.explainer import ExplainerService
+
+from src.utils import load_obj
+from src.app.middleware import http_observability_middleware
+from src.core.constants import DATABASE_NAME, PRODUCTION_COLLECTION_NAME, FEEDBACK_COLLECTION_NAME
+from src.core.mongo_client import MongoDBClient
+from src.core.logger import logging
+from src.core.exception import AppException
+
+from src.app.api.api_routes import router
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # application state setup
+    try:
+        mongo_client = MongoDBClient()
+        # load model
+        xgb_booster = xgb.Booster()
+        xgb_booster.load_model(Path(REGISTERED_MODELS_DIR, "artifacts", "booster.json"))
+        xgb_booster.set_param({"nthread": 1})
+        # load vectorizer
+        vectorizer = load_obj(Path(REGISTERED_MODELS_DIR, "artifacts"), "vectorizer.joblib")
+
+        with open(Path(REGISTERED_MODELS_DIR, "artifacts/metrics.json"), 'r') as f:
+            metrics = json.load(f)
+            eval_threshold = metrics.get("threshold", 0.5)
+
+        # get model version
+        with open(Path("src/app/model/registered_model_meta"), 'r') as f:
+            model_metadata = yaml.safe_load(f)
+        if not model_metadata:
+            raise FileNotFoundError("Failed to load file having model metadata")
+        model_version = int(model_metadata.get("model_version", 0))
+
+        # initialize workers
+        prediction_event_consumer = BufferedEventConsumerWorker(mongo_client, DATABASE_NAME, PRODUCTION_COLLECTION_NAME)
+        feedback_event_consumer = BufferedEventConsumerWorker(mongo_client, DATABASE_NAME, FEEDBACK_COLLECTION_NAME)
+        
+        # initialize services
+        app.state.prediction_service = InferenceService(xgb_booster, vectorizer, eval_threshold, 
+                                                        prediction_event_consumer, model_version)
+        
+        app.state.feedback_service = FeedbackService(feedback_event_consumer)
+
+        lime_explainer = LimeTextExplainer(class_names=["hate", "non-hate"], bow=False)
+        app.state.explainer_service = ExplainerService(lime_explainer, xgb_booster, vectorizer)
+
+        logging.info("Infernce API app server started successfully")
+
+    except Exception as e:
+        logging.critical(f"Startup Failed: {e}", exc_info=True)
+        raise AppException(e, sys)
+    
+    # run application
+    yield
+    
+    # application shutdown
+    prediction_event_consumer.shutdown()
+    feedback_event_consumer.shutdown()
+    mongo_client.close_connection()
+
+# Create FastAPI app
+app = FastAPI(
+    title="Hate Speech Detection API",
+    version="2.0.0",
+    description="Production-grade ML inference API with monitoring and feedback system.",
+    lifespan=lifespan
+)
+
+# Register API routes
+app.include_router(router, prefix="/api")
+
+# Register middleware
 app.middleware("http")(http_observability_middleware)
\ No newline at end of file
diff --git a/app/middleware/__init__.py b/app/middleware/__init__.py
index 7b2ccbf33ba1890b2b1806f284d4554e9ac42cfd..adc6c5a891c2186548df20453e32b77682f2ba5f 100644
--- a/app/middleware/__init__.py
+++ b/app/middleware/__init__.py
@@ -1,66 +1,66 @@
-import time
-import uuid
-from fastapi import Request
-from src.core.logger import logging
-from src.app.monitoring.http_metrics import HTTP_REQUESTS_TOTAL, HTTP_REQUEST_DURATION_SECONDS, HTTP_REQUESTS_IN_PROGRESS
-
-async def http_observability_middleware(request: Request, call_next):
-    # Skip Prometheus metrics endpoint
-    if request.url.path == "/api/metrics":
-        return await call_next(request)
-
-    request_id = request.headers.get("X-Request-ID")
-    if not request_id:
-        request_id = str(uuid.uuid4())
-
-    request.state.request_id = request_id
-
-    method = request.method
-    route = request.scope.get("route")
-    path = route.path if route else request.url.path
-
-    start_time = time.perf_counter()
-
-    logging.info(f"[{request_id}] Incoming request {method} {path}")
-
-    HTTP_REQUESTS_IN_PROGRESS.inc()
-
-    try:
-        response = await call_next(request)
-        status_code = response.status_code
-
-    except Exception:
-        duration = time.perf_counter() - start_time
-
-        HTTP_REQUESTS_TOTAL.labels(
-            method=method,
-            path=path,
-            status="500",
-        ).inc()
-
-        HTTP_REQUEST_DURATION_SECONDS.labels(
-            method=method,
-            path=path,
-        ).observe(duration)
-
-        HTTP_REQUESTS_IN_PROGRESS.dec()
-        raise
-
-    duration = time.perf_counter() - start_time
-
-    HTTP_REQUESTS_TOTAL.labels(
-        method=method,
-        path=path,
-        status=str(status_code),
-    ).inc()
-
-    HTTP_REQUEST_DURATION_SECONDS.labels(
-        method=method,
-        path=path,
-    ).observe(duration)
-
-    HTTP_REQUESTS_IN_PROGRESS.dec()
-
-    response.headers["X-Request-ID"] = request_id
-
+import time
+import uuid
+from fastapi import Request
+from src.core.logger import logging
+from src.app.monitoring.http_metrics import HTTP_REQUESTS_TOTAL, HTTP_REQUEST_DURATION_SECONDS, HTTP_REQUESTS_IN_PROGRESS
+
+async def http_observability_middleware(request: Request, call_next):
+    # Skip Prometheus metrics endpoint
+    if request.url.path == "/api/metrics":
+        return await call_next(request)
+
+    request_id = request.headers.get("X-Request-ID")
+    if not request_id:
+        request_id = str(uuid.uuid4())
+
+    request.state.request_id = request_id
+
+    method = request.method
+    route = request.scope.get("route")
+    path = route.path if route else request.url.path
+
+    start_time = time.perf_counter()
+
+    logging.info(f"[{request_id}] Incoming request {method} {path}")
+
+    HTTP_REQUESTS_IN_PROGRESS.inc()
+
+    try:
+        response = await call_next(request)
+        status_code = response.status_code
+
+    except Exception:
+        duration = time.perf_counter() - start_time
+
+        HTTP_REQUESTS_TOTAL.labels(
+            method=method,
+            path=path,
+            status="500",
+        ).inc()
+
+        HTTP_REQUEST_DURATION_SECONDS.labels(
+            method=method,
+            path=path,
+        ).observe(duration)
+
+        HTTP_REQUESTS_IN_PROGRESS.dec()
+        raise
+
+    duration = time.perf_counter() - start_time
+
+    HTTP_REQUESTS_TOTAL.labels(
+        method=method,
+        path=path,
+        status=str(status_code),
+    ).inc()
+
+    HTTP_REQUEST_DURATION_SECONDS.labels(
+        method=method,
+        path=path,
+    ).observe(duration)
+
+    HTTP_REQUESTS_IN_PROGRESS.dec()
+
+    response.headers["X-Request-ID"] = request_id
+
     return response
\ No newline at end of file
diff --git a/app/middleware/__pycache__/__init__.cpython-311.pyc b/app/middleware/__pycache__/__init__.cpython-311.pyc
index 11c01af8b18f510e0c8d6a5416243476bed4fe7f..32edca86c27468c784b405e05d798a4c8a12ec45 100644
Binary files a/app/middleware/__pycache__/__init__.cpython-311.pyc and b/app/middleware/__pycache__/__init__.cpython-311.pyc differ
diff --git a/app/model/MLmodel b/app/model/MLmodel
index 13f41b22013e97d0decf56f86d5df95a4b7e5473..f172e546711e84bdc8026abb36c6067be0ac9850 100644
--- a/app/model/MLmodel
+++ b/app/model/MLmodel
@@ -1,31 +1,31 @@
-artifact_path: XGB-X
-flavors:
-  python_function:
-    artifacts:
-      booster:
-        path: artifacts/booster.json
-        uri: artifacts/models/booster.json
-      metrics:
-        path: artifacts/metrics.json
-        uri: reports/metrics.json
-      model:
-        path: artifacts/model.joblib
-        uri: artifacts/models/model.joblib
-      vectorizer:
-        path: artifacts/vectorizer.joblib
-        uri: artifacts/models/vectorizer.joblib
-    cloudpickle_version: 3.1.2
-    code: null
-    env:
-      conda: conda.yaml
-      virtualenv: python_env.yaml
-    loader_module: mlflow.pyfunc.model
-    python_model: python_model.pkl
-    python_version: 3.11.15
-    streamable: false
-mlflow_version: 2.22.1
-model_size_bytes: 1869426
-model_uuid: 012be18064704a42aa48c1c5ed8502fd
-prompts: null
-run_id: 82dd63f5ecb04a948006f2b5b30effb6
-utc_time_created: '2026-05-27 17:54:06.414414'
+artifact_path: XGB-v4
+flavors:
+  python_function:
+    artifacts:
+      booster:
+        path: artifacts\booster.json
+        uri: D:\My Projects\Toxic Tweet-Tagger\test_models\booster.json
+      metrics:
+        path: artifacts\metrics.json
+        uri: D:\My Projects\Toxic Tweet-Tagger\test_models\metrics.json
+      model:
+        path: artifacts\model.joblib
+        uri: D:\My Projects\Toxic Tweet-Tagger\test_models\model.joblib
+      vectorizer:
+        path: artifacts\vectorizer.joblib
+        uri: D:\My Projects\Toxic Tweet-Tagger\test_models\vectorizer.joblib
+    cloudpickle_version: 3.1.1
+    code: null
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.pyfunc.model
+    python_model: python_model.pkl
+    python_version: 3.11.5
+    streamable: false
+mlflow_version: 2.22.1
+model_size_bytes: 4509986
+model_uuid: a2c7dc9359894fab899c13a88f9f9a4c
+prompts: null
+run_id: 9b6349ac22e04cf0a22257952299e056
+utc_time_created: '2026-02-17 03:36:56.916972'
diff --git a/app/model/artifacts/booster.json b/app/model/artifacts/booster.json
index eebeedc9088fe5852f809f6e9c147166b42c68a8..3d594d0eb623c3b86dab60ac1b5b1aadca95f2ee 100644
--- a/app/model/artifacts/booster.json
+++ b/app/model/artifacts/booster.json
@@ -1 +1 @@
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diff --git a/app/model/artifacts/metrics.json b/app/model/artifacts/metrics.json
index b0606e304a20dc06c42c25fb999e953b4318ff38..9dbf3183634812e184265e67893676e6c4eb97b9 100644
--- a/app/model/artifacts/metrics.json
+++ b/app/model/artifacts/metrics.json
@@ -1,10 +1 @@
-{
-    "model": "XGB-X",
-    "vectorizer": "tfidf_vectorizer",
-    "threshold": 0.48,
-    "accuracy": 0.497125,
-    "precision": 0.4482758620689655,
-    "recall": 0.002155351073530631,
-    "f1 score": 0.004290075076313835,
-    "roc_auc": 0.46751148647979063
-}
\ No newline at end of file
+{"threshold": 0.48, "accuracy": 0.80601184024014, "precision": 0.7922348788705278, "recall": 0.8272049585392411, "f1 score": 0.8093423478795329, "roc_auc": 0.8852078424195188}
\ No newline at end of file
diff --git a/app/model/artifacts/model.joblib b/app/model/artifacts/model.joblib
index 98eeb34703e88aef5553b0b2daecd03a5dbfdbe9..89744ce0c2efbd3ac909f2a63af7a97d713f485c 100644
--- a/app/model/artifacts/model.joblib
+++ b/app/model/artifacts/model.joblib
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fc79229769fa7f28f5683144dd8f042c237315259062feaf50e6cc3eda444588
-size 495827
+oid sha256:944104aefb800d70fccf6e65e545f3b3fbbbd79382e461ba06f98992f2e686fe
+size 1240274
diff --git a/app/model/artifacts/vectorizer.joblib b/app/model/artifacts/vectorizer.joblib
index 66e547da98a265bdfad39d438ffa36052da62bd8..560d567c8a12929e5231297e388f1c64226ca87c 100644
--- a/app/model/artifacts/vectorizer.joblib
+++ b/app/model/artifacts/vectorizer.joblib
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:700673eac0ebfa2f12a2819b13bd9e701e35bd5ad466f7271d5f7b19b4c97bcf
-size 116444
+oid sha256:1fea91a7b6fb18543f7a7e2717e44135985df11a339efbc30b410dbad8d962fe
+size 116783
diff --git a/app/model/conda.yaml b/app/model/conda.yaml
index 848d52c0acfb4f18e1ab01bf9210840b0570e4e1..dcf576d54cfda69c9577fbb0fb96a09ee82f415c 100644
--- a/app/model/conda.yaml
+++ b/app/model/conda.yaml
@@ -1,15 +1,15 @@
-channels:
-- conda-forge
-dependencies:
-- python=3.11.15
-- pip
-- pip:
-  - mlflow==2.22.1
-  - cloudpickle==3.1.2
-  - numpy==2.2.6
-  - pandas==2.3.1
-  - psutil==7.2.2
-  - scikit-learn==1.7.0
-  - scipy==1.13.1
-  - xgboost==3.0.2
-name: mlflow-env
+channels:
+- conda-forge
+dependencies:
+- python=3.11.5
+- pip<=25.1
+- pip:
+  - mlflow==2.22.1
+  - cloudpickle==3.1.1
+  - numpy==2.2.6
+  - pandas==2.3.1
+  - psutil==7.0.0
+  - scikit-learn==1.6.1
+  - scipy==1.13.1
+  - xgboost==3.0.2
+name: mlflow-env
diff --git a/app/model/python_env.yaml b/app/model/python_env.yaml
index 577237422a9f059c8088e2ee51fdc9fd7600befd..d1b61f8668acabf28e3f16ece1977f3f3db53d2f 100644
--- a/app/model/python_env.yaml
+++ b/app/model/python_env.yaml
@@ -1,7 +1,7 @@
-python: 3.11.15
-build_dependencies:
-- pip
-- setuptools==82.0.1
-- wheel
-dependencies:
-- -r requirements.txt
+python: 3.11.5
+build_dependencies:
+- pip==25.1
+- setuptools==80.9.0
+- wheel==0.45.1
+dependencies:
+- -r requirements.txt
diff --git a/app/model/python_model.pkl b/app/model/python_model.pkl
index dd55cee119e7aaa50381c221acf89a1bfcb565c8..9119e8cac3d52492e1c70f9bee9697a1a6c77a9e 100644
--- a/app/model/python_model.pkl
+++ b/app/model/python_model.pkl
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3cfb849b81f2a70597e85e1a087a9a4dc8eff678ad9e0f1268d8d700168aa1d1
-size 612315
+oid sha256:a6472cb967ef181289c33621e9dbfd2437a53589d1ca75de4307ba20a0bae7ef
+size 1378921
diff --git a/app/model/registered_model_meta b/app/model/registered_model_meta
index 45de68deaa4221f986ca5f3207287f52447b6892..e59a991de55a1add1c06b0febd3a3a92324e1b9e 100644
--- a/app/model/registered_model_meta
+++ b/app/model/registered_model_meta
@@ -1,2 +1,2 @@
-model_name: ToxicTagger-Models
-model_version: '42'
+model_name: ToxicTagger-Models
+model_version: '26'
diff --git a/app/model/requirements.txt b/app/model/requirements.txt
index 31a839d572d30ea9c630a08497e16b22142cfa1c..226762537303b4e850aaf9fcc430d602b8cfc710 100644
--- a/app/model/requirements.txt
+++ b/app/model/requirements.txt
@@ -1,8 +1,8 @@
-mlflow==2.22.1
-cloudpickle==3.1.2
-numpy==2.2.6
-pandas==2.3.1
-psutil==7.2.2
-scikit-learn==1.7.0
-scipy==1.13.1
+mlflow==2.22.1
+cloudpickle==3.1.1
+numpy==2.2.6
+pandas==2.3.1
+psutil==7.0.0
+scikit-learn==1.6.1
+scipy==1.13.1
 xgboost==3.0.2
\ No newline at end of file
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Binary files a/app/monitoring/__pycache__/__init__.cpython-311.pyc and b/app/monitoring/__pycache__/__init__.cpython-311.pyc differ
diff --git a/app/monitoring/__pycache__/http_metrics.cpython-311.pyc b/app/monitoring/__pycache__/http_metrics.cpython-311.pyc
index a5b4e778be78467e71728b36cf58fc353df98a14..352a4922741b45444903e1e5a0d786310fd72b83 100644
Binary files a/app/monitoring/__pycache__/http_metrics.cpython-311.pyc and b/app/monitoring/__pycache__/http_metrics.cpython-311.pyc differ
diff --git a/app/monitoring/__pycache__/service_metrics.cpython-311.pyc b/app/monitoring/__pycache__/service_metrics.cpython-311.pyc
index 4c7d664c43de28681cdfcadec59816925d6c11ae..5b9a9dbc4cd2ad63b6a60a302e40075295e57ee9 100644
Binary files a/app/monitoring/__pycache__/service_metrics.cpython-311.pyc and b/app/monitoring/__pycache__/service_metrics.cpython-311.pyc differ
diff --git a/app/monitoring/http_metrics.py b/app/monitoring/http_metrics.py
index 76e62beba0d0af42ea6a25220b596dcfa92491e1..ac588a72f6e84309cfb0c30cebcab85df3ca16ce 100644
--- a/app/monitoring/http_metrics.py
+++ b/app/monitoring/http_metrics.py
@@ -1,20 +1,20 @@
-from prometheus_client import Counter, Histogram, Gauge
-
-
-HTTP_REQUESTS_TOTAL = Counter(
-    "http_requests_total",
-    "Total number of HTTP requests handled by the inference service",
-    ["method", "path", "status"],
-)
-
-HTTP_REQUEST_DURATION_SECONDS = Histogram(
-    "http_request_duration_seconds",
-    "HTTP request latency in seconds",
-    ["method", "path"],
-    buckets=(0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0),
-)
-
-HTTP_REQUESTS_IN_PROGRESS = Gauge(
-    "http_requests_in_progress",
-    "Number of HTTP requests currently being processed",
+from prometheus_client import Counter, Histogram, Gauge
+
+
+HTTP_REQUESTS_TOTAL = Counter(
+    "http_requests_total",
+    "Total number of HTTP requests handled by the inference service",
+    ["method", "path", "status"],
+)
+
+HTTP_REQUEST_DURATION_SECONDS = Histogram(
+    "http_request_duration_seconds",
+    "HTTP request latency in seconds",
+    ["method", "path"],
+    buckets=(0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0),
+)
+
+HTTP_REQUESTS_IN_PROGRESS = Gauge(
+    "http_requests_in_progress",
+    "Number of HTTP requests currently being processed",
 )
\ No newline at end of file
diff --git a/app/monitoring/service_metrics.py b/app/monitoring/service_metrics.py
index 5a9016df33da9a6bf21b580e5f61d677ecfa6fe8..9f65e4d3b40fa2e61fe613cd91d76b473a792e4f 100644
--- a/app/monitoring/service_metrics.py
+++ b/app/monitoring/service_metrics.py
@@ -1,62 +1,62 @@
-from prometheus_client import Counter, Histogram
-
-# Requests successfully served
-PREDICTION_REQUEST_SUCCESS = Counter(
-    "predict_requests_success_total",
-    "Total successful prediction responses"
-)
-
-# Requests failed
-PREDICTION_REQUEST_FAILED = Counter(
-    "predict_requests_failed_total",
-    "Total failed prediction requests"
-)
-
-# Prediction class distribution (hate / non-hate)
-PREDICTION_CLASS = Counter(
-    "prediction_class_total",
-    "Count of predicted classes",
-    ["class_label"]         # label dimension
-)
-
-# Prediction label confidence
-PREDICTION_CONFIDENCE = Histogram(
-    "prediction_confidence",
-    "Confidence distribution by predicted class",
-    ["class_label"],
-    buckets=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
-)
-
-# Inference responce time
-INFERENCE_LATENCY = Histogram(
-    "model_inference_seconds",
-    "Model inference time in seconds",
-    buckets=[0.005, 0.01, 0.02, 0.03, 0.05, 0.1]
-)
-
-EXPLAINER_REQUEST_SUCCESS = Counter(
-    "explainer_requests_success_total",
-    "Total successful explain requests"
-)
-
-EXPLAINER_REQUEST_FAILED = Counter(
-    "explainer_requests_failed_total",
-    "Total failed explain requests"
-)
-
-# Feedback counter
-FEEDBACK_REQUEST_SUCCESS = Counter(
-    "feedback_submissions_success_total",
-    "Total successful feedback submissions"
-)
-
-USER_PREDICTION_FEEDBACK = Counter(
-    "user_prediction_feedback_total",
-    "User feedback indicating whether the model prediction was correct",
-    ["feedback"]
-)
-
-FEEDBACK_REQUEST_FAILED = Counter(
-    "feedback_submissions_failed_total",
-    "Total failed feedback submissions"
+from prometheus_client import Counter, Histogram
+
+# Requests successfully served
+PREDICTION_REQUEST_SUCCESS = Counter(
+    "predict_requests_success_total",
+    "Total successful prediction responses"
+)
+
+# Requests failed
+PREDICTION_REQUEST_FAILED = Counter(
+    "predict_requests_failed_total",
+    "Total failed prediction requests"
+)
+
+# Prediction class distribution (hate / non-hate)
+PREDICTION_CLASS = Counter(
+    "prediction_class_total",
+    "Count of predicted classes",
+    ["class_label"]         # label dimension
+)
+
+# Prediction label confidence
+PREDICTION_CONFIDENCE = Histogram(
+    "prediction_confidence",
+    "Confidence distribution by predicted class",
+    ["class_label"],
+    buckets=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+)
+
+# Inference responce time
+INFERENCE_LATENCY = Histogram(
+    "model_inference_seconds",
+    "Model inference time in seconds",
+    buckets=[0.005, 0.01, 0.02, 0.03, 0.05, 0.1]
+)
+
+EXPLAINER_REQUEST_SUCCESS = Counter(
+    "explainer_requests_success_total",
+    "Total successful explain requests"
+)
+
+EXPLAINER_REQUEST_FAILED = Counter(
+    "explainer_requests_failed_total",
+    "Total failed explain requests"
+)
+
+# Feedback counter
+FEEDBACK_REQUEST_SUCCESS = Counter(
+    "feedback_submissions_success_total",
+    "Total successful feedback submissions"
+)
+
+USER_PREDICTION_FEEDBACK = Counter(
+    "user_prediction_feedback_total",
+    "User feedback indicating whether the model prediction was correct",
+    ["feedback"]
+)
+
+FEEDBACK_REQUEST_FAILED = Counter(
+    "feedback_submissions_failed_total",
+    "Total failed feedback submissions"
 )
\ No newline at end of file
diff --git a/app/requirements.txt b/app/requirements.txt
index e45b4db01f2814fd7230d6244386efbea51c1b6c..2e8c4250fabf80cc3f4628e62ef19ff12d47fb21 100644
--- a/app/requirements.txt
+++ b/app/requirements.txt
@@ -1,21 +1,21 @@
-fastapi==0.116.1
-uvicorn==0.35.0
-joblib==1.5.1
-PyYAML==6.0.2
-lime==0.2.0.1
-gunicorn==23.0.0
-prometheus-client==0.23.1
-nltk==3.9.1
-pymongo==4.14.0
-python-box==7.3.2
-python-dotenv==1.1.1
-python-multipart==0.0.20
-ensure==1.0.4
-evidently==0.7.12
-PyYAML==6.0.2
-dagshub==0.5.10
-fastapi==0.116.1
-gunicorn==23.0.0
-pydantic==2.11.7
-tqdm==4.67.1
-requests==2.32.4
+fastapi==0.116.1
+uvicorn==0.35.0
+joblib==1.5.1
+PyYAML==6.0.2
+lime==0.2.0.1
+gunicorn==23.0.0
+prometheus-client==0.23.1
+nltk==3.9.1
+pymongo==4.14.0
+python-box==7.3.2
+python-dotenv==1.1.1
+python-multipart==0.0.20
+ensure==1.0.4
+evidently==0.7.12
+PyYAML==6.0.2
+dagshub==0.5.10
+fastapi==0.116.1
+gunicorn==23.0.0
+pydantic==2.11.7
+tqdm==4.67.1
+requests==2.32.4
diff --git a/app/services/__pycache__/__init__.cpython-311.pyc b/app/services/__pycache__/__init__.cpython-311.pyc
index b556ebee1d59f3cdd0f8a20497383a134b2e6558..0d24bc976f4b2d72b983b95c491b022d8dbaaad2 100644
Binary files a/app/services/__pycache__/__init__.cpython-311.pyc and b/app/services/__pycache__/__init__.cpython-311.pyc differ
diff --git a/app/services/__pycache__/explainer.cpython-311.pyc b/app/services/__pycache__/explainer.cpython-311.pyc
index ce6be5d71a0d61c27eed0a3669dd0950be4846c1..3ee5f55bf6e27506cf38d7433b1312ce803941d6 100644
Binary files a/app/services/__pycache__/explainer.cpython-311.pyc and b/app/services/__pycache__/explainer.cpython-311.pyc differ
diff --git a/app/services/__pycache__/feedback.cpython-311.pyc b/app/services/__pycache__/feedback.cpython-311.pyc
index f71f01d6270811fff88cc58e47a154fbf7f5429d..9700660b11cc681ec20cc3b3c4745ce65d0e386b 100644
Binary files a/app/services/__pycache__/feedback.cpython-311.pyc and b/app/services/__pycache__/feedback.cpython-311.pyc differ
diff --git a/app/services/__pycache__/inference.cpython-311.pyc b/app/services/__pycache__/inference.cpython-311.pyc
index d6bae36e7225a03c9310aa9bd057e919a3742077..a83074e48f834076533a88cb4ff9e7c2e082f3f6 100644
Binary files a/app/services/__pycache__/inference.cpython-311.pyc and b/app/services/__pycache__/inference.cpython-311.pyc differ
diff --git a/app/services/explainer.py b/app/services/explainer.py
index 0ae20292fcbbf0a0c4cfea755a99b0e4062152ec..416f3613f2321895687f213a20082e7b90a3473e 100644
--- a/app/services/explainer.py
+++ b/app/services/explainer.py
@@ -1,55 +1,55 @@
-import sys
-import numpy as np
-from src.core.logger import logging
-from src.core.exception import AppException
-from src.app.monitoring.service_metrics import EXPLAINER_REQUEST_SUCCESS, EXPLAINER_REQUEST_FAILED
-
-
-class ExplainerService:
-    def __init__(self, explainer, model_booster, vectorizer):
-        """
-        Initializes an instance of LimeExplainer explaination service.
-
-        Args:
-            explainer (LimeTextExplainer) : An instance of LimeTextExplainer.
-            model_booster (xgb.XGBClassifier) : An instance of the XGBoost model.
-            vectorizer : An instance of the vectorizer to transform input text into numerical features.
-        """
-        self.explainer = explainer
-        self.booster = model_booster
-        self.vectorizer = vectorizer
-
-    def _get_prediction(self, text) -> np.ndarray:
-        """
-        Internal function to get class probability scores for lime explainer.
-        """
-        X = self.vectorizer.transform(text)
-        prob = self.booster.inplace_predict(X)
-    
-        if len(prob.shape) == 1:
-            prob = np.vstack([1 - prob, prob]).T
-        return prob    
-    
-    def explain(self, text: str):
-        """
-        Generate an explanation of the prediction made by the model for a given text.
-
-        Returns:
-            HTML content of the explanation which is rendered in the UI
-        """
-        try:
-            explanation = self.explainer.explain_instance(
-                text,
-                self._get_prediction,
-                num_features=10,
-                num_samples=20
-            )
-            html_content = explanation.as_html()
-            EXPLAINER_REQUEST_SUCCESS.inc()
-
-            return html_content
-        
-        except Exception as e:
-            EXPLAINER_REQUEST_FAILED.inc()
-            logging.error(f"Explainer service failed: {e}", exc_info=True)
+import sys
+import numpy as np
+from src.core.logger import logging
+from src.core.exception import AppException
+from src.app.monitoring.service_metrics import EXPLAINER_REQUEST_SUCCESS, EXPLAINER_REQUEST_FAILED
+
+
+class ExplainerService:
+    def __init__(self, explainer, model_booster, vectorizer):
+        """
+        Initializes an instance of LimeExplainer explaination service.
+
+        Args:
+            explainer (LimeTextExplainer) : An instance of LimeTextExplainer.
+            model_booster (xgb.XGBClassifier) : An instance of the XGBoost model.
+            vectorizer : An instance of the vectorizer to transform input text into numerical features.
+        """
+        self.explainer = explainer
+        self.booster = model_booster
+        self.vectorizer = vectorizer
+
+    def _get_prediction(self, text) -> np.ndarray:
+        """
+        Internal function to get class probability scores for lime explainer.
+        """
+        X = self.vectorizer.transform(text)
+        prob = self.booster.inplace_predict(X)
+    
+        if len(prob.shape) == 1:
+            prob = np.vstack([1 - prob, prob]).T
+        return prob    
+    
+    def explain(self, text: str):
+        """
+        Generate an explanation of the prediction made by the model for a given text.
+
+        Returns:
+            HTML content of the explanation which is rendered in the UI
+        """
+        try:
+            explanation = self.explainer.explain_instance(
+                text,
+                self._get_prediction,
+                num_features=10,
+                num_samples=20
+            )
+            html_content = explanation.as_html()
+            EXPLAINER_REQUEST_SUCCESS.inc()
+
+            return html_content
+        
+        except Exception as e:
+            EXPLAINER_REQUEST_FAILED.inc()
+            logging.error(f"Explainer service failed: {e}", exc_info=True)
             raise AppException(e, sys)
\ No newline at end of file
diff --git a/app/services/feedback.py b/app/services/feedback.py
index 3de1a68c0a3e61379ca155bcec9aa4b1b681c19c..e1b5552fb06920bec18b7455827ffa28396a6bbb 100644
--- a/app/services/feedback.py
+++ b/app/services/feedback.py
@@ -1,38 +1,38 @@
-import sys
-from datetime import datetime, timezone
-from src.core.logger import logging
-from src.core.exception import AppException
-from src.app.monitoring.service_metrics import FEEDBACK_REQUEST_SUCCESS, USER_PREDICTION_FEEDBACK, FEEDBACK_REQUEST_FAILED
-
-
-class FeedbackService:
-    def __init__(self, feedback_event_consumer):
-        
-        self.event_consumer_worker = feedback_event_consumer
-
-    def submit_feedback(self, request_id, pred_label, feedback_label):
-        try:
-            feedback_record = {
-                "request_id": request_id,
-                "time_stamp": datetime.now(timezone.utc).isoformat(),
-                "predicted_label": pred_label,
-                "feedback_label": feedback_label
-            }
-
-            self.event_consumer_worker.add_event(feedback_record)
-            FEEDBACK_REQUEST_SUCCESS.inc()
-
-            if feedback_label == 1:
-                USER_PREDICTION_FEEDBACK.labels(feedback="correct").inc()
-            else:
-                USER_PREDICTION_FEEDBACK.labels(feedback="incorrect").inc()
-
-            return {
-                "status": "success",
-                "message": "Feedback recorded successfully",
-            }
-
-        except Exception as e:
-            logging.exception(f"Failed to submit feedback for request_id: {request_id} : {e}")
-            FEEDBACK_REQUEST_FAILED.inc()
+import sys
+from datetime import datetime, timezone
+from src.core.logger import logging
+from src.core.exception import AppException
+from src.app.monitoring.service_metrics import FEEDBACK_REQUEST_SUCCESS, USER_PREDICTION_FEEDBACK, FEEDBACK_REQUEST_FAILED
+
+
+class FeedbackService:
+    def __init__(self, feedback_event_consumer):
+        
+        self.event_consumer_worker = feedback_event_consumer
+
+    def submit_feedback(self, request_id, pred_label, feedback_label):
+        try:
+            feedback_record = {
+                "request_id": request_id,
+                "time_stamp": datetime.now(timezone.utc).isoformat(),
+                "predicted_label": pred_label,
+                "feedback_label": feedback_label
+            }
+
+            self.event_consumer_worker.add_event(feedback_record)
+            FEEDBACK_REQUEST_SUCCESS.inc()
+
+            if feedback_label == 1:
+                USER_PREDICTION_FEEDBACK.labels(feedback="correct").inc()
+            else:
+                USER_PREDICTION_FEEDBACK.labels(feedback="incorrect").inc()
+
+            return {
+                "status": "success",
+                "message": "Feedback recorded successfully",
+            }
+
+        except Exception as e:
+            logging.exception(f"Failed to submit feedback for request_id: {request_id} : {e}")
+            FEEDBACK_REQUEST_FAILED.inc()
             raise AppException(e, sys)
\ No newline at end of file
diff --git a/app/services/inference.py b/app/services/inference.py
index 45374999bb6c35658497f583f3aa3dcca6d23490..bedc876974ef1700a61a3ebf2997743a8daf1017 100644
--- a/app/services/inference.py
+++ b/app/services/inference.py
@@ -1,113 +1,113 @@
-import time, sys
-from datetime import datetime, timezone
-from src.core.logger import logging
-from src.core.exception import AppException
-from src.app.monitoring.service_metrics import (PREDICTION_REQUEST_SUCCESS, PREDICTION_REQUEST_FAILED, 
-                                     PREDICTION_CLASS, INFERENCE_LATENCY, PREDICTION_CONFIDENCE)
-
-class InferenceService:
-    def __init__(self, model_booster, vectorizer, eval_threshold, 
-                 prediction_event_consumer, model_version):
-        
-        self.booster = model_booster
-        self.vectorizer = vectorizer
-        self.threshold = eval_threshold
-        self.event_consumer_worker = prediction_event_consumer
-        self.model_version = model_version
-
-    def predict(self, request_id: str, input_tweet: str, text: str):
-        """
-        Model inference API endpoint.
-
-        This endpoint accepts a POST request with a JSON payload containing a comment string.
-        It returns a JSON response containing the model prediction, and metadata.
-        """
-        try:
-            timestamp = datetime.now(timezone.utc).isoformat()
-            start_time = time.perf_counter()
-        
-            x = self.vectorizer.transform([text])
-            prob = self.booster.inplace_predict(x)              # P(class=1)
-            pred = (prob > self.threshold).astype(int)
-
-        except Exception as e:
-            logging.exception(e)
-            PREDICTION_REQUEST_FAILED.inc()
-            raise AppException(e, sys)
-
-        if prob is None or len(pred) == 0:
-            logging.error("No prediction made by the model")
-            PREDICTION_REQUEST_FAILED.inc()
-            raise RuntimeError("No prediction made by the model")
-
-        if prob[0] > 0.70:
-            toxicity = "strong"
-        elif prob[0] > self.threshold + 0.05:
-            toxicity = "high"
-        elif prob[0] > self.threshold - 0.03:
-            toxicity = "uncertain"
-        else:
-            toxicity = "none"
-
-        confidence = float(prob[0] if pred[0] == 1 else 1-prob[0])
-        confidence_margin = abs(2*float(prob[0]) - 1)
-
-        warnings = None
-        if confidence_margin < 0.10:
-            message=f"Prediction is close to model decision boundary. Confidence Margin: {round(confidence_margin, 4)}. Manual review is recommended!"
-            warnings = {
-                "code": "LOW_CONFIDENCE_MARGIN",
-                "message": message
-            }
-
-        # Prepare the record to insert into database
-        prediction_record = {
-            "request_id": request_id,
-            "timestamp": timestamp,
-            "comment": input_tweet,
-            "prediction": int(pred[0]),
-            "confidence": round(confidence, 4)
-        }
-
-        # Add the record to the batch writer for asynchronous insertion into MongoDB
-        self.event_consumer_worker.add_event(prediction_record)
-
-        end_time = time.perf_counter()
-        response_time = round((end_time - start_time), 4)
-
-        PREDICTION_REQUEST_SUCCESS.inc()
-        # Track latency
-        INFERENCE_LATENCY.observe(response_time)
-        # Track class distribution
-        PREDICTION_CLASS.labels(class_label=str(pred[0])).inc()
-        # Track class confidence
-        PREDICTION_CONFIDENCE.labels(class_label=str(pred[0])).observe(confidence)
-
-        response = {
-            "id": request_id,
-            "timestamp": timestamp,
-            "object": "text-classification",
-            "prediction": {
-                "label": int(pred[0]),
-                "confidence": round(confidence, 4),
-                "toxicity": toxicity,
-            },
-            "warnings": warnings if warnings else None,
-            "metadata": {
-                "latency": response_time,
-                "usage": {
-                    "word_count": len(text.split()),
-                    "total_characters": len(text)
-                },
-                "model": {
-                    "name": "XGB-Classifier-Booster",
-                    "version": self.model_version,
-                    "vectorizer": str(type(self.vectorizer).__name__),
-                },
-                "streamable": False,
-                "environment": "Production",
-                "api_version": "v-2.0"
-            }
-        }
-        
+import time, sys
+from datetime import datetime, timezone
+from src.core.logger import logging
+from src.core.exception import AppException
+from src.app.monitoring.service_metrics import (PREDICTION_REQUEST_SUCCESS, PREDICTION_REQUEST_FAILED, 
+                                     PREDICTION_CLASS, INFERENCE_LATENCY, PREDICTION_CONFIDENCE)
+
+class InferenceService:
+    def __init__(self, model_booster, vectorizer, eval_threshold, 
+                 prediction_event_consumer, model_version):
+        
+        self.booster = model_booster
+        self.vectorizer = vectorizer
+        self.threshold = eval_threshold
+        self.event_consumer_worker = prediction_event_consumer
+        self.model_version = model_version
+
+    def predict(self, request_id: str, input_tweet: str, text: str):
+        """
+        Model inference API endpoint.
+
+        This endpoint accepts a POST request with a JSON payload containing a comment string.
+        It returns a JSON response containing the model prediction, and metadata.
+        """
+        try:
+            timestamp = datetime.now(timezone.utc).isoformat()
+            start_time = time.perf_counter()
+        
+            x = self.vectorizer.transform([text])
+            prob = self.booster.inplace_predict(x)              # P(class=1)
+            pred = (prob > self.threshold).astype(int)
+
+        except Exception as e:
+            logging.exception(e)
+            PREDICTION_REQUEST_FAILED.inc()
+            raise AppException(e, sys)
+
+        if prob is None or len(pred) == 0:
+            logging.error("No prediction made by the model")
+            PREDICTION_REQUEST_FAILED.inc()
+            raise RuntimeError("No prediction made by the model")
+
+        if prob[0] > 0.70:
+            toxicity = "strong"
+        elif prob[0] > self.threshold + 0.05:
+            toxicity = "high"
+        elif prob[0] > self.threshold - 0.03:
+            toxicity = "uncertain"
+        else:
+            toxicity = "none"
+
+        confidence = float(prob[0] if pred[0] == 1 else 1-prob[0])
+        confidence_margin = abs(2*float(prob[0]) - 1)
+
+        warnings = None
+        if confidence_margin < 0.10:
+            message=f"Prediction is close to model decision boundary. Confidence Margin: {round(confidence_margin, 4)}. Manual review is recommended!"
+            warnings = {
+                "code": "LOW_CONFIDENCE_MARGIN",
+                "message": message
+            }
+
+        # Prepare the record to insert into database
+        prediction_record = {
+            "request_id": request_id,
+            "timestamp": timestamp,
+            "comment": input_tweet,
+            "prediction": int(pred[0]),
+            "confidence": round(confidence, 4)
+        }
+
+        # Add the record to the batch writer for asynchronous insertion into MongoDB
+        self.event_consumer_worker.add_event(prediction_record)
+
+        end_time = time.perf_counter()
+        response_time = round((end_time - start_time), 4)
+
+        PREDICTION_REQUEST_SUCCESS.inc()
+        # Track latency
+        INFERENCE_LATENCY.observe(response_time)
+        # Track class distribution
+        PREDICTION_CLASS.labels(class_label=str(pred[0])).inc()
+        # Track class confidence
+        PREDICTION_CONFIDENCE.labels(class_label=str(pred[0])).observe(confidence)
+
+        response = {
+            "id": request_id,
+            "timestamp": timestamp,
+            "object": "text-classification",
+            "prediction": {
+                "label": int(pred[0]),
+                "confidence": round(confidence, 4),
+                "toxicity": toxicity,
+            },
+            "warnings": warnings if warnings else None,
+            "metadata": {
+                "latency": response_time,
+                "usage": {
+                    "word_count": len(text.split()),
+                    "total_characters": len(text)
+                },
+                "model": {
+                    "name": "XGB-Classifier-Booster",
+                    "version": self.model_version,
+                    "vectorizer": str(type(self.vectorizer).__name__),
+                },
+                "streamable": False,
+                "environment": "Production",
+                "api_version": "v-2.0"
+            }
+        }
+        
         return response
\ No newline at end of file
diff --git a/app/workers/__init__.py b/app/workers/__init__.py
index 6991a4450c445de58820effc5644edd1d715dd3f..54b51048262d1eb715b7334fae09b9682ce99319 100644
--- a/app/workers/__init__.py
+++ b/app/workers/__init__.py
@@ -1,144 +1,144 @@
-# This module contains the implementation of the BufferedEventConsumerWorker class, which is responsible for consuming events from a buffer queue and writing them to a MongoDB collection in batches. 
-
-import time
-import threading
-from typing import Any
-from queue import Queue, Empty, Full
-from src.core.logger import logging 
-
-class BufferedEventConsumerWorker:
-    def __init__(self, mongo_client, database_name, collection_name, queue_maxsize: int = 1200, 
-                 max_batch_size: int = 1000, flush_interval: int = 30, mongo_timeout: int = 5):
-        """
-        Initializes the buffered event consumer worker.
-
-        Args:
-            mongo_client (MongoClient): The MongoDB client.
-            database_name (str): The name of the MongoDB database.
-            collection_name (str): The name of the MongoDB collection.
-            queue_maxsize (int, optional): The maximum size of the queue. Defaults to 1200.
-            max_batch_size (int, optional): The maximum batch size for write operations. Defaults to 1000.
-            flush_interval (int, optional): The interval (in seconds) at which to flush the queue. Defaults to 30.
-            mongo_timeout (int, optional): The timeout (in seconds) for write operations. Defaults to 5.
-        """
-        self.queue: Queue[dict[str, Any] | None] = Queue(queue_maxsize)
-        self.max_batch_size = max_batch_size                
-        self.flush_interval = flush_interval
-        self.mongo_timeout = mongo_timeout
-
-        self.shutdown_event: threading.Event = threading.Event()
-
-        self.client = mongo_client 
-        self.database_name = database_name
-        self.collection_name = collection_name
-
-        self.worker: threading.Thread = threading.Thread(
-            target=self._writer_worker,
-            daemon=True
-        )
-        self.worker.start()
-
-    def add_event(self, record: dict) -> None:  
-        """
-        Adds a record to the buffer queue.
-
-        Args:
-            record (dict): The record to add to the buffer queue.
-        """
-        try:
-            self.queue.put(record, timeout=0.3)
-        except Full:
-            logging.warning(f"Failed to add record in buffer queue: Queue is full")
-
-    def shutdown(self) -> None:
-        """
-        Gracefully shut down the worker thread.
-        Ensures final flush before exit.
-        """
-        self.shutdown_event.set()
-
-        # Wake up worker if it's blocked on queue.get()
-        self.queue.put(None)
-        # Wait the main thread until worker fully exits.
-        self.worker.join()
-
-
-    # INTERNAL WORKER
-    def _writer_worker(self) -> None:
-        """
-        The worker thread responsible for consuming records from the buffer queue and writing them to MongoDB.
-
-        It runs indefinitely until the shutdown event is set, at which point it will drain the queue quickly and exit.
-        The worker thread tries to flush the queue at regular intervals, or when the batch size reaches the maximum threshold.
-        If the queue is empty, it will wait indefinitely for new records to arrive. If a timeout is reached, it will flush the queue and reset the timer.
-        """
-        batch = []
-        first_record_time = None
-
-        while not self.shutdown_event.is_set():
-            try:
-                if first_record_time is None:
-                    # No records yet → wait indefinitely
-                    record = self.queue.get()
-                else:
-                    elapsed = time.time() - first_record_time
-                    remaining = max(self.flush_interval - elapsed, 0)
-                    record = self.queue.get(timeout=remaining)
-                
-                if record is None:
-                    break
-                
-                batch.append(record)
-
-                if first_record_time is None:
-                    first_record_time = time.time()
-
-                # Drain quickly if batch growing
-                while len(batch) < self.max_batch_size:
-                    try:
-                        record = self.queue.get_nowait()
-                        if record is None:
-                            break
-                        batch.append(record)
-                    except Empty:
-                        break
-
-            except Empty:
-                # Timeout reached
-                pass
-
-            # Flush conditions
-            if batch and (
-                len(batch) >= self.max_batch_size or
-                (first_record_time and
-                 time.time() - first_record_time >= self.flush_interval)
-            ):
-                self._flush(batch)
-                batch.clear()
-                first_record_time = None
-
-        # Final flush on shutdown
-        if batch:
-            self._flush(batch)
-        logging.info("BufferedEventConsumer worker stopped cleanly.")
-
-    # Database flush
-    def _flush(self, batch_records: list):
-        """
-        Writes batch records to MongoDB with basic failure handling.
-
-        Args:
-            batch_records (list): The list of records to flush to the database.
-
-        Raises:
-            Exception: If an error occurs while flushing the batch records to the database.
-        """
-        try:
-            self.client.insert_docs(self.collection_name, 
-                                    self.database_name, 
-                                    batch_records
-                                )
-            logging.info(f"Flushed {len(batch_records)} records to MongoDB")
-        
-        except Exception as e:
+# This module contains the implementation of the BufferedEventConsumerWorker class, which is responsible for consuming events from a buffer queue and writing them to a MongoDB collection in batches. 
+
+import time
+import threading
+from typing import Any
+from queue import Queue, Empty, Full
+from src.core.logger import logging 
+
+class BufferedEventConsumerWorker:
+    def __init__(self, mongo_client, database_name, collection_name, queue_maxsize: int = 1200, 
+                 max_batch_size: int = 1000, flush_interval: int = 30, mongo_timeout: int = 5):
+        """
+        Initializes the buffered event consumer worker.
+
+        Args:
+            mongo_client (MongoClient): The MongoDB client.
+            database_name (str): The name of the MongoDB database.
+            collection_name (str): The name of the MongoDB collection.
+            queue_maxsize (int, optional): The maximum size of the queue. Defaults to 1200.
+            max_batch_size (int, optional): The maximum batch size for write operations. Defaults to 1000.
+            flush_interval (int, optional): The interval (in seconds) at which to flush the queue. Defaults to 30.
+            mongo_timeout (int, optional): The timeout (in seconds) for write operations. Defaults to 5.
+        """
+        self.queue: Queue[dict[str, Any] | None] = Queue(queue_maxsize)
+        self.max_batch_size = max_batch_size                
+        self.flush_interval = flush_interval
+        self.mongo_timeout = mongo_timeout
+
+        self.shutdown_event: threading.Event = threading.Event()
+
+        self.client = mongo_client 
+        self.database_name = database_name
+        self.collection_name = collection_name
+
+        self.worker: threading.Thread = threading.Thread(
+            target=self._writer_worker,
+            daemon=True
+        )
+        self.worker.start()
+
+    def add_event(self, record: dict) -> None:  
+        """
+        Adds a record to the buffer queue.
+
+        Args:
+            record (dict): The record to add to the buffer queue.
+        """
+        try:
+            self.queue.put(record, timeout=0.3)
+        except Full:
+            logging.warning(f"Failed to add record in buffer queue: Queue is full")
+
+    def shutdown(self) -> None:
+        """
+        Gracefully shut down the worker thread.
+        Ensures final flush before exit.
+        """
+        self.shutdown_event.set()
+
+        # Wake up worker if it's blocked on queue.get()
+        self.queue.put(None)
+        # Wait the main thread until worker fully exits.
+        self.worker.join()
+
+
+    # INTERNAL WORKER
+    def _writer_worker(self) -> None:
+        """
+        The worker thread responsible for consuming records from the buffer queue and writing them to MongoDB.
+
+        It runs indefinitely until the shutdown event is set, at which point it will drain the queue quickly and exit.
+        The worker thread tries to flush the queue at regular intervals, or when the batch size reaches the maximum threshold.
+        If the queue is empty, it will wait indefinitely for new records to arrive. If a timeout is reached, it will flush the queue and reset the timer.
+        """
+        batch = []
+        first_record_time = None
+
+        while not self.shutdown_event.is_set():
+            try:
+                if first_record_time is None:
+                    # No records yet → wait indefinitely
+                    record = self.queue.get()
+                else:
+                    elapsed = time.time() - first_record_time
+                    remaining = max(self.flush_interval - elapsed, 0)
+                    record = self.queue.get(timeout=remaining)
+                
+                if record is None:
+                    break
+                
+                batch.append(record)
+
+                if first_record_time is None:
+                    first_record_time = time.time()
+
+                # Drain quickly if batch growing
+                while len(batch) < self.max_batch_size:
+                    try:
+                        record = self.queue.get_nowait()
+                        if record is None:
+                            break
+                        batch.append(record)
+                    except Empty:
+                        break
+
+            except Empty:
+                # Timeout reached
+                pass
+
+            # Flush conditions
+            if batch and (
+                len(batch) >= self.max_batch_size or
+                (first_record_time and
+                 time.time() - first_record_time >= self.flush_interval)
+            ):
+                self._flush(batch)
+                batch.clear()
+                first_record_time = None
+
+        # Final flush on shutdown
+        if batch:
+            self._flush(batch)
+        logging.info("BufferedEventConsumer worker stopped cleanly.")
+
+    # Database flush
+    def _flush(self, batch_records: list):
+        """
+        Writes batch records to MongoDB with basic failure handling.
+
+        Args:
+            batch_records (list): The list of records to flush to the database.
+
+        Raises:
+            Exception: If an error occurs while flushing the batch records to the database.
+        """
+        try:
+            self.client.insert_docs(self.collection_name, 
+                                    self.database_name, 
+                                    batch_records
+                                )
+            logging.info(f"Flushed {len(batch_records)} records to MongoDB")
+        
+        except Exception as e:
             logging.error(f"BufferedBatchWriter failed to flush: {e}", exc_info=True)
\ No newline at end of file
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diff --git a/components/data_ingestion.py b/components/data_ingestion.py
index e635be2756b93cf73090ccb9e4bcd26d98e8c7aa..72596deaafffe6c53ef5666a9e148fd059862c27 100644
--- a/components/data_ingestion.py
+++ b/components/data_ingestion.py
@@ -1,85 +1,85 @@
-import sys
-from sklearn.model_selection import train_test_split
-from src.core.constants import DATABASE_NAME, TRAINING_COLLECTION_NAME
-from src.core.mongo_client import MongoDBClient
-from src.core.logger import logging
-from src.core.exception import AppException
-from src.core.configuration import AppConfiguration
-import gc
-
-class DataIngestion:
-    def __init__(self, app_config = AppConfiguration()):
-        """
-        DataIngestion Intialization
-        data_ingestion_config: DataIngestionConfig 
-        """
-        try:
-            self.data_ingestion_config = app_config.data_ingestion_config()
-
-        except Exception as e:
-            logging.error(f"Data Ingestion error: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-    
-    def download_data(self):
-        """
-        Downloads data from MongoDB and saves it in csv format, and also
-        splits the data into training and testing sets and saves them in parquet format.
-
-        Raises:
-            AppException: If there is an error downloading data from MongoDB
-        """
-        try:
-            save_ingested_data_path = self.data_ingestion_config.ingested_data_path
-            save_train_data_path = self.data_ingestion_config.train_data_path
-            save_test_data_path = self.data_ingestion_config.test_data_path
-
-            logging.info("Connecting to MongoDB")
-            client = MongoDBClient()
-
-            data = client.get_all_docs(database_name=DATABASE_NAME, collection_name=TRAINING_COLLECTION_NAME)
-
-            data.to_csv(save_ingested_data_path, index=False)
-            logging.info(f"Raw ingested data saved successfully saved at {save_ingested_data_path}")
-
-            # close mongo connection 
-            client.close_connection()
-
-            logging.info("Splitting data into training and testing sets")
-            train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
-
-            train_data.to_parquet(save_train_data_path, index=False)
-            test_data.to_parquet(save_test_data_path, index=False)
-            logging.info(f"Successfully saved train data and test data")
-            # free memory
-            del data, train_data, test_data
-            gc.collect()
-
-        except Exception as e:
-            logging.error(f"Failed to download data from MongoDB: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-
-def initiate_data_ingestion():
-    """
-    Main function to initiate the Data Ingestion workflow. It downloads data from MongoDB and
-    saves it to the specified directory using the configuration provided in the
-    data_ingestion_config.
-
-    Raises:
-        AppException: If an error occurs during data ingestion.
-    """
-    obj = DataIngestion()
-    try:
-        logging.info(f"{'='*20}Data Ingestion{'='*20}")
-        obj.download_data()
-
-        logging.info(f"{'='*20}Data Ingestion Completed Successfully{'='*20} \n\n")
-
-    except Exception as e:
-        logging.error(f"Error in Data Ingestion process: {e}", exc_info=True)
-        raise AppException(e, sys)
-
-
-if __name__ == "__main__":
+import sys
+from sklearn.model_selection import train_test_split
+from src.core.constants import DATABASE_NAME, TRAINING_COLLECTION_NAME
+from src.core.mongo_client import MongoDBClient
+from src.core.logger import logging
+from src.core.exception import AppException
+from src.core.configuration import AppConfiguration
+import gc
+
+class DataIngestion:
+    def __init__(self, app_config = AppConfiguration()):
+        """
+        DataIngestion Intialization
+        data_ingestion_config: DataIngestionConfig 
+        """
+        try:
+            self.data_ingestion_config = app_config.data_ingestion_config()
+
+        except Exception as e:
+            logging.error(f"Data Ingestion error: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+    
+    def download_data(self):
+        """
+        Downloads data from MongoDB and saves it in csv format, and also
+        splits the data into training and testing sets and saves them in parquet format.
+
+        Raises:
+            AppException: If there is an error downloading data from MongoDB
+        """
+        try:
+            save_ingested_data_path = self.data_ingestion_config.ingested_data_path
+            save_train_data_path = self.data_ingestion_config.train_data_path
+            save_test_data_path = self.data_ingestion_config.test_data_path
+
+            logging.info("Connecting to MongoDB")
+            client = MongoDBClient()
+
+            data = client.get_all_docs(database_name=DATABASE_NAME, collection_name=TRAINING_COLLECTION_NAME)
+
+            data.to_csv(save_ingested_data_path, index=False)
+            logging.info(f"Raw ingested data saved successfully saved at {save_ingested_data_path}")
+
+            # close mongo connection 
+            client.close_connection()
+
+            logging.info("Splitting data into training and testing sets")
+            train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
+
+            train_data.to_parquet(save_train_data_path, index=False)
+            test_data.to_parquet(save_test_data_path, index=False)
+            logging.info(f"Successfully saved train data and test data")
+            # free memory
+            del data, train_data, test_data
+            gc.collect()
+
+        except Exception as e:
+            logging.error(f"Failed to download data from MongoDB: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+
+def initiate_data_ingestion():
+    """
+    Main function to initiate the Data Ingestion workflow. It downloads data from MongoDB and
+    saves it to the specified directory using the configuration provided in the
+    data_ingestion_config.
+
+    Raises:
+        AppException: If an error occurs during data ingestion.
+    """
+    obj = DataIngestion()
+    try:
+        logging.info(f"{'='*20}Data Ingestion{'='*20}")
+        obj.download_data()
+
+        logging.info(f"{'='*20}Data Ingestion Completed Successfully{'='*20} \n\n")
+
+    except Exception as e:
+        logging.error(f"Error in Data Ingestion process: {e}", exc_info=True)
+        raise AppException(e, sys)
+
+
+if __name__ == "__main__":
     initiate_data_ingestion()
\ No newline at end of file
diff --git a/components/data_preprocessing.py b/components/data_preprocessing.py
index 692c2edfa051823d6f3b413099fa46206e213a59..1bef46cced95b9a1a208d40aa48e89de52277864 100644
--- a/components/data_preprocessing.py
+++ b/components/data_preprocessing.py
@@ -1,142 +1,142 @@
-import nltk
-nltk.download('stopwords')
-nltk.download('wordnet')
-nltk.download('averaged_perceptron_tagger_eng')
-
-import sys
-import string
-import pandas as pd
-from pathlib import Path
-from tqdm import tqdm
-from nltk import pos_tag
-from nltk.stem import WordNetLemmatizer
-from nltk.corpus import stopwords, wordnet
-from src.core.logger import logging
-from src.core.exception import AppException
-from src.core.configuration import AppConfiguration
-import gc
-
-
-class HelperFunctions:
-    def __init__(self):
-        """
-        Initializes an instance of the HelperFunctions class.
-
-        This class contains methods for various text preprocessing tasks such as
-        lowercasing, removing punctuations, removing stopwords, and lemmatization.
-        """
-        pass
-    
-    def lower_case(self, text) -> str:
-        """Converts the given text to lowercase."""
-        return text.lower()
-    
-    def remove_punctuations(self, text) -> str:
-        """Removes all punctuation marks from the given text."""
-        exclude = string.punctuation
-        return text.translate(str.maketrans("", "", exclude))
-    
-    def remove_stopwords(self, text) -> str:
-        """Removes all English stopwords from the given text."""
-        stop_words = set(stopwords.words('english'))
-        text = [word for word in text.split() if word not in stop_words]
-        return " ".join(text)
-
-    def lemmatization(self, text) -> str:
-        """
-        Does parts of speech tagging and then converts words to their base form 
-        using WordNetLemmatizer.
-        """
-        lemmatizer = WordNetLemmatizer()
-        wordnet_map = {"N": wordnet.NOUN, "V": wordnet.VERB, "J": wordnet.ADJ, "R": wordnet.ADV}
-        # Perform POS tagging and lemmatization
-        pos_text = pos_tag(text.split())
-        text = [lemmatizer.lemmatize(word, wordnet_map.get(pos[0], wordnet.NOUN)) for word, pos in pos_text]
-        return " ".join(text)
-
-
-class DataPreprocessing:
-    def __init__(self, config = AppConfiguration()):
-        """
-        Initializes the DataPreprocessing object by creating the data prepocessing configuration.
-        Args:
-            config (AppConfiguration): The configuration object containing the application configuration.
-        """
-        try:
-            self.data_preprocessing_config = config.data_preprocessing_config()
-
-        except Exception as e:
-            logging.error(f"Failed to create data preprocessing configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-
-
-    def preprocess(self, df: pd.DataFrame, filename: str) -> pd.DataFrame:
-        """
-        Preprocesses the given dataframe by performing lowercasing, removing punctuation, 
-        removing stopwords and performing lemmatization with parts of speech tagging.
-        Args:
-            df (pd.DataFrame): The dataframe to be preprocessed
-            filename (str): The filename to save the preprocessed dataframe
-
-        Returns:
-            pd.DataFrame: The preprocessed dataframe
-        """
-        fn = HelperFunctions()
-        # Preprocessing steps
-        try:
-            df.dropna(how='any', inplace=True)
-            tqdm.pandas()
-
-            logging.info("Performing lowercasing")
-            df['Content'] = df['Content'].progress_apply(fn.lower_case)
-
-            logging.info("Removing punctuations")
-            df['Content'] = df['Content'].progress_apply(fn.remove_punctuations)
-
-            logging.info("Performing pos tagging and lemmatization")
-            df['Content'] = df['Content'].progress_apply(fn.lemmatization)
-
-            logging.info("Removing stopwords")
-            df['Content'] = df['Content'].progress_apply(fn.remove_stopwords)
-
-            logging.info("Finished preprocessing operations successfully")
-
-            preprocessed_data_dir = self.data_preprocessing_config.preprocessed_data_dir
-            df.to_feather(Path(preprocessed_data_dir, filename))
-            
-            logging.info(f"Data successfully saved at {preprocessed_data_dir}")
-            return df
-        
-        except Exception as e:
-            logging.error(f"Data preprocessing failed: {e}", exc_info=True)
-            raise AppException(e, sys)
-    
-def initiate_data_preprocessing():
-    """
-    Main function to initiate the data preprocessing workflow. It reads ingested dataset,
-    performs different preprocessing operations and saves the preprocessed data as a CSV file.
-
-    Raises:
-        AppException: If an error occurs during data preprocessing.
-    """
-    obj = DataPreprocessing()
-    try:
-        logging.info(f"{'='*20}Data Preprocessing{'='*20}")
-        data_path = obj.data_preprocessing_config.data_path
-        if not data_path:
-            raise ValueError("No data path found")
-        
-        df = pd.read_parquet(data_path)
-        preprocessed_dataset_name = obj.data_preprocessing_config.preprocessed_data_filename
-        obj.preprocess(df, preprocessed_dataset_name)
-        del df
-        gc.collect()
-        logging.info(f"{'='*20}Data Preprocessing Completed Successfully{'='*20} \n\n")
-
-    except Exception as e:
-        logging.error(f"Error in Data Preprocessing process: {e}", exc_info=True)
-        raise AppException(e, sys)
-    
-
-if __name__ == "__main__":
+import nltk
+nltk.download('stopwords')
+nltk.download('wordnet')
+nltk.download('averaged_perceptron_tagger_eng')
+
+import sys
+import string
+import pandas as pd
+from pathlib import Path
+from tqdm import tqdm
+from nltk import pos_tag
+from nltk.stem import WordNetLemmatizer
+from nltk.corpus import stopwords, wordnet
+from src.core.logger import logging
+from src.core.exception import AppException
+from src.core.configuration import AppConfiguration
+import gc
+
+
+class HelperFunctions:
+    def __init__(self):
+        """
+        Initializes an instance of the HelperFunctions class.
+
+        This class contains methods for various text preprocessing tasks such as
+        lowercasing, removing punctuations, removing stopwords, and lemmatization.
+        """
+        pass
+    
+    def lower_case(self, text) -> str:
+        """Converts the given text to lowercase."""
+        return text.lower()
+    
+    def remove_punctuations(self, text) -> str:
+        """Removes all punctuation marks from the given text."""
+        exclude = string.punctuation
+        return text.translate(str.maketrans("", "", exclude))
+    
+    def remove_stopwords(self, text) -> str:
+        """Removes all English stopwords from the given text."""
+        stop_words = set(stopwords.words('english'))
+        text = [word for word in text.split() if word not in stop_words]
+        return " ".join(text)
+
+    def lemmatization(self, text) -> str:
+        """
+        Does parts of speech tagging and then converts words to their base form 
+        using WordNetLemmatizer.
+        """
+        lemmatizer = WordNetLemmatizer()
+        wordnet_map = {"N": wordnet.NOUN, "V": wordnet.VERB, "J": wordnet.ADJ, "R": wordnet.ADV}
+        # Perform POS tagging and lemmatization
+        pos_text = pos_tag(text.split())
+        text = [lemmatizer.lemmatize(word, wordnet_map.get(pos[0], wordnet.NOUN)) for word, pos in pos_text]
+        return " ".join(text)
+
+
+class DataPreprocessing:
+    def __init__(self, config = AppConfiguration()):
+        """
+        Initializes the DataPreprocessing object by creating the data prepocessing configuration.
+        Args:
+            config (AppConfiguration): The configuration object containing the application configuration.
+        """
+        try:
+            self.data_preprocessing_config = config.data_preprocessing_config()
+
+        except Exception as e:
+            logging.error(f"Failed to create data preprocessing configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+
+    def preprocess(self, df: pd.DataFrame, filename: str) -> pd.DataFrame:
+        """
+        Preprocesses the given dataframe by performing lowercasing, removing punctuation, 
+        removing stopwords and performing lemmatization with parts of speech tagging.
+        Args:
+            df (pd.DataFrame): The dataframe to be preprocessed
+            filename (str): The filename to save the preprocessed dataframe
+
+        Returns:
+            pd.DataFrame: The preprocessed dataframe
+        """
+        fn = HelperFunctions()
+        # Preprocessing steps
+        try:
+            df.dropna(how='any', inplace=True)
+            tqdm.pandas()
+
+            logging.info("Performing lowercasing")
+            df['Content'] = df['Content'].progress_apply(fn.lower_case)
+
+            logging.info("Removing punctuations")
+            df['Content'] = df['Content'].progress_apply(fn.remove_punctuations)
+
+            logging.info("Performing pos tagging and lemmatization")
+            df['Content'] = df['Content'].progress_apply(fn.lemmatization)
+
+            logging.info("Removing stopwords")
+            df['Content'] = df['Content'].progress_apply(fn.remove_stopwords)
+
+            logging.info("Finished preprocessing operations successfully")
+
+            preprocessed_data_dir = self.data_preprocessing_config.preprocessed_data_dir
+            df.to_feather(Path(preprocessed_data_dir, filename))
+            
+            logging.info(f"Data successfully saved at {preprocessed_data_dir}")
+            return df
+        
+        except Exception as e:
+            logging.error(f"Data preprocessing failed: {e}", exc_info=True)
+            raise AppException(e, sys)
+    
+def initiate_data_preprocessing():
+    """
+    Main function to initiate the data preprocessing workflow. It reads ingested dataset,
+    performs different preprocessing operations and saves the preprocessed data as a CSV file.
+
+    Raises:
+        AppException: If an error occurs during data preprocessing.
+    """
+    obj = DataPreprocessing()
+    try:
+        logging.info(f"{'='*20}Data Preprocessing{'='*20}")
+        data_path = obj.data_preprocessing_config.data_path
+        if not data_path:
+            raise ValueError("No data path found")
+        
+        df = pd.read_parquet(data_path)
+        preprocessed_dataset_name = obj.data_preprocessing_config.preprocessed_data_filename
+        obj.preprocess(df, preprocessed_dataset_name)
+        del df
+        gc.collect()
+        logging.info(f"{'='*20}Data Preprocessing Completed Successfully{'='*20} \n\n")
+
+    except Exception as e:
+        logging.error(f"Error in Data Preprocessing process: {e}", exc_info=True)
+        raise AppException(e, sys)
+    
+
+if __name__ == "__main__":
     initiate_data_preprocessing()
\ No newline at end of file
diff --git a/components/data_validation.py b/components/data_validation.py
index 374a688dbf2584c38e7e2ba05b2820a92b61c297..4beefddb22c64454a92f98ce76122db3ef970cb0 100644
--- a/components/data_validation.py
+++ b/components/data_validation.py
@@ -1,201 +1,201 @@
-import sys
-import pandas as pd
-import json
-from pathlib import Path
-from evidently import DataDefinition, Dataset, Report
-from evidently.presets import DataDriftPreset
-from src.core.logger import logging
-from src.core.exception import AppException
-from src.core.configuration import AppConfiguration
-import gc
-
-class DataValidation:
-    def __init__(self, config = AppConfiguration()):
-        """
-         Initializes the DataPreprocessing object by creating the data prepocessing configuration.
-         Args:
-             config (AppConfiguration): The configuration object containing the application configuration.
-         """
-        self.validation_config = config.data_validation_config()
-
-
-    def get_data_drift_report(self, train_data: pd.DataFrame, test_data: pd.DataFrame):
-        try:
-            logging.info("Preparing data drift report")
-            train_df, test_df = self._get_features(train_data, test_data)
-
-            schema = DataDefinition(
-                    text_columns=["Content"],                          
-                    numerical_columns=["num_words", "num_characters", "avg_word_len"],
-                )
-            
-            train_ds = Dataset.from_pandas(train_df, data_definition=schema)
-            test_ds  = Dataset.from_pandas(test_df,  data_definition=schema)
-
-            report = Report(metrics=[DataDriftPreset()])
-            result = report.run(reference_data=train_ds, current_data=test_ds)
-            
-            result.save_json("reports/data_drift.json")
-            logging.info("Data drift report prepared and saved in json format")
-        
-        except Exception as e:
-            logging.error(f"Faild to get the data drift report: {e}", exc_info=True)
-            raise AppException(e, sys)
-
-
-    def check_data_drift(self):  
-        """
-        Checks if there is a significant drift in the data distribution.
-
-        Reads the data drift report generated by the `get_data_drift_report` method and 
-        checks if there is a significant drift in the data distribution. If a drift is detected, 
-        it raises a `ValueError` with a message indicating that a data drift has been detected.
-        """
-        try:
-            data_drift_report_path = Path("reports/data_drift.json")
-            if not data_drift_report_path.exists():
-                raise FileNotFoundError("'data_drift.json' file not found")
-
-            with open(data_drift_report_path, "r") as file:
-                report = json.load(file)
-
-            logging.info("Evaluating data drift report")
-
-            metrics = report.get("metrics", [])
-            drift_count_metric = next(
-                (m for m in metrics if m["metric_id"].startswith("DriftedColumnsCount")), None
-            )
-
-            if drift_count_metric is None:
-                raise ValueError("DriftedColumnsCount metric not found in report")
-
-            drift_count = drift_count_metric["value"]["count"]
-            drift_share = drift_count_metric["value"]["share"]
-
-            # Evidently default drift detection threshold: drift_share >= 0.5
-            DRIFT_SHARE_THRESHOLD = 0.5
-
-            if drift_share >= DRIFT_SHARE_THRESHOLD:
-                logging.error(f"Data drift detected: {drift_count} columns drifted ({drift_share:.0%} of total columns).")
-                with open(self.validation_config.validation_status_file, "w") as f:
-                    f.write("Validation Status : Failed")
-                raise ValueError("Data drift detected")
-            else:
-                logging.info("No significant data drift detected")
-                with open(self.validation_config.validation_status_file, "w") as f:
-                    f.write("Validation Status : Successful")
-
-        except Exception as e:
-            logging.error(f"Error occured in checking for data drift: {e}", exc_info=True)
-            raise AppException(e, sys)
-    
-    
-    def _get_features(self, train_data: pd.DataFrame, test_data: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
-        """
-        Gets the features required for preparing the data drift report.
-        Args:
-            train_data (pd.DataFrame): The training data.
-            test_data (pd.DataFrame): The test data.
-
-        Returns:
-            tuple: A tuple containing the modified train and test dataframes with added features.
-        """
-        try:
-            train_data["num_words"] = train_data["Content"].apply(lambda x: len(x.split()))
-            train_data["num_characters"] = train_data["Content"].apply(lambda x: len(x.replace(" ", "")))
-            train_data['avg_word_len'] = round(train_data["num_characters"]/train_data["num_words"], 4)
-
-            test_data["num_words"] = test_data["Content"].apply(lambda x: len(x.split()))
-            test_data["num_characters"] = test_data["Content"].apply(lambda x: len(x.replace(" ", "")))
-            test_data['avg_word_len'] = round(test_data["num_characters"]/test_data["num_words"], 4)
-
-            return train_data, test_data
-
-        except Exception as e:
-            logging.error(f"Failed to get features required for preparing data drift report: {e}", exc_info=True)
-            raise AppException(e, sys)
-
-
-    def validate_dataset(self, df: pd.DataFrame):
-        """
-        Validates the given dataframe for required columns and labels. Also checks for class imbalance.
-        Args:
-            df (pd.DataFrame): The dataframe to be validated.
-        
-        Raises:
-            ValueError: If the dataframe does not have required columns or labels.
-            ValueError: If the class imbalance ratio is greater than 1.6.
-            AppException: If any other error occurs during the validation process.
-        """
-        try:
-            required_columns = ["Content", "Label"]
-            required_labels = [1, 0]
-            
-            columns = [col for col in required_columns if col not in df.columns]
-            labels = [label for label in required_labels if label not in df["Label"].unique()]
-            
-            if not columns or not labels:
-                logging.info("All required columns and labels are present")
-            else:
-                with open(self.validation_config.validation_status_file, "w") as f:
-                    f.write("Validation Status : Failed")
-                raise ValueError(f"Required columns/labels not satisfied. Verify data.")
-            
-            # Check for class imbalance
-            labels_count = df["Label"].value_counts()
-            majority_class_count = labels_count.max()
-            minority_class_count = labels_count.min()
-            class_imbalance_ratio = majority_class_count/minority_class_count
-
-            if class_imbalance_ratio > 1.6 :
-                raise ValueError(f"High class label imbalance found in data")
-            elif class_imbalance_ratio > 1.3 :
-                logging.warning("Class label imbalance exists. Model performance might be affected.")
-            else:
-                logging.info("No major class imbalance detected")
-
-        except Exception as e:
-            logging.error(f"Feature engineering operation terminated: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-
-def initiate_data_validation():
-    """
-    Initiates the data validation workflow.
-
-    This function initializes a DataValidation object, reads the ingested, 
-    training, and testing datasets, and performs validation checks on the 
-    ingested dataset. It generates a data drift report and checks for any 
-    significant data drift between the training and testing datasets.
-
-    Raises:
-        AppException: If any error occurs during the data validation process.
-    """
-
-    obj = DataValidation()
-
-    try:
-        logging.info(f"{'='*20}Data Validation{'='*20}")
-        ingested_data_path = obj.validation_config.ingested_data_path
-        train_data_path = obj.validation_config.train_data_path
-        test_data_path = obj.validation_config.test_data_path
-
-        ingested_df = pd.read_csv(ingested_data_path)
-        train_df = pd.read_parquet(train_data_path)
-        test_df = pd.read_parquet(test_data_path)
-
-        obj.validate_dataset(df=ingested_df)
-        obj.get_data_drift_report(train_df, test_df)
-        obj.check_data_drift()
-        # free memory
-        del ingested_df, train_df, test_df
-        gc.collect()
-        logging.info(f"{'='*20}Data Validation Completed Successfully{'='*20} \n\n")
-
-    except Exception as e:
-        logging.error(f"Data Validation failed: {e}", exc_info=True)
-        raise AppException(e, sys)
-    
-
-if __name__ == "__main__":
+import sys
+import pandas as pd
+import json
+from pathlib import Path
+from evidently import DataDefinition, Dataset, Report
+from evidently.presets import DataDriftPreset
+from src.core.logger import logging
+from src.core.exception import AppException
+from src.core.configuration import AppConfiguration
+import gc
+
+class DataValidation:
+    def __init__(self, config = AppConfiguration()):
+        """
+         Initializes the DataPreprocessing object by creating the data prepocessing configuration.
+         Args:
+             config (AppConfiguration): The configuration object containing the application configuration.
+         """
+        self.validation_config = config.data_validation_config()
+
+
+    def get_data_drift_report(self, train_data: pd.DataFrame, test_data: pd.DataFrame):
+        try:
+            logging.info("Preparing data drift report")
+            train_df, test_df = self._get_features(train_data, test_data)
+
+            schema = DataDefinition(
+                    text_columns=["Content"],                          
+                    numerical_columns=["num_words", "num_characters", "avg_word_len"],
+                )
+            
+            train_ds = Dataset.from_pandas(train_df, data_definition=schema)
+            test_ds  = Dataset.from_pandas(test_df,  data_definition=schema)
+
+            report = Report(metrics=[DataDriftPreset()])
+            result = report.run(reference_data=train_ds, current_data=test_ds)
+            
+            result.save_json("reports/data_drift.json")
+            logging.info("Data drift report prepared and saved in json format")
+        
+        except Exception as e:
+            logging.error(f"Faild to get the data drift report: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+
+    def check_data_drift(self):  
+        """
+        Checks if there is a significant drift in the data distribution.
+
+        Reads the data drift report generated by the `get_data_drift_report` method and 
+        checks if there is a significant drift in the data distribution. If a drift is detected, 
+        it raises a `ValueError` with a message indicating that a data drift has been detected.
+        """
+        try:
+            data_drift_report_path = Path("reports/data_drift.json")
+            if not data_drift_report_path.exists():
+                raise FileNotFoundError("'data_drift.json' file not found")
+
+            with open(data_drift_report_path, "r") as file:
+                report = json.load(file)
+
+            logging.info("Evaluating data drift report")
+
+            metrics = report.get("metrics", [])
+            drift_count_metric = next(
+                (m for m in metrics if m["metric_id"].startswith("DriftedColumnsCount")), None
+            )
+
+            if drift_count_metric is None:
+                raise ValueError("DriftedColumnsCount metric not found in report")
+
+            drift_count = drift_count_metric["value"]["count"]
+            drift_share = drift_count_metric["value"]["share"]
+
+            # Evidently default drift detection threshold: drift_share >= 0.5
+            DRIFT_SHARE_THRESHOLD = 0.5
+
+            if drift_share >= DRIFT_SHARE_THRESHOLD:
+                logging.error(f"Data drift detected: {drift_count} columns drifted ({drift_share:.0%} of total columns).")
+                with open(self.validation_config.validation_status_file, "w") as f:
+                    f.write("Validation Status : Failed")
+                raise ValueError("Data drift detected")
+            else:
+                logging.info("No significant data drift detected")
+                with open(self.validation_config.validation_status_file, "w") as f:
+                    f.write("Validation Status : Successful")
+
+        except Exception as e:
+            logging.error(f"Error occured in checking for data drift: {e}", exc_info=True)
+            raise AppException(e, sys)
+    
+    
+    def _get_features(self, train_data: pd.DataFrame, test_data: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
+        """
+        Gets the features required for preparing the data drift report.
+        Args:
+            train_data (pd.DataFrame): The training data.
+            test_data (pd.DataFrame): The test data.
+
+        Returns:
+            tuple: A tuple containing the modified train and test dataframes with added features.
+        """
+        try:
+            train_data["num_words"] = train_data["Content"].apply(lambda x: len(x.split()))
+            train_data["num_characters"] = train_data["Content"].apply(lambda x: len(x.replace(" ", "")))
+            train_data['avg_word_len'] = round(train_data["num_characters"]/train_data["num_words"], 4)
+
+            test_data["num_words"] = test_data["Content"].apply(lambda x: len(x.split()))
+            test_data["num_characters"] = test_data["Content"].apply(lambda x: len(x.replace(" ", "")))
+            test_data['avg_word_len'] = round(test_data["num_characters"]/test_data["num_words"], 4)
+
+            return train_data, test_data
+
+        except Exception as e:
+            logging.error(f"Failed to get features required for preparing data drift report: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+
+    def validate_dataset(self, df: pd.DataFrame):
+        """
+        Validates the given dataframe for required columns and labels. Also checks for class imbalance.
+        Args:
+            df (pd.DataFrame): The dataframe to be validated.
+        
+        Raises:
+            ValueError: If the dataframe does not have required columns or labels.
+            ValueError: If the class imbalance ratio is greater than 1.6.
+            AppException: If any other error occurs during the validation process.
+        """
+        try:
+            required_columns = ["Content", "Label"]
+            required_labels = [1, 0]
+            
+            columns = [col for col in required_columns if col not in df.columns]
+            labels = [label for label in required_labels if label not in df["Label"].unique()]
+            
+            if not columns or not labels:
+                logging.info("All required columns and labels are present")
+            else:
+                with open(self.validation_config.validation_status_file, "w") as f:
+                    f.write("Validation Status : Failed")
+                raise ValueError(f"Required columns/labels not satisfied. Verify data.")
+            
+            # Check for class imbalance
+            labels_count = df["Label"].value_counts()
+            majority_class_count = labels_count.max()
+            minority_class_count = labels_count.min()
+            class_imbalance_ratio = majority_class_count/minority_class_count
+
+            if class_imbalance_ratio > 1.6 :
+                raise ValueError(f"High class label imbalance found in data")
+            elif class_imbalance_ratio > 1.3 :
+                logging.warning("Class label imbalance exists. Model performance might be affected.")
+            else:
+                logging.info("No major class imbalance detected")
+
+        except Exception as e:
+            logging.error(f"Feature engineering operation terminated: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+
+def initiate_data_validation():
+    """
+    Initiates the data validation workflow.
+
+    This function initializes a DataValidation object, reads the ingested, 
+    training, and testing datasets, and performs validation checks on the 
+    ingested dataset. It generates a data drift report and checks for any 
+    significant data drift between the training and testing datasets.
+
+    Raises:
+        AppException: If any error occurs during the data validation process.
+    """
+
+    obj = DataValidation()
+
+    try:
+        logging.info(f"{'='*20}Data Validation{'='*20}")
+        ingested_data_path = obj.validation_config.ingested_data_path
+        train_data_path = obj.validation_config.train_data_path
+        test_data_path = obj.validation_config.test_data_path
+
+        ingested_df = pd.read_csv(ingested_data_path)
+        train_df = pd.read_parquet(train_data_path)
+        test_df = pd.read_parquet(test_data_path)
+
+        obj.validate_dataset(df=ingested_df)
+        obj.get_data_drift_report(train_df, test_df)
+        obj.check_data_drift()
+        # free memory
+        del ingested_df, train_df, test_df
+        gc.collect()
+        logging.info(f"{'='*20}Data Validation Completed Successfully{'='*20} \n\n")
+
+    except Exception as e:
+        logging.error(f"Data Validation failed: {e}", exc_info=True)
+        raise AppException(e, sys)
+    
+
+if __name__ == "__main__":
     initiate_data_validation()
\ No newline at end of file
diff --git a/components/feature_engineering.py b/components/feature_engineering.py
index 38c780186748ccaba53464b6667332b9b8d27875..44665cc4936b7e002429daf229363167d820ab67 100644
--- a/components/feature_engineering.py
+++ b/components/feature_engineering.py
@@ -1,94 +1,94 @@
-import sys
-from src.core.constants import PARAMS_FILE
-from src.core.configuration import AppConfiguration
-from src.core.logger import logging
-from src.core.exception import AppException
-from src.utils import read_yaml, save_obj
-import gc
-import pandas as pd
-from scipy.sparse import csr_matrix
-from pathlib import Path
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-
-class FeatureEngineering:
-    def __init__(self, config = AppConfiguration()):
-        """
-        Initializes the FeatureEngineering object by creating a feature engineering configuration.
-        Args:
-            config (AppConfiguration): The configuration object containing the application configuration.
-        """
-        try:
-            self.eng_config = config.feature_engineering_config()
-
-        except Exception as e:
-            logging.error(f"Failed to create feature engineering Configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-
-
-    def perform_feature_engineering(self, df: pd.DataFrame):
-        """
-        Performs feature engineering on the given dataframe by extracting features with TF-IDF vectorization 
-        and also splits data into training and testing sets.
-        Saves the vectorizer object and training dataset.
-        """
-        try:
-            config_params = read_yaml(PARAMS_FILE)
-            params = config_params.feature_engineering
-            vectorizer_name = params.vectorizer
-
-            vectorizer = TfidfVectorizer(max_features=params.max_features,
-                                        min_df=params.min_df, ngram_range=(params.ngrams.min, params.ngrams.max)
-                                    )
-
-            logging.info("Performing TF-IDF vectorization")
-            X_tfidf = vectorizer.fit_transform(df['Content'])
-            X_tfidf = csr_matrix(X_tfidf)
-
-            training_data = pd.DataFrame(X_tfidf.toarray())
-            training_data['Label'] = df['Label'].values
-
-            save_model_path = self.eng_config.models_dir
-            save_obj(location_path=save_model_path, obj_name=f"vectorizer.joblib", obj=vectorizer)
-            
-            with open(Path(save_model_path, "vectorizer_meta.txt"), 'w') as f:
-                f.write(f"{vectorizer_name} has been created and fitted on the training data\n\n {params}")
-            
-            logging.info("Saving training dataset")
-            training_data.to_feather(self.eng_config.training_data_path)
-
-            logging.info("Feature engineering operation done")
-
-        except Exception as e:
-            logging.error(f"Error - feature engineering operation terminated: {e}", exc_info=True)
-            raise AppException(e, sys)
-
-
-def initiate_feature_engineering():
-    """
-    Main function to initiate the feature engineering workflow. It reads preprocessed data, 
-    performs feature engineering on the data, and splits data into training and testing sets.
-
-    Raises:
-        AppException: If an error occurs during feature engineering.
-    """
-    obj = FeatureEngineering()
-    try:
-        logging.info(f"{'='*20}Feature Engineering{'='*20}")
-        data_path = obj.eng_config.preprocessed_data_path
-        if not data_path:
-            logging.error("Dataset path after preprocessing stage not found")
-        df = pd.read_feather(data_path)
-        df.dropna(how='any', inplace=True)
-        obj.perform_feature_engineering(df)
-        del df, obj
-        gc.collect()
-        logging.info(f"{'='*20}Feature Engineering Completed Successfully{'='*20} \n\n")
-
-    except Exception as e:
-        logging.error(f"Error during Feature Engineering: {e}", exc_info=True)
-        raise AppException(e, sys)
-    
-
-if __name__ == "__main__":
+import sys
+from src.core.constants import PARAMS_FILE
+from src.core.configuration import AppConfiguration
+from src.core.logger import logging
+from src.core.exception import AppException
+from src.utils import read_yaml, save_obj
+import gc
+import pandas as pd
+from scipy.sparse import csr_matrix
+from pathlib import Path
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+
+class FeatureEngineering:
+    def __init__(self, config = AppConfiguration()):
+        """
+        Initializes the FeatureEngineering object by creating a feature engineering configuration.
+        Args:
+            config (AppConfiguration): The configuration object containing the application configuration.
+        """
+        try:
+            self.eng_config = config.feature_engineering_config()
+
+        except Exception as e:
+            logging.error(f"Failed to create feature engineering Configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+
+    def perform_feature_engineering(self, df: pd.DataFrame):
+        """
+        Performs feature engineering on the given dataframe by extracting features with TF-IDF vectorization 
+        and also splits data into training and testing sets.
+        Saves the vectorizer object and training dataset.
+        """
+        try:
+            config_params = read_yaml(PARAMS_FILE)
+            params = config_params.feature_engineering
+            vectorizer_name = params.vectorizer
+
+            vectorizer = TfidfVectorizer(max_features=params.max_features,
+                                        min_df=params.min_df, ngram_range=(params.ngrams.min, params.ngrams.max)
+                                    )
+
+            logging.info("Performing TF-IDF vectorization")
+            X_tfidf = vectorizer.fit_transform(df['Content'])
+            X_tfidf = csr_matrix(X_tfidf)
+
+            training_data = pd.DataFrame(X_tfidf.toarray())
+            training_data['Label'] = df['Label'].values
+
+            save_model_path = self.eng_config.models_dir
+            save_obj(location_path=save_model_path, obj_name=f"vectorizer.joblib", obj=vectorizer)
+            
+            with open(Path(save_model_path, "vectorizer_meta.txt"), 'w') as f:
+                f.write(f"{vectorizer_name} has been created and fitted on the training data\n\n {params}")
+            
+            logging.info("Saving training dataset")
+            training_data.to_feather(self.eng_config.training_data_path)
+
+            logging.info("Feature engineering operation done")
+
+        except Exception as e:
+            logging.error(f"Error - feature engineering operation terminated: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+
+def initiate_feature_engineering():
+    """
+    Main function to initiate the feature engineering workflow. It reads preprocessed data, 
+    performs feature engineering on the data, and splits data into training and testing sets.
+
+    Raises:
+        AppException: If an error occurs during feature engineering.
+    """
+    obj = FeatureEngineering()
+    try:
+        logging.info(f"{'='*20}Feature Engineering{'='*20}")
+        data_path = obj.eng_config.preprocessed_data_path
+        if not data_path:
+            logging.error("Dataset path after preprocessing stage not found")
+        df = pd.read_feather(data_path)
+        df.dropna(how='any', inplace=True)
+        obj.perform_feature_engineering(df)
+        del df, obj
+        gc.collect()
+        logging.info(f"{'='*20}Feature Engineering Completed Successfully{'='*20} \n\n")
+
+    except Exception as e:
+        logging.error(f"Error during Feature Engineering: {e}", exc_info=True)
+        raise AppException(e, sys)
+    
+
+if __name__ == "__main__":
     initiate_feature_engineering()
\ No newline at end of file
diff --git a/components/model_evaluation.py b/components/model_evaluation.py
index 45bbfd0635152e83d1ced8c545df5383d4e8d425..e4af76a65c3b0ed8a23945d940f462a82fe9e72d 100644
--- a/components/model_evaluation.py
+++ b/components/model_evaluation.py
@@ -1,229 +1,229 @@
-import sys, os
-import json
-
-import pandas as pd
-from typing import Any
-from pathlib import Path
-from .data_preprocessing import DataPreprocessing
-from src.core.constants import PARAMS_FILE
-from src.core.logger import logging
-from src.core.exception import AppException
-from src.core.configuration import AppConfiguration
-from src.utils import create_directory, read_yaml, load_obj
-from sklearn.metrics import (accuracy_score, precision_score, recall_score,f1_score, roc_auc_score)
-
-import gc
-import dagshub
-import mlflow
-from mlflow.xgboost import log_model
-from src.script.model_wrapper import CustomModel
-from dotenv import load_dotenv
-load_dotenv()
-
-# get environment variables
-uri = os.getenv("MLFLOW_URI")
-dagshub_token = os.getenv("DAGSHUB_TOKEN")
-dagshub_username = os.getenv("OWNER")
-if not dagshub_token or not dagshub_username:
-    raise EnvironmentError("Dagshub environment variables is not set")
-
-os.environ["MLFLOW_TRACKING_USERNAME"] = dagshub_username
-os.environ["MLFLOW_TRACKING_PASSWORD"] = dagshub_token
-
-mlflow.set_tracking_uri(uri)            # type: ignore
-
-# For local use
-# =================================================================================
-# repo_owner = os.getenv("OWNER")
-# repo_name = os.getenv("REPO")
-# 
-# mlflow.set_tracking_uri(uri)
-# if repo_owner is None:
-# 	raise EnvironmentError("Missing dagshub logging environment credentials.")
-# dagshub.init(repo_owner=repo_owner, repo_name=repo_name, mlflow=True) 
-# ==================================================================================
-
-
-class ModelEvaluation:
-    def __init__(self, config = AppConfiguration()):
-        """
-        Initializes the ModelEvaluation object by creating a model evaluation configuration.
-        Args:
-            config (AppConfiguration): The configuration object containing the application configuration.
-        """
-        try:
-            self.evaluation_config = config.model_evaluation_config()
-
-        except Exception as e:
-            logging.error(f"Failed to create model evaluation configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-
-    def save_report(self, report: dict):
-        """
-        Saves the model evaluation metrics to a JSON file in the "reports" directory.
-        Args:
-            report (dict): A dictionary containing the model evaluation metrics.
-        """
-        try:
-            create_directory(Path("reports"))
-            with open("reports/metrics.json", 'w')  as f:
-                json.dump(report, f, indent=4)
-        
-        except Exception as e:
-            logging.error(f"Failed to save model evaluation report: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-    
-    def save_experiment_info(self, model_name: str, run_id: str):
-        """
-        Saves the model name and the Mlflow run ID to a JSON file 
-        for logging purposes.
-        Args:
-            model_name (str): The name of the model
-            run_id (str): The Mlflow run ID
-        """
-        try:
-            experiment_info = {
-                'model' : model_name,
-                'run_id' : run_id,
-            }
-            exp_info_path = Path("reports/experiment.json")
-            with open(exp_info_path, 'w') as f:
-                json.dump(experiment_info, f, indent=4)
-        
-        except Exception as e:
-            logging.error(f"Failed to save model experiment info: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-    def evaluate(self, model:Any, model_name:str, vectorizer:Any, vectorizer_name:str, 
-                 eval_threshold:float, df:pd.DataFrame) -> tuple[dict, dict]:
-        """
-        Evaluates the given model and vectorizer on the given dataset.
-        Args:
-            model (Any): The model to evaluate.
-            model_name (str): The name of the model.
-            vectorizer (Any): The vectorizer to use.
-            vectorizer_name (str): The name of the vectorizer.
-            df (pd.DataFrame): The test dataset to evaluate on.
-        
-        Returns:
-            tuple: A tuple containing the evaluation report and the model parameters.
-        """
-        try:
-            X_test = df.drop(columns='Label')
-            # Perform feature extraction
-            X_test = vectorizer.transform(X_test["Content"])
-            
-            logging.info(f"Evaluating model: {model_name}")
-            # Predict using the model
-            y_probs = model.predict_proba(X_test)[:, 1]
-
-            y_pred = (y_probs >= eval_threshold).astype(int)
- 
-            # Get the true labels
-            y_test = df['Label'].values
-
-            evaluation_report = {
-                "model": model_name,
-                "vectorizer": vectorizer_name,
-                "threshold": eval_threshold,
-                "accuracy": accuracy_score(y_test, y_pred),                 # type: ignore
-                "precision": precision_score(y_test, y_pred),               # type: ignore
-                "recall": recall_score(y_test, y_pred),                     # type: ignore
-                "f1 score": f1_score(y_test, y_pred),                       # type: ignore
-                "roc_auc": roc_auc_score(y_test, y_probs)                   # type: ignore
-            }
-            # Get XGBoost model parameters
-            model_params: dict = model.get_xgb_params()
-
-            return (
-                evaluation_report,
-                model_params
-            )
-        
-        except Exception as e:
-            logging.error(f"Failed in model evaluation process: {e}", exc_info=True)
-            raise AppException(e, sys)
-    
-
-def initiate_model_evaluation():
-    """
-    Initiates the model evaluation process by creating a ModelEvaluation object,
-    which then evaluates the model using the evaluate method and logs the
-    evaluation metrics in Mlflow.
-    """
-    eval_obj = ModelEvaluation()
-    preprocessor = DataPreprocessing()
-    logging.info(f"{'='*20}Model Evaluation{'='*20}")
-
-    # get model name
-    config_params = read_yaml(PARAMS_FILE)
-    model_name = config_params.model_training.model_name
-    vectorizer_name = config_params.feature_engineering.vectorizer
-    eval_threshold = config_params.model_evaluation.threshold
-
-    test_data_path = eval_obj.evaluation_config.test_data_path
-    model_path = eval_obj.evaluation_config.models_dir
-
-    test_df = pd.read_parquet(test_data_path)
-    test_df.dropna(how='any', inplace=True)
-    processed_test_df = preprocessor.preprocess(test_df, filename="preprocessed_test_data.feather")
-
-    mlflow.set_experiment("DVC Pipeline Model Experiments")
-    with mlflow.start_run(run_name=model_name) as run:
-        try:
-            model = load_obj(location_path=model_path, obj_name="model.joblib")
-            vectorizer = load_obj(location_path=model_path, obj_name="vectorizer.joblib")
-
-            evaluation_report, model_params = eval_obj.evaluate(model=model, model_name=model_name, vectorizer=vectorizer,
-                                                                vectorizer_name=vectorizer_name, eval_threshold=eval_threshold, df=processed_test_df)
-
-            eval_obj.save_report(evaluation_report)
-            logging.info("Logging model and different parameters in Mlflow")
-
-            # Log model evaluation metrics in Mlflow
-            # remove the model and vectorizer name from the report
-            evaluation_report.pop("model", None)
-            evaluation_report.pop("vectorizer", None)
-
-            for metric_name, metric_score in evaluation_report.items():
-               mlflow.log_metric(metric_name, metric_score)
-
-            # log model parameters in Mlflow
-            if model_params is not None:
-                for param_name, param_value in model_params.items():
-                    mlflow.log_param(param_name, param_value)
-                mlflow.log_param("model", model_name)
-                mlflow.log_param("vectorizer", vectorizer_name)
-            else:
-                logging.warning("No model parameters found. Skipping parameter logging")   
-
-            # Custom Model wraps the classifer and the vectorizer into a single Python Model object
-            final_model = CustomModel(model=model, vectorizer=vectorizer)
-            
-            # Log the model and artifacts
-            mlflow.pyfunc.log_model(
-                artifact_path = model_name,
-                python_model = final_model,
-                artifacts={"vectorizer": os.path.join(model_path, "vectorizer.joblib"),
-                           "model": os.path.join(model_path, "model.joblib"),
-                           "booster": os.path.join(model_path, "booster.json"),
-                           "metrics": os.path.join("reports", "metrics.json")}
-            )
-
-            # save model info
-            eval_obj.save_experiment_info(model_name=model_name, run_id=run.info.run_id)
-            logging.info("Model evaluation completed")
-
-            #free memory
-            del test_df, processed_test_df, model, vectorizer
-            gc.collect()
-
-        except Exception as e:
-            logging.error(f"Error during model evaluation: {e}", exc_info=True)
-            raise AppException(e, sys)
-
- 
-if __name__ == "__main__":
+import sys, os
+import json
+
+import pandas as pd
+from typing import Any
+from pathlib import Path
+from .data_preprocessing import DataPreprocessing
+from src.core.constants import PARAMS_FILE
+from src.core.logger import logging
+from src.core.exception import AppException
+from src.core.configuration import AppConfiguration
+from src.utils import create_directory, read_yaml, load_obj
+from sklearn.metrics import (accuracy_score, precision_score, recall_score,f1_score, roc_auc_score)
+
+import gc
+import dagshub
+import mlflow
+from mlflow.xgboost import log_model
+from src.script.model_wrapper import CustomModel
+from dotenv import load_dotenv
+load_dotenv()
+
+# get environment variables
+uri = os.getenv("MLFLOW_URI")
+dagshub_token = os.getenv("DAGSHUB_TOKEN")
+dagshub_username = os.getenv("OWNER")
+if not dagshub_token or not dagshub_username:
+    raise EnvironmentError("Dagshub environment variables is not set")
+
+os.environ["MLFLOW_TRACKING_USERNAME"] = dagshub_username
+os.environ["MLFLOW_TRACKING_PASSWORD"] = dagshub_token
+
+mlflow.set_tracking_uri(uri)            # type: ignore
+
+# For local use
+# =================================================================================
+# repo_owner = os.getenv("OWNER")
+# repo_name = os.getenv("REPO")
+# 
+# mlflow.set_tracking_uri(uri)
+# if repo_owner is None:
+# 	raise EnvironmentError("Missing dagshub logging environment credentials.")
+# dagshub.init(repo_owner=repo_owner, repo_name=repo_name, mlflow=True) 
+# ==================================================================================
+
+
+class ModelEvaluation:
+    def __init__(self, config = AppConfiguration()):
+        """
+        Initializes the ModelEvaluation object by creating a model evaluation configuration.
+        Args:
+            config (AppConfiguration): The configuration object containing the application configuration.
+        """
+        try:
+            self.evaluation_config = config.model_evaluation_config()
+
+        except Exception as e:
+            logging.error(f"Failed to create model evaluation configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+
+    def save_report(self, report: dict):
+        """
+        Saves the model evaluation metrics to a JSON file in the "reports" directory.
+        Args:
+            report (dict): A dictionary containing the model evaluation metrics.
+        """
+        try:
+            create_directory(Path("reports"))
+            with open("reports/metrics.json", 'w')  as f:
+                json.dump(report, f, indent=4)
+        
+        except Exception as e:
+            logging.error(f"Failed to save model evaluation report: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+    
+    def save_experiment_info(self, model_name: str, run_id: str):
+        """
+        Saves the model name and the Mlflow run ID to a JSON file 
+        for logging purposes.
+        Args:
+            model_name (str): The name of the model
+            run_id (str): The Mlflow run ID
+        """
+        try:
+            experiment_info = {
+                'model' : model_name,
+                'run_id' : run_id,
+            }
+            exp_info_path = Path("reports/experiment.json")
+            with open(exp_info_path, 'w') as f:
+                json.dump(experiment_info, f, indent=4)
+        
+        except Exception as e:
+            logging.error(f"Failed to save model experiment info: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+    def evaluate(self, model:Any, model_name:str, vectorizer:Any, vectorizer_name:str, 
+                 eval_threshold:float, df:pd.DataFrame) -> tuple[dict, dict]:
+        """
+        Evaluates the given model and vectorizer on the given dataset.
+        Args:
+            model (Any): The model to evaluate.
+            model_name (str): The name of the model.
+            vectorizer (Any): The vectorizer to use.
+            vectorizer_name (str): The name of the vectorizer.
+            df (pd.DataFrame): The test dataset to evaluate on.
+        
+        Returns:
+            tuple: A tuple containing the evaluation report and the model parameters.
+        """
+        try:
+            X_test = df.drop(columns='Label')
+            # Perform feature extraction
+            X_test = vectorizer.transform(X_test["Content"])
+            
+            logging.info(f"Evaluating model: {model_name}")
+            # Predict using the model
+            y_probs = model.predict_proba(X_test)[:, 1]
+
+            y_pred = (y_probs >= eval_threshold).astype(int)
+ 
+            # Get the true labels
+            y_test = df['Label'].values
+
+            evaluation_report = {
+                "model": model_name,
+                "vectorizer": vectorizer_name,
+                "threshold": eval_threshold,
+                "accuracy": accuracy_score(y_test, y_pred),                 # type: ignore
+                "precision": precision_score(y_test, y_pred),               # type: ignore
+                "recall": recall_score(y_test, y_pred),                     # type: ignore
+                "f1 score": f1_score(y_test, y_pred),                       # type: ignore
+                "roc_auc": roc_auc_score(y_test, y_probs)                   # type: ignore
+            }
+            # Get XGBoost model parameters
+            model_params: dict = model.get_xgb_params()
+
+            return (
+                evaluation_report,
+                model_params
+            )
+        
+        except Exception as e:
+            logging.error(f"Failed in model evaluation process: {e}", exc_info=True)
+            raise AppException(e, sys)
+    
+
+def initiate_model_evaluation():
+    """
+    Initiates the model evaluation process by creating a ModelEvaluation object,
+    which then evaluates the model using the evaluate method and logs the
+    evaluation metrics in Mlflow.
+    """
+    eval_obj = ModelEvaluation()
+    preprocessor = DataPreprocessing()
+    logging.info(f"{'='*20}Model Evaluation{'='*20}")
+
+    # get model name
+    config_params = read_yaml(PARAMS_FILE)
+    model_name = config_params.model_training.model_name
+    vectorizer_name = config_params.feature_engineering.vectorizer
+    eval_threshold = config_params.model_evaluation.threshold
+
+    test_data_path = eval_obj.evaluation_config.test_data_path
+    model_path = eval_obj.evaluation_config.models_dir
+
+    test_df = pd.read_parquet(test_data_path)
+    test_df.dropna(how='any', inplace=True)
+    processed_test_df = preprocessor.preprocess(test_df, filename="preprocessed_test_data.feather")
+
+    mlflow.set_experiment("DVC Pipeline Model Experiments")
+    with mlflow.start_run(run_name=model_name) as run:
+        try:
+            model = load_obj(location_path=model_path, obj_name="model.joblib")
+            vectorizer = load_obj(location_path=model_path, obj_name="vectorizer.joblib")
+
+            evaluation_report, model_params = eval_obj.evaluate(model=model, model_name=model_name, vectorizer=vectorizer,
+                                                                vectorizer_name=vectorizer_name, eval_threshold=eval_threshold, df=processed_test_df)
+
+            eval_obj.save_report(evaluation_report)
+            logging.info("Logging model and different parameters in Mlflow")
+
+            # Log model evaluation metrics in Mlflow
+            # remove the model and vectorizer name from the report
+            evaluation_report.pop("model", None)
+            evaluation_report.pop("vectorizer", None)
+
+            for metric_name, metric_score in evaluation_report.items():
+               mlflow.log_metric(metric_name, metric_score)
+
+            # log model parameters in Mlflow
+            if model_params is not None:
+                for param_name, param_value in model_params.items():
+                    mlflow.log_param(param_name, param_value)
+                mlflow.log_param("model", model_name)
+                mlflow.log_param("vectorizer", vectorizer_name)
+            else:
+                logging.warning("No model parameters found. Skipping parameter logging")   
+
+            # Custom Model wraps the classifer and the vectorizer into a single Python Model object
+            final_model = CustomModel(model=model, vectorizer=vectorizer)
+            
+            # Log the model and artifacts
+            mlflow.pyfunc.log_model(
+                artifact_path = model_name,
+                python_model = final_model,
+                artifacts={"vectorizer": os.path.join(model_path, "vectorizer.joblib"),
+                           "model": os.path.join(model_path, "model.joblib"),
+                           "booster": os.path.join(model_path, "booster.json"),
+                           "metrics": os.path.join("reports", "metrics.json")}
+            )
+
+            # save model info
+            eval_obj.save_experiment_info(model_name=model_name, run_id=run.info.run_id)
+            logging.info("Model evaluation completed")
+
+            #free memory
+            del test_df, processed_test_df, model, vectorizer
+            gc.collect()
+
+        except Exception as e:
+            logging.error(f"Error during model evaluation: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+ 
+if __name__ == "__main__":
     initiate_model_evaluation()
\ No newline at end of file
diff --git a/components/model_training.py b/components/model_training.py
index 55fd0e21bc9eafa53ae78ad2b63e096b7f74ed1f..6150f9fcec57bb2766762684f0e79eed9c2f1877 100644
--- a/components/model_training.py
+++ b/components/model_training.py
@@ -1,107 +1,107 @@
-import sys
-import pandas as pd
-from pathlib import Path
-import pandas as pd
-from xgboost import XGBClassifier
-from src.core.constants import PARAMS_FILE
-from src.core.logger import logging
-from src.core.exception import AppException
-from src.core.configuration import AppConfiguration
-from src.utils import read_yaml, save_obj
-import gc
-
-class ModelTrainer:
-    def __init__(self, config = AppConfiguration()):
-        """
-        Initializes the ModelTrainer object by creating a model training configuration.
-        Args:
-            config (AppConfiguration): The configuration object containing the application configuration.
-        """
-        try:
-            self.model_training_config = config.model_training_config()
-
-        except Exception as e:
-            logging.error(f"Failed to create model training configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-
-        
-    def train(self, df):
-        """
-        Trains ML model on the given training data and saves the model.
-
-        Args:
-            df: The training dataframe
-        """
-        y_train = df['Label'].values
-        X_train = df.drop(columns='Label')
-
-        try:
-            config_params = read_yaml(PARAMS_FILE)
-            params = config_params.model_training
-
-            model = XGBClassifier(n_estimators=params.hyperparameters.n_estimators,
-                              learning_rate=params.hyperparameters.learning_rate,
-                              max_depth=params.hyperparameters.max_depth, 
-                              tree_method=params.hyperparameters.tree_method,
-                              max_bin=params.hyperparameters.max_bin,
-                              scale_pos_weight=params.hyperparameters.scale_pos_weight,
-                              gamma=params.hyperparameters.gamma,
-                              reg_lambda=params.hyperparameters.reg_lambda,
-                              subsample=params.hyperparameters.subsample,
-                              colsample_bytree=params.hyperparameters.colsample_bytree,
-                              n_jobs=-1, random_state=42, use_label_encoder=False, predictor="cpu_predictor"
-                            )
-        
-            logging.info("Model training started")
-            model.fit(X_train, y_train)
-            # get XGB booster
-            booster = model.get_booster()
-            
-            save_model_path = self.model_training_config.models_dir
-            
-            # save models
-            booster.save_model(Path(save_model_path, "booster.json"))
-            save_obj(location_path=save_model_path, obj=model, obj_name=f"model.joblib")
-            logging.info(f"Model trained and saved at: {save_model_path}")
-
-            with open(Path(save_model_path, "model_meta.txt"), 'w') as f:
-                f.write(f"{params.model_name} model has been trained successfully.\n\n {params}")
-
-            # free memory
-            del X_train, y_train, model
-            gc.collect()
-    
-        except Exception as e:
-            logging.error(f"Failed to train model: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-    
-def initiate_model_training():
-    """
-    Main function to initiate the model training workflow. It reads the training dataset,
-    trains an ML model and saves the model.
-    
-    Raises:
-        AppException: If an error occurs during model training.
-    """
-    obj = ModelTrainer()
-    try:
-        logging.info(f"{'='*20}Model Training{'='*20}")
-        train_data_path = obj.model_training_config.training_data_path
-        if not train_data_path:
-            logging.error("Training dataset path not found")
-            return
-        df = pd.read_feather(train_data_path)
-        df.dropna(how='any', inplace=True)
-        obj.train(df)
-        del df
-        gc.collect()
-        logging.info(f"{'='*20}Model Training Completed Successfully{'='*20} \n\n")
-
-    except Exception as e:
-        logging.error(f"Error during model training: {e}", exc_info=True)
-        raise AppException(e, sys)
-    
-
-if __name__ == "__main__":  
+import sys
+import pandas as pd
+from pathlib import Path
+import pandas as pd
+from xgboost import XGBClassifier
+from src.core.constants import PARAMS_FILE
+from src.core.logger import logging
+from src.core.exception import AppException
+from src.core.configuration import AppConfiguration
+from src.utils import read_yaml, save_obj
+import gc
+
+class ModelTrainer:
+    def __init__(self, config = AppConfiguration()):
+        """
+        Initializes the ModelTrainer object by creating a model training configuration.
+        Args:
+            config (AppConfiguration): The configuration object containing the application configuration.
+        """
+        try:
+            self.model_training_config = config.model_training_config()
+
+        except Exception as e:
+            logging.error(f"Failed to create model training configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+        
+    def train(self, df):
+        """
+        Trains ML model on the given training data and saves the model.
+
+        Args:
+            df: The training dataframe
+        """
+        y_train = df['Label'].values
+        X_train = df.drop(columns='Label')
+
+        try:
+            config_params = read_yaml(PARAMS_FILE)
+            params = config_params.model_training
+
+            model = XGBClassifier(n_estimators=params.hyperparameters.n_estimators,
+                              learning_rate=params.hyperparameters.learning_rate,
+                              max_depth=params.hyperparameters.max_depth, 
+                              tree_method=params.hyperparameters.tree_method,
+                              max_bin=params.hyperparameters.max_bin,
+                              scale_pos_weight=params.hyperparameters.scale_pos_weight,
+                              gamma=params.hyperparameters.gamma,
+                              reg_lambda=params.hyperparameters.reg_lambda,
+                              subsample=params.hyperparameters.subsample,
+                              colsample_bytree=params.hyperparameters.colsample_bytree,
+                              n_jobs=-1, random_state=42, use_label_encoder=False, predictor="cpu_predictor"
+                            )
+        
+            logging.info("Model training started")
+            model.fit(X_train, y_train)
+            # get XGB booster
+            booster = model.get_booster()
+            
+            save_model_path = self.model_training_config.models_dir
+            
+            # save models
+            booster.save_model(Path(save_model_path, "booster.json"))
+            save_obj(location_path=save_model_path, obj=model, obj_name=f"model.joblib")
+            logging.info(f"Model trained and saved at: {save_model_path}")
+
+            with open(Path(save_model_path, "model_meta.txt"), 'w') as f:
+                f.write(f"{params.model_name} model has been trained successfully.\n\n {params}")
+
+            # free memory
+            del X_train, y_train, model
+            gc.collect()
+    
+        except Exception as e:
+            logging.error(f"Failed to train model: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+    
+def initiate_model_training():
+    """
+    Main function to initiate the model training workflow. It reads the training dataset,
+    trains an ML model and saves the model.
+    
+    Raises:
+        AppException: If an error occurs during model training.
+    """
+    obj = ModelTrainer()
+    try:
+        logging.info(f"{'='*20}Model Training{'='*20}")
+        train_data_path = obj.model_training_config.training_data_path
+        if not train_data_path:
+            logging.error("Training dataset path not found")
+            return
+        df = pd.read_feather(train_data_path)
+        df.dropna(how='any', inplace=True)
+        obj.train(df)
+        del df
+        gc.collect()
+        logging.info(f"{'='*20}Model Training Completed Successfully{'='*20} \n\n")
+
+    except Exception as e:
+        logging.error(f"Error during model training: {e}", exc_info=True)
+        raise AppException(e, sys)
+    
+
+if __name__ == "__main__":  
     initiate_model_training()
\ No newline at end of file
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diff --git a/core/__pycache__/mongo_client.cpython-311.pyc b/core/__pycache__/mongo_client.cpython-311.pyc
index ce25ba2abe729190f40fa8b805e66d18491e32b0..7218cde8f5cdf721497ee1ac6629a38d7461fa4d 100644
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diff --git a/core/config_entity.py b/core/config_entity.py
index 722c6114320c16c0c682e8fc0804e0056ff1bc2f..be25a8d82430a927fa3488480caa1b6300737f18 100644
--- a/core/config_entity.py
+++ b/core/config_entity.py
@@ -1,43 +1,43 @@
-from dataclasses import dataclass
-from pathlib import Path
-
-@dataclass(frozen=True)
-class DataIngestionConfig:
-    ingested_data_path: Path
-    train_data_path: Path
-    test_data_path: Path
-
-@dataclass(frozen=True)
-class DataValidationConfig:
-    ingested_data_path: Path
-    train_data_path: Path
-    test_data_path: Path
-    validation_status_file: Path
-
-@dataclass(frozen=True)
-class DataPreprocessingConfig:
-    data_path: Path
-    preprocessed_data_dir: Path
-    preprocessed_data_filename: str
-
-@dataclass(frozen=True)
-class FeatureEngineeringConfig:
-    models_dir: Path
-    preprocessed_data_path: Path
-    training_data_path: Path
-
-@dataclass(frozen=True)
-class ModelTrainingConfig:
-    training_data_path: Path
-    models_dir: Path
-
-@dataclass(frozen=True)
-class ModelEvaluationConfig:
-    test_data_path: Path
-    models_dir: Path
-    evaluation_report_filepath: Path
-    experiment_info_filepath: Path
-
-@dataclass(frozen=True)
-class ModelRegistrationConfig:
+from dataclasses import dataclass
+from pathlib import Path
+
+@dataclass(frozen=True)
+class DataIngestionConfig:
+    ingested_data_path: Path
+    train_data_path: Path
+    test_data_path: Path
+
+@dataclass(frozen=True)
+class DataValidationConfig:
+    ingested_data_path: Path
+    train_data_path: Path
+    test_data_path: Path
+    validation_status_file: Path
+
+@dataclass(frozen=True)
+class DataPreprocessingConfig:
+    data_path: Path
+    preprocessed_data_dir: Path
+    preprocessed_data_filename: str
+
+@dataclass(frozen=True)
+class FeatureEngineeringConfig:
+    models_dir: Path
+    preprocessed_data_path: Path
+    training_data_path: Path
+
+@dataclass(frozen=True)
+class ModelTrainingConfig:
+    training_data_path: Path
+    models_dir: Path
+
+@dataclass(frozen=True)
+class ModelEvaluationConfig:
+    test_data_path: Path
+    models_dir: Path
+    evaluation_report_filepath: Path
+    experiment_info_filepath: Path
+
+@dataclass(frozen=True)
+class ModelRegistrationConfig:
     experiment_info_filepath: Path
\ No newline at end of file
diff --git a/core/configuration.py b/core/configuration.py
index d9750c373b6611fa2c8d23e6690e48afca1dd95b..1eb1f6e7c120194f3eb848403a8ab9748fab2d37 100644
--- a/core/configuration.py
+++ b/core/configuration.py
@@ -1,222 +1,222 @@
-# Project pipeline configuration 
-import sys
-from pathlib import Path
-from .constants import CONFIG_FILE
-from ..utils import read_yaml, create_directory
-from ..core.logger import logging
-from ..core.exception import AppException
-from ..core.config_entity import (DataIngestionConfig, DataValidationConfig, DataPreprocessingConfig, 
-                                  FeatureEngineeringConfig, ModelTrainingConfig, ModelEvaluationConfig, ModelRegistrationConfig)
-
-# 
-class AppConfiguration:
-    # class variable to hold the configuration
-    _config = None
-    def __init__(self, config_filepath: Path = CONFIG_FILE):
-        
-        """
-        Initializes the AppConfiguration object by loading the configuration from the given file path.
-        Args:
-            config_filepath (Path): The path to the configuration YAML file. Defaults to CONFIG_FILE_PATH.
-        
-        Raises:
-            AppException: If an error occurs while loading the configuration.
-        """
-        if AppConfiguration._config is None:
-            try:
-                AppConfiguration._config = read_yaml(config_filepath)
-                
-            except Exception as e:
-                logging.error(f"Failed to load configuration: {e}", exc_info=True)
-                raise AppException(e, sys)       
-            
-        self.config = AppConfiguration._config
-
-        
-    def data_ingestion_config(self) -> DataIngestionConfig:
-        """
-        Creates the configuration for Data Ingestion 
-        Returns: DataIngestionConfig object
-        """
-        try:
-            ingestion_config = self.config.data_ingestion
-
-            ingestion_root_dir = Path(ingestion_config.root_dir)
-            ingested_data_dir = Path(ingestion_root_dir, ingestion_config.ingested_data_dir)
-            ingested_dataset_name = ingestion_config.ingested_dataset
-            train_data_filename = ingestion_config.train_dataset
-            test_data_filename = ingestion_config.test_dataset
-
-            create_directory(ingestion_root_dir)
-            create_directory(ingested_data_dir)
-
-            ingestion_configuration = DataIngestionConfig(
-                ingested_data_path = Path(ingested_data_dir, ingested_dataset_name), 
-                train_data_path = Path(ingestion_root_dir, train_data_filename),
-                test_data_path = Path(ingestion_root_dir, test_data_filename)
-            )
-            logging.info("Data Ingestion Configuration creation successfull")
-            return ingestion_configuration
-        
-        except Exception as e:
-            logging.error(f"Error while creating Data Ingestion Configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-
-
-    def data_validation_config(self) -> DataValidationConfig:
-        """
-        Creates the configuration for Data Validation.
-        Returns: DataValidationConfig object
-        """
-        try:
-            validation_config = self.config.data_validation
-            ingestion_configuration = self.data_ingestion_config()
-
-            validation_dir = Path(validation_config.root_dir)
-            reports_dir = Path(validation_config.reports_dir)
-            status_filename = validation_config.status_file
-            status_file_path = Path(validation_dir, status_filename)
-
-            create_directory(validation_dir)
-            create_directory(reports_dir)
-
-            validation_configuration = DataValidationConfig(
-                ingested_data_path = ingestion_configuration.ingested_data_path,
-                train_data_path = ingestion_configuration.train_data_path,
-                test_data_path = ingestion_configuration.test_data_path,
-                validation_status_file = status_file_path
-            )
-            logging.info("Data Validation Configuration creation successfull")
-            return validation_configuration
-        
-        except Exception as e:
-            logging.error(f"Error while creating Data Validation Configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-
-    def data_preprocessing_config(self) -> DataPreprocessingConfig:
-        """
-        Creates the configuration for Data Preprocessing.
-        Returns: DataPreprocessingConfig object
-        """
-        try:
-            preprocessing_config = self.config.data_preprocessing
-            ingestion_configuration = self.data_ingestion_config()
-
-            preprocessing_dir = Path(preprocessing_config.root_dir)
-            preprocessed_dataset_name = preprocessing_config.preprocessed_dataset 
-            create_directory(preprocessing_dir)
-
-            preprocessing_configuration = DataPreprocessingConfig(
-                preprocessed_data_dir = preprocessing_dir,
-                preprocessed_data_filename = preprocessed_dataset_name,
-                data_path = ingestion_configuration.train_data_path
-            )
-            logging.info("Data Preprocessing Configuration creation successfull")
-            return preprocessing_configuration
-        
-        except Exception as e:
-            logging.error(f"Error while creating Data Preprocessing Configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-
-    def feature_engineering_config(self) -> FeatureEngineeringConfig:
-        """
-        Creates the configuration for Feature Engineering.
-        Returns: FeatureEngineeringConfig object
-        """
-        try:
-            feature_eng_config = self.config.feature_engineering
-            preprocessing_configuration = self.data_preprocessing_config()
-
-            feature_eng_root_dir = Path(feature_eng_config.root_dir)
-            models_root_dir = Path(feature_eng_config.models_dir)
-            training_dataset = feature_eng_config.training_dataset
-            training_data_path = Path(feature_eng_root_dir, training_dataset)
-
-            create_directory(feature_eng_root_dir)
-            create_directory(models_root_dir)
-
-            feature_engineering_configuration = FeatureEngineeringConfig(
-                models_dir = models_root_dir,
-                preprocessed_data_path = Path(preprocessing_configuration.preprocessed_data_dir, preprocessing_configuration.preprocessed_data_filename), 
-                training_data_path = training_data_path
-            )
-            logging.info("Feature Engineering Configuration creation successfull")
-            return feature_engineering_configuration
-        
-        except Exception as e:
-            logging.error(f"Error while creating Feature Engineering Configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-
-
-    def model_training_config(self) -> ModelTrainingConfig:
-        """
-        Creates the configuration for Model Training.
-        Returns: ModelTrainingConfig object
-        """
-        try:
-            training_config = self.config.model_training
-            feature_engineering_configuration = self.feature_engineering_config()
-
-            models_dir_path = Path(training_config.models_dir)
-
-            training_configuration = ModelTrainingConfig(
-                training_data_path = feature_engineering_configuration.training_data_path,
-                models_dir = models_dir_path,
-            )
-            logging.info("Model Training Configuration creation successfull")
-            return training_configuration
-
-        except Exception as e:
-            logging.error(f"Error while creating Model Training Configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-
-    def model_evaluation_config(self) -> ModelEvaluationConfig:
-        """
-        Creates the configuration for Model Training.
-        Returns: ModelTrainingConfig object
-        """
-        try:
-            evaluation_config = self.config.model_evaluation
-            training_configuration = self.model_training_config()
-            ingestion_configuration = self.data_ingestion_config()
-
-            models_dir_path = training_configuration.models_dir
-            eval_report_filename = evaluation_config.evaluation_report
-            exp_info_filename = evaluation_config.experiment_info
-
-            reports_dir_path = Path(evaluation_config.reports_dir)
-            create_directory(reports_dir_path)
-
-            eval_report_filepath = Path(reports_dir_path, eval_report_filename)
-            exp_info_filepath = Path(reports_dir_path, exp_info_filename)
-
-            evaluation_configuration = ModelEvaluationConfig(
-                test_data_path = ingestion_configuration.test_data_path,
-                models_dir = models_dir_path,
-                evaluation_report_filepath = eval_report_filepath,
-                experiment_info_filepath = exp_info_filepath
-            )
-            logging.info("Model Evaluation Configuration creation successfull")
-            return evaluation_configuration
-
-        except Exception as e:
-            logging.error(f"Error while creating Model Training Configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-
-    def model_registration_config(self) -> ModelRegistrationConfig:
-        try:
-            evaluation_configuration = self.model_evaluation_config()
-            exp_info_filepath = evaluation_configuration.experiment_info_filepath
-
-            logging.info("Model Registration Configuration creation successfull")
-            return ModelRegistrationConfig(
-                experiment_info_filepath = exp_info_filepath
-            )
-        
-        except Exception as e:
-            logging.error(f"Error while creating Model Evaluation Configuration: {e}", exc_info=True)
+# Project pipeline configuration 
+import sys
+from pathlib import Path
+from .constants import CONFIG_FILE
+from ..utils import read_yaml, create_directory
+from ..core.logger import logging
+from ..core.exception import AppException
+from ..core.config_entity import (DataIngestionConfig, DataValidationConfig, DataPreprocessingConfig, 
+                                  FeatureEngineeringConfig, ModelTrainingConfig, ModelEvaluationConfig, ModelRegistrationConfig)
+
+# 
+class AppConfiguration:
+    # class variable to hold the configuration
+    _config = None
+    def __init__(self, config_filepath: Path = CONFIG_FILE):
+        
+        """
+        Initializes the AppConfiguration object by loading the configuration from the given file path.
+        Args:
+            config_filepath (Path): The path to the configuration YAML file. Defaults to CONFIG_FILE_PATH.
+        
+        Raises:
+            AppException: If an error occurs while loading the configuration.
+        """
+        if AppConfiguration._config is None:
+            try:
+                AppConfiguration._config = read_yaml(config_filepath)
+                
+            except Exception as e:
+                logging.error(f"Failed to load configuration: {e}", exc_info=True)
+                raise AppException(e, sys)       
+            
+        self.config = AppConfiguration._config
+
+        
+    def data_ingestion_config(self) -> DataIngestionConfig:
+        """
+        Creates the configuration for Data Ingestion 
+        Returns: DataIngestionConfig object
+        """
+        try:
+            ingestion_config = self.config.data_ingestion
+
+            ingestion_root_dir = Path(ingestion_config.root_dir)
+            ingested_data_dir = Path(ingestion_root_dir, ingestion_config.ingested_data_dir)
+            ingested_dataset_name = ingestion_config.ingested_dataset
+            train_data_filename = ingestion_config.train_dataset
+            test_data_filename = ingestion_config.test_dataset
+
+            create_directory(ingestion_root_dir)
+            create_directory(ingested_data_dir)
+
+            ingestion_configuration = DataIngestionConfig(
+                ingested_data_path = Path(ingested_data_dir, ingested_dataset_name), 
+                train_data_path = Path(ingestion_root_dir, train_data_filename),
+                test_data_path = Path(ingestion_root_dir, test_data_filename)
+            )
+            logging.info("Data Ingestion Configuration creation successfull")
+            return ingestion_configuration
+        
+        except Exception as e:
+            logging.error(f"Error while creating Data Ingestion Configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+
+    def data_validation_config(self) -> DataValidationConfig:
+        """
+        Creates the configuration for Data Validation.
+        Returns: DataValidationConfig object
+        """
+        try:
+            validation_config = self.config.data_validation
+            ingestion_configuration = self.data_ingestion_config()
+
+            validation_dir = Path(validation_config.root_dir)
+            reports_dir = Path(validation_config.reports_dir)
+            status_filename = validation_config.status_file
+            status_file_path = Path(validation_dir, status_filename)
+
+            create_directory(validation_dir)
+            create_directory(reports_dir)
+
+            validation_configuration = DataValidationConfig(
+                ingested_data_path = ingestion_configuration.ingested_data_path,
+                train_data_path = ingestion_configuration.train_data_path,
+                test_data_path = ingestion_configuration.test_data_path,
+                validation_status_file = status_file_path
+            )
+            logging.info("Data Validation Configuration creation successfull")
+            return validation_configuration
+        
+        except Exception as e:
+            logging.error(f"Error while creating Data Validation Configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+
+    def data_preprocessing_config(self) -> DataPreprocessingConfig:
+        """
+        Creates the configuration for Data Preprocessing.
+        Returns: DataPreprocessingConfig object
+        """
+        try:
+            preprocessing_config = self.config.data_preprocessing
+            ingestion_configuration = self.data_ingestion_config()
+
+            preprocessing_dir = Path(preprocessing_config.root_dir)
+            preprocessed_dataset_name = preprocessing_config.preprocessed_dataset 
+            create_directory(preprocessing_dir)
+
+            preprocessing_configuration = DataPreprocessingConfig(
+                preprocessed_data_dir = preprocessing_dir,
+                preprocessed_data_filename = preprocessed_dataset_name,
+                data_path = ingestion_configuration.train_data_path
+            )
+            logging.info("Data Preprocessing Configuration creation successfull")
+            return preprocessing_configuration
+        
+        except Exception as e:
+            logging.error(f"Error while creating Data Preprocessing Configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+
+    def feature_engineering_config(self) -> FeatureEngineeringConfig:
+        """
+        Creates the configuration for Feature Engineering.
+        Returns: FeatureEngineeringConfig object
+        """
+        try:
+            feature_eng_config = self.config.feature_engineering
+            preprocessing_configuration = self.data_preprocessing_config()
+
+            feature_eng_root_dir = Path(feature_eng_config.root_dir)
+            models_root_dir = Path(feature_eng_config.models_dir)
+            training_dataset = feature_eng_config.training_dataset
+            training_data_path = Path(feature_eng_root_dir, training_dataset)
+
+            create_directory(feature_eng_root_dir)
+            create_directory(models_root_dir)
+
+            feature_engineering_configuration = FeatureEngineeringConfig(
+                models_dir = models_root_dir,
+                preprocessed_data_path = Path(preprocessing_configuration.preprocessed_data_dir, preprocessing_configuration.preprocessed_data_filename), 
+                training_data_path = training_data_path
+            )
+            logging.info("Feature Engineering Configuration creation successfull")
+            return feature_engineering_configuration
+        
+        except Exception as e:
+            logging.error(f"Error while creating Feature Engineering Configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+
+    def model_training_config(self) -> ModelTrainingConfig:
+        """
+        Creates the configuration for Model Training.
+        Returns: ModelTrainingConfig object
+        """
+        try:
+            training_config = self.config.model_training
+            feature_engineering_configuration = self.feature_engineering_config()
+
+            models_dir_path = Path(training_config.models_dir)
+
+            training_configuration = ModelTrainingConfig(
+                training_data_path = feature_engineering_configuration.training_data_path,
+                models_dir = models_dir_path,
+            )
+            logging.info("Model Training Configuration creation successfull")
+            return training_configuration
+
+        except Exception as e:
+            logging.error(f"Error while creating Model Training Configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+
+    def model_evaluation_config(self) -> ModelEvaluationConfig:
+        """
+        Creates the configuration for Model Training.
+        Returns: ModelTrainingConfig object
+        """
+        try:
+            evaluation_config = self.config.model_evaluation
+            training_configuration = self.model_training_config()
+            ingestion_configuration = self.data_ingestion_config()
+
+            models_dir_path = training_configuration.models_dir
+            eval_report_filename = evaluation_config.evaluation_report
+            exp_info_filename = evaluation_config.experiment_info
+
+            reports_dir_path = Path(evaluation_config.reports_dir)
+            create_directory(reports_dir_path)
+
+            eval_report_filepath = Path(reports_dir_path, eval_report_filename)
+            exp_info_filepath = Path(reports_dir_path, exp_info_filename)
+
+            evaluation_configuration = ModelEvaluationConfig(
+                test_data_path = ingestion_configuration.test_data_path,
+                models_dir = models_dir_path,
+                evaluation_report_filepath = eval_report_filepath,
+                experiment_info_filepath = exp_info_filepath
+            )
+            logging.info("Model Evaluation Configuration creation successfull")
+            return evaluation_configuration
+
+        except Exception as e:
+            logging.error(f"Error while creating Model Training Configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+
+    def model_registration_config(self) -> ModelRegistrationConfig:
+        try:
+            evaluation_configuration = self.model_evaluation_config()
+            exp_info_filepath = evaluation_configuration.experiment_info_filepath
+
+            logging.info("Model Registration Configuration creation successfull")
+            return ModelRegistrationConfig(
+                experiment_info_filepath = exp_info_filepath
+            )
+        
+        except Exception as e:
+            logging.error(f"Error while creating Model Evaluation Configuration: {e}", exc_info=True)
             raise AppException(e, sys)
\ No newline at end of file
diff --git a/core/constants.py b/core/constants.py
index 60717a7266bb34194e6168678b7ed27c84ff2598..9d2517beff7cae711be3a1f1312aaa65337ed573 100644
--- a/core/constants.py
+++ b/core/constants.py
@@ -1,27 +1,27 @@
-# Constants for configuration file paths
-# This module defines constants for the configuration file paths used in the application.
-
-import os
-from pathlib import Path
-
-# Get current working directory
-ROOT_DIR = os.getcwd()
-
-# configuration file path
-CONFIG_DIR = Path(ROOT_DIR, "configs")
-CONFIG_FILE = Path(CONFIG_DIR, "config.yaml")
-
-# Params file path
-PARAMS_FILE = Path(CONFIG_DIR, "params.yaml")
-
-# Database Constants
-DATABASE_NAME = "Tweets_DB"
-TRAINING_COLLECTION_NAME = "tweets_collection"
-PRODUCTION_COLLECTION_NAME = "production_tweets"
-FEEDBACK_COLLECTION_NAME = "prediction_feedback"
-
-# Final registerd models dir
-REGISTERED_MODELS_DIR = Path(ROOT_DIR, "src/app/model")
-
-# HuggingFace Repo
+# Constants for configuration file paths
+# This module defines constants for the configuration file paths used in the application.
+
+import os
+from pathlib import Path
+
+# Get current working directory
+ROOT_DIR = os.getcwd()
+
+# configuration file path
+CONFIG_DIR = Path(ROOT_DIR, "configs")
+CONFIG_FILE = Path(CONFIG_DIR, "config.yaml")
+
+# Params file path
+PARAMS_FILE = Path(CONFIG_DIR, "params.yaml")
+
+# Database Constants
+DATABASE_NAME = "Tweets_DB"
+TRAINING_COLLECTION_NAME = "tweets_collection"
+PRODUCTION_COLLECTION_NAME = "production_tweets"
+FEEDBACK_COLLECTION_NAME = "prediction_feedback"
+
+# Final registerd models dir
+REGISTERED_MODELS_DIR = Path(ROOT_DIR, "src/app/model")
+
+# HuggingFace Repo
 HUGGINGFACE_REPO_ID = "Subi003/ToxicTweet-Tagger"
\ No newline at end of file
diff --git a/core/exception.py b/core/exception.py
index b4c9daf08bd634c463babdd433dfbfe79b047f1f..11780d82a8498cf4d6684c6a778dbdfb69d1e60a 100644
--- a/core/exception.py
+++ b/core/exception.py
@@ -1,63 +1,63 @@
-# Custom exception class for handling errors in the project.
-# It provides a detailed error message including the file name, line number, and error message.
-
-import sys
-from types import ModuleType
-
-# static method
-def error_message_details(error, error_details: ModuleType = sys) -> str:
-    """
-    This function returns the error message with the information of the file name, line number, 
-    and error message.
-
-    Parameters:
-    error (Exception): The exception object
-    error_details (sys): The sys module with the error information
-
-    Returns:
-    str: The error message with the information of the file name, line number, and error message
-    """
-    if isinstance(error, Exception):
-        _, _, exc_tb = error_details.exc_info()
-        # extracting file name from exception traceback
-        if exc_tb is not None:
-            file_name = exc_tb.tb_frame.f_code.co_filename
-            line_number = exc_tb.tb_lineno
-        else:
-            file_name = "Unknown"
-            line_number = "Unknown"
-
-        error_message = "Error occurred in python script name [{0}] line number [{1}] error message [{2}]".format(
-            file_name, line_number, str(error)
-        )
-    else:
-        error_message = str(error)
-    
-    return error_message
-
-  
-class AppException(Exception):
-    def __init__(self, error_message: Exception, error_details: ModuleType = sys) -> None:
-        super().__init__(error_message)
-        """
-        Initializes a CustomException instance with a detailed error message.
-
-        Parameters:
-        error_message (str): The error message string.
-        error_details (sys): The sys module containing error information.
-
-        This constructor calls the error_message_details function to generate
-        a detailed error message, including the file name, line number, and
-        error message, and assigns it to the error_message attribute.
-        """
-        self.error_message= error_message_details(error_message, error_details = error_details)
-        
-    def __str__(self) -> str:
-        """
-        This method returns the detailed error message stored in the error_message attribute.
-        The error message includes the file name, line number, and error message.
-
-        Returns: str: The detailed error message
-        """
-
+# Custom exception class for handling errors in the project.
+# It provides a detailed error message including the file name, line number, and error message.
+
+import sys
+from types import ModuleType
+
+# static method
+def error_message_details(error, error_details: ModuleType = sys) -> str:
+    """
+    This function returns the error message with the information of the file name, line number, 
+    and error message.
+
+    Parameters:
+    error (Exception): The exception object
+    error_details (sys): The sys module with the error information
+
+    Returns:
+    str: The error message with the information of the file name, line number, and error message
+    """
+    if isinstance(error, Exception):
+        _, _, exc_tb = error_details.exc_info()
+        # extracting file name from exception traceback
+        if exc_tb is not None:
+            file_name = exc_tb.tb_frame.f_code.co_filename
+            line_number = exc_tb.tb_lineno
+        else:
+            file_name = "Unknown"
+            line_number = "Unknown"
+
+        error_message = "Error occurred in python script name [{0}] line number [{1}] error message [{2}]".format(
+            file_name, line_number, str(error)
+        )
+    else:
+        error_message = str(error)
+    
+    return error_message
+
+  
+class AppException(Exception):
+    def __init__(self, error_message: Exception, error_details: ModuleType = sys) -> None:
+        super().__init__(error_message)
+        """
+        Initializes a CustomException instance with a detailed error message.
+
+        Parameters:
+        error_message (str): The error message string.
+        error_details (sys): The sys module containing error information.
+
+        This constructor calls the error_message_details function to generate
+        a detailed error message, including the file name, line number, and
+        error message, and assigns it to the error_message attribute.
+        """
+        self.error_message= error_message_details(error_message, error_details = error_details)
+        
+    def __str__(self) -> str:
+        """
+        This method returns the detailed error message stored in the error_message attribute.
+        The error message includes the file name, line number, and error message.
+
+        Returns: str: The detailed error message
+        """
+
         return self.error_message
\ No newline at end of file
diff --git a/core/logger.py b/core/logger.py
index 0635f2b86b74534041b87473b71f59a431c8643a..c4ec1c10961f16645c2584119ada674021cdceaf 100644
--- a/core/logger.py
+++ b/core/logger.py
@@ -1,32 +1,32 @@
-# This script sets up the logging configuration for the project.
-# Creates a log file with a timestamp in the name and sets up logging to both a file and the console.
-
-import os
-import sys
-import logging
-from datetime import datetime
-from dotenv import load_dotenv
-load_dotenv()
-
-LOG_LEVEL = logging.INFO
-ENV = os.getenv("ENV")
-
-log_handlers: list[logging.Handler] = [logging.StreamHandler(sys.stdout)]               # Typing fixes for mypy
-
-if ENV == "DEV":
-    LOG_FILE = f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log"
-    logs_path = os.path.join(os.getcwd(), "logs")
-
-    # Create a logs directory if it doesn't exist
-    os.makedirs(logs_path, exist_ok = True)
-
-    LOG_FILE_PATH = os.path.join(logs_path, LOG_FILE)
-
-    log_handlers.append(logging.FileHandler(LOG_FILE_PATH))
-
-# logging configuration
-logging.basicConfig(
-    format = "[ %(asctime)s ]: %(levelname)s - %(lineno)d %(module)s - %(message)s",
-    level = logging.INFO,
-    handlers = log_handlers
-)
+# This script sets up the logging configuration for the project.
+# Creates a log file with a timestamp in the name and sets up logging to both a file and the console.
+
+import os
+import sys
+import logging
+from datetime import datetime
+from dotenv import load_dotenv
+load_dotenv()
+
+LOG_LEVEL = logging.INFO
+ENV = os.getenv("ENV")
+
+log_handlers: list[logging.Handler] = [logging.StreamHandler(sys.stdout)]               # Typing fixes for mypy
+
+if ENV == "DEV":
+    LOG_FILE = f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log"
+    logs_path = os.path.join(os.getcwd(), "logs")
+
+    # Create a logs directory if it doesn't exist
+    os.makedirs(logs_path, exist_ok = True)
+
+    LOG_FILE_PATH = os.path.join(logs_path, LOG_FILE)
+
+    log_handlers.append(logging.FileHandler(LOG_FILE_PATH))
+
+# logging configuration
+logging.basicConfig(
+    format = "[ %(asctime)s ]: %(levelname)s - %(lineno)d %(module)s - %(message)s",
+    level = logging.INFO,
+    handlers = log_handlers
+)
diff --git a/core/mongo_client.py b/core/mongo_client.py
index a6ec365e953e0a465e0df4cefa058eb86fbef04a..60d2ab7da39a732b6af69612474646445bad7a3f 100644
--- a/core/mongo_client.py
+++ b/core/mongo_client.py
@@ -1,93 +1,93 @@
-import sys, os
-import pymongo
-import pandas as pd
-from urllib.parse import quote_plus
-from src.core.logger import logging
-from src.core.exception import AppException
-from dotenv import load_dotenv
-load_dotenv()
-
-mongo_username = os.getenv("MONGO_USERNAME")
-mongo_password = os.getenv("MONGO_PASSWORD")
-url = os.getenv("MONGO_URL")
-
-class MongoDBClient:
-    def __init__(self):
-        """
-        Initializes the MongoDBClient object by connecting to the MongoDB instance.
-        This method connects to the MongoDB instance using the credentials stored in environment variables.
-        """
-        try:
-            username = quote_plus(str(mongo_username).strip())
-            password = quote_plus(str(mongo_password).strip())
-
-            mongo_connection_url = f"mongodb+srv://{username}:{password}@{url}"
-
-            self.client = pymongo.MongoClient(mongo_connection_url)
-            logging.info("MongoDB connection successfull")
-        
-        except Exception as e:
-            logging.error(f"Failed to inititalize MongoDBClient -connection failed: {e}", exc_info=True)
-            raise AppException(e, sys)
-    
-
-    def get_all_docs(self, collection_name:str, database_name:str) -> pd.DataFrame:
-        """
-        Fetches data from the specified MongoDB collection and database.
-
-        Args:
-            collection_name (str): The name of the MongoDB collection.
-            database_name (str): The name of the MongoDB database.
-
-        Returns:
-            pd.DataFrame: A pandas DataFrame containing the downloaded data.
-        """
-        try:
-            database = self.client[database_name]
-            collection = database[collection_name]
-
-            df = pd.DataFrame(list(collection.find()))
-            if "_id" in df.columns:
-                df.drop(columns="_id", inplace=True)
-            logging.info("Data downloaded successfully")
-            return df
-        
-        except Exception as e:
-            logging.error(f"Failed to fetch data from MongoDB: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-
-    def insert_docs(self, collection_name:str, database_name:str, docs:list):
-        """
-        Inserts data into the specified MongoDB collection and database.
-
-        Args:
-            collection_name (str): The name of the MongoDB collection.
-            database_name (str): The name of the MongoDB database.
-            data (list): A list of dictionaries containing the data to be inserted.
-
-        Raises:
-            AppException: If an error occurs while inserting data into MongoDB.
-        """
-        try:
-            database = self.client[database_name]
-            collection = database[collection_name]
-
-            collection.insert_many(docs, ordered=False)
-            logging.info("Data inserted successfully")
-        
-        except Exception as e:
-            logging.warning(f"Failed to insert docs into MongoDB collection: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-
-    def close_connection(self):
-        """
-        Closes the MongoDB connection.
-        """
-        try:
-            self.client.close()
-            logging.info("MongoDB connection closed successfully")
-
-        except Exception as e:
+import sys, os
+import pymongo
+import pandas as pd
+from urllib.parse import quote_plus
+from src.core.logger import logging
+from src.core.exception import AppException
+from dotenv import load_dotenv
+load_dotenv()
+
+mongo_username = os.getenv("MONGO_USERNAME")
+mongo_password = os.getenv("MONGO_PASSWORD")
+url = os.getenv("MONGO_URL")
+
+class MongoDBClient:
+    def __init__(self):
+        """
+        Initializes the MongoDBClient object by connecting to the MongoDB instance.
+        This method connects to the MongoDB instance using the credentials stored in environment variables.
+        """
+        try:
+            username = quote_plus(str(mongo_username).strip())
+            password = quote_plus(str(mongo_password).strip())
+
+            mongo_connection_url = f"mongodb+srv://{username}:{password}@{url}"
+
+            self.client = pymongo.MongoClient(mongo_connection_url)
+            logging.info("MongoDB connection successfull")
+        
+        except Exception as e:
+            logging.error(f"Failed to inititalize MongoDBClient -connection failed: {e}", exc_info=True)
+            raise AppException(e, sys)
+    
+
+    def get_all_docs(self, collection_name:str, database_name:str) -> pd.DataFrame:
+        """
+        Fetches data from the specified MongoDB collection and database.
+
+        Args:
+            collection_name (str): The name of the MongoDB collection.
+            database_name (str): The name of the MongoDB database.
+
+        Returns:
+            pd.DataFrame: A pandas DataFrame containing the downloaded data.
+        """
+        try:
+            database = self.client[database_name]
+            collection = database[collection_name]
+
+            df = pd.DataFrame(list(collection.find()))
+            if "_id" in df.columns:
+                df.drop(columns="_id", inplace=True)
+            logging.info("Data downloaded successfully")
+            return df
+        
+        except Exception as e:
+            logging.error(f"Failed to fetch data from MongoDB: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+
+    def insert_docs(self, collection_name:str, database_name:str, docs:list):
+        """
+        Inserts data into the specified MongoDB collection and database.
+
+        Args:
+            collection_name (str): The name of the MongoDB collection.
+            database_name (str): The name of the MongoDB database.
+            data (list): A list of dictionaries containing the data to be inserted.
+
+        Raises:
+            AppException: If an error occurs while inserting data into MongoDB.
+        """
+        try:
+            database = self.client[database_name]
+            collection = database[collection_name]
+
+            collection.insert_many(docs, ordered=False)
+            logging.info("Data inserted successfully")
+        
+        except Exception as e:
+            logging.warning(f"Failed to insert docs into MongoDB collection: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+
+    def close_connection(self):
+        """
+        Closes the MongoDB connection.
+        """
+        try:
+            self.client.close()
+            logging.info("MongoDB connection closed successfully")
+
+        except Exception as e:
             logging.warning(f"Failed to close MongoDB connection: {e}", exc_info=True)
\ No newline at end of file
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diff --git a/operations/__pycache__/serve_model.cpython-311.pyc b/operations/__pycache__/serve_model.cpython-311.pyc
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diff --git a/operations/load_model.py b/operations/load_model.py
index dfaf75b83aa72224397dbd01d750845a53e29c3c..4c32322c7fafd582e5cd4bde40aa14bb589e38d3 100644
--- a/operations/load_model.py
+++ b/operations/load_model.py
@@ -1,59 +1,59 @@
-# This module is responsible for loading the production model artifacts from MLflow registry and packages in the app folder for serving.
-
-import os, sys
-import mlflow
-import dagshub
-from mlflow import artifacts
-from src.core.logger import logging
-from src.core.exception import AppException
-from dotenv import load_dotenv
-load_dotenv()
-
-# get environment variables
-uri = os.getenv("MLFLOW_URI")
-dagshub_token = os.getenv("DAGSHUB_TOKEN")
-dagshub_username = os.getenv("OWNER")
-
-if not dagshub_token or not dagshub_username:
-    raise EnvironmentError("Dagshub environment variables is not set")
-
-os.environ["MLFLOW_TRACKING_USERNAME"] = dagshub_username
-os.environ["MLFLOW_TRACKING_PASSWORD"] = dagshub_token
-
-mlflow.set_tracking_uri(uri)        # type: ignore
-
-# For local use
-# ==============================================================================
-# repo_owner = os.getenv("OWNER")
-# repo_name = os.getenv("REPO")
-# 
-# mlflow.set_tracking_uri(uri) 
-# if repo_owner is None:
-# 	raise EnvironmentError("Missing dagshub logging environment credentials.")
-# dagshub.init(repo_owner=repo_owner, repo_name=repo_name, mlflow=True)
-# ===============================================================================
-
-def load_model():
-    """
-    Download the production model artifacts from MLflow model registry and to the app folder.
-
-    Raises:
-        AppException: If there is an error during the model loading process.
-        EnvironmentError: If the Dagshub Token environment variable is not set.
-    """
-    try:
-        model_name = "ToxicTagger-Models"
-        stage = "Production"
-        model_uri = f"models:/{model_name}/{stage}"
-
-        logging.info(f"Downloading model artifacts from MLflow model registry")
-        artifacts.download_artifacts(artifact_uri=model_uri, dst_path="src/app/model")
-        logging.info("Successfully downloaded model artifacts!")
-
-    except Exception as e:
-        logging.error(f"Model loading operation failed: {e}", exc_info=True)
-        raise AppException(e, sys)
-    
-
-if __name__ == "__main__":
+# This module is responsible for loading the production model artifacts from MLflow registry and packages in the app folder for serving.
+
+import os, sys
+import mlflow
+import dagshub
+from mlflow import artifacts
+from src.core.logger import logging
+from src.core.exception import AppException
+from dotenv import load_dotenv
+load_dotenv()
+
+# get environment variables
+uri = os.getenv("MLFLOW_URI")
+dagshub_token = os.getenv("DAGSHUB_TOKEN")
+dagshub_username = os.getenv("OWNER")
+
+if not dagshub_token or not dagshub_username:
+    raise EnvironmentError("Dagshub environment variables is not set")
+
+os.environ["MLFLOW_TRACKING_USERNAME"] = dagshub_username
+os.environ["MLFLOW_TRACKING_PASSWORD"] = dagshub_token
+
+mlflow.set_tracking_uri(uri)        # type: ignore
+
+# For local use
+# ==============================================================================
+# repo_owner = os.getenv("OWNER")
+# repo_name = os.getenv("REPO")
+# 
+# mlflow.set_tracking_uri(uri) 
+# if repo_owner is None:
+# 	raise EnvironmentError("Missing dagshub logging environment credentials.")
+# dagshub.init(repo_owner=repo_owner, repo_name=repo_name, mlflow=True)
+# ===============================================================================
+
+def load_model():
+    """
+    Download the production model artifacts from MLflow model registry and to the app folder.
+
+    Raises:
+        AppException: If there is an error during the model loading process.
+        EnvironmentError: If the Dagshub Token environment variable is not set.
+    """
+    try:
+        model_name = "ToxicTagger-Models"
+        stage = "Production"
+        model_uri = f"models:/{model_name}/{stage}"
+
+        logging.info(f"Downloading model artifacts from MLflow model registry")
+        artifacts.download_artifacts(artifact_uri=model_uri, dst_path="src/app/model")
+        logging.info("Successfully downloaded model artifacts!")
+
+    except Exception as e:
+        logging.error(f"Model loading operation failed: {e}", exc_info=True)
+        raise AppException(e, sys)
+    
+
+if __name__ == "__main__":
     load_model()
\ No newline at end of file
diff --git a/operations/promote_model.py b/operations/promote_model.py
index 3ebf44f2c389b717559eed7c61bd0a8602d12348..abb2ddba98b91d31d1962e8ebba75da69d0c3b1b 100644
--- a/operations/promote_model.py
+++ b/operations/promote_model.py
@@ -1,73 +1,73 @@
-# This script promotes model from staging to production in MLflow model registry.
-import os, sys
-import mlflow
-from src.core.logger import logging
-from src.core.exception import AppException
-from dotenv import load_dotenv
-load_dotenv()
-
-
-def promote_model():
-    """
-    Promotes a model from staging to production in MLflow.
-
-    This function sets up MLflow tracking URI and authentication using environment variables,
-    retrieves the latest version of the model in the Staging stage, archives any existing
-    Production model versions, and transitions the selected model version to the Production stage.
-    Raises:
-        AppException: If there is an error during the promotion process.
-        EnvironmentError: If the Dagshub Token environment variable is not set.
-    """
-    try:
-        # Set up MLflow tracking URI and authentication
-        uri = os.getenv("MLFLOW_URI")
-        dagshub_token = os.getenv("DAGSHUB_TOKEN")
-        dagshub_username = os.getenv("OWNER")
-        if not dagshub_token or not dagshub_username:
-            raise EnvironmentError("Dagshub environment variables is not set")
-
-        os.environ["MLFLOW_TRACKING_USERNAME"] = dagshub_username
-        os.environ["MLFLOW_TRACKING_PASSWORD"] = dagshub_token
-
-        mlflow.set_tracking_uri(uri)        # type: ignore
-
-        # For local use
-        # =================================================================================
-        # repo_owner = os.getenv("OWNER")
-        # repo_name = os.getenv("REPO")
-        # 
-        # mlflow.set_tracking_uri(uri)
-        # if repo_owner is None:
-        # 	raise EnvironmentError("Missing dagshub logging environment credentials.")
-        # dagshub.init(repo_owner=repo_owner, repo_name=repo_name, mlflow=True)    
-        # ==================================================================================
-
-        client = mlflow.MlflowClient()
-
-        model_name = "ToxicTagger-Models"
-        latest_version_staging = client.get_latest_versions(name=model_name, stages=["Staging"])
-        model_version_staging = latest_version_staging[0].version
-
-        logging.info("Archiving existing production model versions")
-        prod_versions = client.get_latest_versions(name=model_name, stages=["Production"])
-        for version in prod_versions:
-            client.transition_model_version_stage(
-                name=model_name,
-                version=version.version,
-                stage="Archived"
-            )
-        logging.info("Transitioning model version from Staging to Production")
-        client.transition_model_version_stage(
-            name=model_name,
-            version=model_version_staging,
-            stage="Production"
-        )
-        logging.info(f"Model version {model_version_staging} successfully promoted to production stage")
-
-    except Exception as e:
-        logging.error(f"Failed promoting model to production in mlflow model registry: {e}", exc_info=True)
-        raise AppException(e, sys)
-
-
-if __name__ == "__main__":
+# This script promotes model from staging to production in MLflow model registry.
+import os, sys
+import mlflow
+from src.core.logger import logging
+from src.core.exception import AppException
+from dotenv import load_dotenv
+load_dotenv()
+
+
+def promote_model():
+    """
+    Promotes a model from staging to production in MLflow.
+
+    This function sets up MLflow tracking URI and authentication using environment variables,
+    retrieves the latest version of the model in the Staging stage, archives any existing
+    Production model versions, and transitions the selected model version to the Production stage.
+    Raises:
+        AppException: If there is an error during the promotion process.
+        EnvironmentError: If the Dagshub Token environment variable is not set.
+    """
+    try:
+        # Set up MLflow tracking URI and authentication
+        uri = os.getenv("MLFLOW_URI")
+        dagshub_token = os.getenv("DAGSHUB_TOKEN")
+        dagshub_username = os.getenv("OWNER")
+        if not dagshub_token or not dagshub_username:
+            raise EnvironmentError("Dagshub environment variables is not set")
+
+        os.environ["MLFLOW_TRACKING_USERNAME"] = dagshub_username
+        os.environ["MLFLOW_TRACKING_PASSWORD"] = dagshub_token
+
+        mlflow.set_tracking_uri(uri)        # type: ignore
+
+        # For local use
+        # =================================================================================
+        # repo_owner = os.getenv("OWNER")
+        # repo_name = os.getenv("REPO")
+        # 
+        # mlflow.set_tracking_uri(uri)
+        # if repo_owner is None:
+        # 	raise EnvironmentError("Missing dagshub logging environment credentials.")
+        # dagshub.init(repo_owner=repo_owner, repo_name=repo_name, mlflow=True)    
+        # ==================================================================================
+
+        client = mlflow.MlflowClient()
+
+        model_name = "ToxicTagger-Models"
+        latest_version_staging = client.get_latest_versions(name=model_name, stages=["Staging"])
+        model_version_staging = latest_version_staging[0].version
+
+        logging.info("Archiving existing production model versions")
+        prod_versions = client.get_latest_versions(name=model_name, stages=["Production"])
+        for version in prod_versions:
+            client.transition_model_version_stage(
+                name=model_name,
+                version=version.version,
+                stage="Archived"
+            )
+        logging.info("Transitioning model version from Staging to Production")
+        client.transition_model_version_stage(
+            name=model_name,
+            version=model_version_staging,
+            stage="Production"
+        )
+        logging.info(f"Model version {model_version_staging} successfully promoted to production stage")
+
+    except Exception as e:
+        logging.error(f"Failed promoting model to production in mlflow model registry: {e}", exc_info=True)
+        raise AppException(e, sys)
+
+
+if __name__ == "__main__":
     promote_model()
\ No newline at end of file
diff --git a/operations/register_model.py b/operations/register_model.py
index 86ff05274605e89e889fb256179999c0f58ee878..f4da0c5cd17f35560ee4cec4562598a35cb1f693 100644
--- a/operations/register_model.py
+++ b/operations/register_model.py
@@ -1,131 +1,131 @@
-# Register model in the Mlflow model registry and transition it to the "Staging" stage.
-import sys, os
-import json
-import mlflow
-import dagshub
-from pathlib import Path
-from src.core.logger import logging
-from src.core.exception import AppException
-from src.core.configuration import AppConfiguration
-from dotenv import load_dotenv
-load_dotenv()
-
-# get environment variables
-uri = os.getenv("MLFLOW_URI")
-dagshub_token = os.getenv("DAGSHUB_TOKEN")
-dagshub_username = os.getenv("OWNER")
-if not dagshub_token or not dagshub_username:
-    raise EnvironmentError("Dagshub environment variables is not set")
-
-os.environ["MLFLOW_TRACKING_USERNAME"] = dagshub_username
-os.environ["MLFLOW_TRACKING_PASSWORD"] = dagshub_token
-
-mlflow.set_tracking_uri(uri)         # type: ignore
-
-# For local use
-# ==================================================================================
-# repo_owner = os.getenv("OWNER")
-# repo_name = os.getenv("REPO")
-# mlflow.set_tracking_uri(uri)
-# 
-# if repo_owner is None:
-# 	raise EnvironmentError("Missing dagshub logging environment credentials.")
-# dagshub.init(repo_owner=repo_owner, repo_name=repo_name, mlflow=True) 
-# ===================================================================================
-
-
-class ModelRegistration:
-    def __init__(self, config = AppConfiguration()):
-        """
-        Initializes the DataPreprocessing object by creating a data preprocessing configuration.
-        Raises:
-            AppException: If error occurs during creation of data preprocessing configuration
-        """
-        try:
-            self.registration_config = config.model_registration_config()
-
-        except Exception as e:
-            logging.error(f"Failed to create model registration configuration: {e}", exc_info=True)
-            raise AppException(e, sys)
-
-
-    def load_exp_info(self, exp_info_file: Path):
-        """
-        Loads the experiment information from the saved JSON file.
-        Args:
-            exp_info_file (Path): The file path to the 'experiment_info.json' file.
-        Returns:
-            dict: A dictionary containing the experiment information if the file is found.
-        """
-        try:
-            if not os.path.exists(exp_info_file):
-                raise FileNotFoundError("'experiment_info.json' file not found")
-            with open(exp_info_file, 'r') as file:
-                exp_info = json.load(file)
-                return exp_info
-            
-        except Exception as e:
-            logging.error(f"Failed to get model experiment information: {e}", exc_info=True)
-            raise AppException(e, sys)
-        
-        
-    def register(self, experiment_info: dict, register_modelname: str):
-        """
-        Registers a model in Mlflow using the provided experiment information and model name.
-        Transitions the registered model version to the "Staging" stage.
-
-        Args:
-            experiment_info (dict): dictionary containing experiment details:
-                                    'run_id' and 'model_name'.
-            register_modelname (str): name under which the model to be registered in Mlflow..
-        """
-        try:
-            model_uri = f"runs:/{experiment_info['run_id']}/{experiment_info['model']}"
-
-            logging.info("Registering model in Mlflow")
-            model_version = mlflow.register_model(model_uri, register_modelname)
-
-            # Transition the model to "Staging" stage
-            client = mlflow.MlflowClient()
-            client.transition_model_version_stage(
-                name = register_modelname,
-                version = model_version.version,
-                stage = "Staging"
-            )
-            logging.info(f"{register_modelname} - version : {model_version.version} registered and transitioned to Staging")
-        
-        except Exception as e:
-            logging.error(f"Error during model registration in Mlflow: {e}", exc_info=True)
-            raise AppException(e, sys)
-
-
-def register_model():
-    """
-    Main function to handle the model registration process in Mlflow.
-    Initializes a RegisterModel object, loads experiment information from a JSON file,
-    and registers the model in Mlflow. The registered model is transitioned to the "Staging" stage.
-
-    Raises:
-        AppException: If an error occurs during model registration.
-    """
-    obj = ModelRegistration()
-    try:
-        logging.info(f"{'='*20}Model Registration{'='*20}")
-        exp_info_filepath = obj.registration_config.experiment_info_filepath
-
-        experiment_info = obj.load_exp_info(exp_info_filepath)
-        register_modelname = "ToxicTagger-Models"
-
-        if type(experiment_info) is not dict:
-            logging.error("'register_model' function expects dict type object ")
-            return
-        obj.register(experiment_info, register_modelname)
-        logging.info(f"{'='*20}Model Registration in Mlflow Completed Successfully{'='*20} \n\n")
-
-    except Exception as e:
-        logging.error(f"Error during model registration in Mlflow: {e}", exc_info=True)
-        raise AppException(e, sys)
-    
-
-if __name__ == "__main__":
+# Register model in the Mlflow model registry and transition it to the "Staging" stage.
+import sys, os
+import json
+import mlflow
+import dagshub
+from pathlib import Path
+from src.core.logger import logging
+from src.core.exception import AppException
+from src.core.configuration import AppConfiguration
+from dotenv import load_dotenv
+load_dotenv()
+
+# get environment variables
+uri = os.getenv("MLFLOW_URI")
+dagshub_token = os.getenv("DAGSHUB_TOKEN")
+dagshub_username = os.getenv("OWNER")
+if not dagshub_token or not dagshub_username:
+    raise EnvironmentError("Dagshub environment variables is not set")
+
+os.environ["MLFLOW_TRACKING_USERNAME"] = dagshub_username
+os.environ["MLFLOW_TRACKING_PASSWORD"] = dagshub_token
+
+mlflow.set_tracking_uri(uri)         # type: ignore
+
+# For local use
+# ==================================================================================
+# repo_owner = os.getenv("OWNER")
+# repo_name = os.getenv("REPO")
+# mlflow.set_tracking_uri(uri)
+# 
+# if repo_owner is None:
+# 	raise EnvironmentError("Missing dagshub logging environment credentials.")
+# dagshub.init(repo_owner=repo_owner, repo_name=repo_name, mlflow=True) 
+# ===================================================================================
+
+
+class ModelRegistration:
+    def __init__(self, config = AppConfiguration()):
+        """
+        Initializes the DataPreprocessing object by creating a data preprocessing configuration.
+        Raises:
+            AppException: If error occurs during creation of data preprocessing configuration
+        """
+        try:
+            self.registration_config = config.model_registration_config()
+
+        except Exception as e:
+            logging.error(f"Failed to create model registration configuration: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+
+    def load_exp_info(self, exp_info_file: Path):
+        """
+        Loads the experiment information from the saved JSON file.
+        Args:
+            exp_info_file (Path): The file path to the 'experiment_info.json' file.
+        Returns:
+            dict: A dictionary containing the experiment information if the file is found.
+        """
+        try:
+            if not os.path.exists(exp_info_file):
+                raise FileNotFoundError("'experiment_info.json' file not found")
+            with open(exp_info_file, 'r') as file:
+                exp_info = json.load(file)
+                return exp_info
+            
+        except Exception as e:
+            logging.error(f"Failed to get model experiment information: {e}", exc_info=True)
+            raise AppException(e, sys)
+        
+        
+    def register(self, experiment_info: dict, register_modelname: str):
+        """
+        Registers a model in Mlflow using the provided experiment information and model name.
+        Transitions the registered model version to the "Staging" stage.
+
+        Args:
+            experiment_info (dict): dictionary containing experiment details:
+                                    'run_id' and 'model_name'.
+            register_modelname (str): name under which the model to be registered in Mlflow..
+        """
+        try:
+            model_uri = f"runs:/{experiment_info['run_id']}/{experiment_info['model']}"
+
+            logging.info("Registering model in Mlflow")
+            model_version = mlflow.register_model(model_uri, register_modelname)
+
+            # Transition the model to "Staging" stage
+            client = mlflow.MlflowClient()
+            client.transition_model_version_stage(
+                name = register_modelname,
+                version = model_version.version,
+                stage = "Staging"
+            )
+            logging.info(f"{register_modelname} - version : {model_version.version} registered and transitioned to Staging")
+        
+        except Exception as e:
+            logging.error(f"Error during model registration in Mlflow: {e}", exc_info=True)
+            raise AppException(e, sys)
+
+
+def register_model():
+    """
+    Main function to handle the model registration process in Mlflow.
+    Initializes a RegisterModel object, loads experiment information from a JSON file,
+    and registers the model in Mlflow. The registered model is transitioned to the "Staging" stage.
+
+    Raises:
+        AppException: If an error occurs during model registration.
+    """
+    obj = ModelRegistration()
+    try:
+        logging.info(f"{'='*20}Model Registration{'='*20}")
+        exp_info_filepath = obj.registration_config.experiment_info_filepath
+
+        experiment_info = obj.load_exp_info(exp_info_filepath)
+        register_modelname = "ToxicTagger-Models"
+
+        if type(experiment_info) is not dict:
+            logging.error("'register_model' function expects dict type object ")
+            return
+        obj.register(experiment_info, register_modelname)
+        logging.info(f"{'='*20}Model Registration in Mlflow Completed Successfully{'='*20} \n\n")
+
+    except Exception as e:
+        logging.error(f"Error during model registration in Mlflow: {e}", exc_info=True)
+        raise AppException(e, sys)
+    
+
+if __name__ == "__main__":
     register_model()
\ No newline at end of file
diff --git a/operations/serve_model.py b/operations/serve_model.py
index 448245a1901b94c202a056a3200e9478f127aa1a..67348caf89c3232ef23d35befc78ca66d1d91fe0 100644
--- a/operations/serve_model.py
+++ b/operations/serve_model.py
@@ -1,44 +1,44 @@
-# Uploads the production model along with the FastAPI backend API services to Huggingface Spaces for serving
-
-import os, sys
-from pathlib import Path
-from huggingface_hub import upload_folder
-from src.core.constants import HUGGINGFACE_REPO_ID
-from src.core.logger import logging
-from src.core.exception import AppException
-
-from dotenv import load_dotenv
-load_dotenv()
-
-hf_token = os.getenv("HUGGINGFACE_TOKEN")
-
-def serve_model():
-    """
-    Serves and deploys the model along with the API services to Huggingface Spaces.
-
-    Uploads the app folder containing the FastAPI backend API services and the production model artifacts to
-    Huggingface Spaces.
-    """
-    try:
-        if not hf_token:
-            raise EnvironmentError("Huggingface access token is not set")
-
-        logging.info(f"Deploying ML application to Huggingface Spaces")
-        
-        upload_folder(
-            folder_path = Path("src"),
-            repo_id = HUGGINGFACE_REPO_ID,
-            repo_type = "space",
-            token = hf_token,
-            delete_patterns = ["**"]
-        )
-        
-        logging.info("Successfully uploaded!")
-
-    except Exception as e:
-        logging.error(f"Deployment failed: {e}", exc_info=True)
-        raise AppException(e, sys)
-    
-
-if __name__ == "__main__":
+# Uploads the production model along with the FastAPI backend API services to Huggingface Spaces for serving
+
+import os, sys
+from pathlib import Path
+from huggingface_hub import upload_folder
+from src.core.constants import HUGGINGFACE_REPO_ID
+from src.core.logger import logging
+from src.core.exception import AppException
+
+from dotenv import load_dotenv
+load_dotenv()
+
+hf_token = os.getenv("HUGGINGFACE_TOKEN")
+
+def serve_model():
+    """
+    Serves and deploys the model along with the API services to Huggingface Spaces.
+
+    Uploads the app folder containing the FastAPI backend API services and the production model artifacts to
+    Huggingface Spaces.
+    """
+    try:
+        if not hf_token:
+            raise EnvironmentError("Huggingface access token is not set")
+
+        logging.info(f"Deploying ML application to Huggingface Spaces")
+        
+        upload_folder(
+            folder_path = Path("src"),
+            repo_id = HUGGINGFACE_REPO_ID,
+            repo_type = "space",
+            token = hf_token,
+            delete_patterns = ["**"]
+        )
+        
+        logging.info("Successfully uploaded!")
+
+    except Exception as e:
+        logging.error(f"Deployment failed: {e}", exc_info=True)
+        raise AppException(e, sys)
+    
+
+if __name__ == "__main__":
     serve_model()
\ No newline at end of file
diff --git a/pipeline/__pycache__/__init__.cpython-311.pyc b/pipeline/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fa06a051010e884e87821ea2dd33794b2834fd5f
Binary files /dev/null and b/pipeline/__pycache__/__init__.cpython-311.pyc differ
diff --git a/pipeline/__pycache__/ml_pipeline.cpython-311.pyc b/pipeline/__pycache__/ml_pipeline.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..62216d631f8d8d15d9158ecdac4f1fd6667cc546
Binary files /dev/null and b/pipeline/__pycache__/ml_pipeline.cpython-311.pyc differ
diff --git a/pipeline/ml_pipeline.py b/pipeline/ml_pipeline.py
index a1e1724f75fb92b2900720a8e58d2c713b025bf0..1d7548ee7299bbf7b123afc39df6446d43552ddd 100644
--- a/pipeline/ml_pipeline.py
+++ b/pipeline/ml_pipeline.py
@@ -1,43 +1,43 @@
-# Core ML Pipeline
-import sys
-from ..core.logger import logging
-from ..core.exception import AppException
-from ..components.data_ingestion import initiate_data_ingestion
-from ..components.data_preprocessing import initiate_data_preprocessing
-from ..components.feature_engineering import initiate_feature_engineering
-from ..components.model_training import initiate_model_training
-from ..components.model_evaluation import initiate_model_evaluation
-
-def run_ml_pipeline():
-    """
-    This function is the entry point for the Machine Learning pipeline.
-    It starts all the stages of the pipeline one by one and logs the status of each stage.
-    If any error occurs in any stage, it logs the error and raises an AppException with 
-    the error message.
-    If all stages are completed successfully, it logs a success message.
-    """
-    try:
-        logging.info("STAGE:1 Data Ingestion Stage Initiated")
-        initiate_data_ingestion()
-
-        logging.info("STAGE:2 Data Preprocessing Stage Initiated")
-        initiate_data_preprocessing()
-
-        logging.info("STAGE:3 Feature Engineering Stage Initiated")
-        initiate_feature_engineering()
-
-        logging.info("STAGE:4 Model Training Stage Initiated")
-        initiate_model_training()
-
-        logging.info("STAGE:5 Model Evaluation Stage Initiated")
-        initiate_model_evaluation()
-
-        logging.info("ML Pipeline Completed")
-
-    except Exception as e:
-        logging.error(f"ML Pipeline Terminated: {e}", exc_info=True)
-        raise AppException(e, sys)
-    
-# entry point for running the Pipeline
-if __name__ == "__main__":
+# Core ML Pipeline
+import sys
+from ..core.logger import logging
+from ..core.exception import AppException
+from ..components.data_ingestion import initiate_data_ingestion
+from ..components.data_preprocessing import initiate_data_preprocessing
+from ..components.feature_engineering import initiate_feature_engineering
+from ..components.model_training import initiate_model_training
+from ..components.model_evaluation import initiate_model_evaluation
+
+def run_ml_pipeline():
+    """
+    This function is the entry point for the Machine Learning pipeline.
+    It starts all the stages of the pipeline one by one and logs the status of each stage.
+    If any error occurs in any stage, it logs the error and raises an AppException with 
+    the error message.
+    If all stages are completed successfully, it logs a success message.
+    """
+    try:
+        logging.info("STAGE:1 Data Ingestion Stage Initiated")
+        initiate_data_ingestion()
+
+        logging.info("STAGE:2 Data Preprocessing Stage Initiated")
+        initiate_data_preprocessing()
+
+        logging.info("STAGE:3 Feature Engineering Stage Initiated")
+        initiate_feature_engineering()
+
+        logging.info("STAGE:4 Model Training Stage Initiated")
+        initiate_model_training()
+
+        logging.info("STAGE:5 Model Evaluation Stage Initiated")
+        initiate_model_evaluation()
+
+        logging.info("ML Pipeline Completed")
+
+    except Exception as e:
+        logging.error(f"ML Pipeline Terminated: {e}", exc_info=True)
+        raise AppException(e, sys)
+    
+# entry point for running the Pipeline
+if __name__ == "__main__":
     run_ml_pipeline()
\ No newline at end of file
diff --git a/prometheus.yaml b/prometheus.yaml
index 8d9c738557e1999f0ddc4556934c7fee1f6f43d8..c6588f3630059e65867d4b299f6c66cb8a393fd6 100644
--- a/prometheus.yaml
+++ b/prometheus.yaml
@@ -1,14 +1,14 @@
-global:
-  scrape_interval: 60s
-
-remote_write:
-  - url: "${GRAFANA_REMOTE_WRITE_URL}"
-    basic_auth:
-      username: "${GRAFANA_USERNAME}"
-      password: "${GRAFANA_PASSWORD}"
-
-scrape_configs:
-  - job_name: node
-    static_configs:
-      - targets: ["localhost:7860"]
-    metrics_path: /api/metrics
+global:
+  scrape_interval: 60s
+
+remote_write:
+  - url: "${GRAFANA_REMOTE_WRITE_URL}"
+    basic_auth:
+      username: "${GRAFANA_USERNAME}"
+      password: "${GRAFANA_PASSWORD}"
+
+scrape_configs:
+  - job_name: node
+    static_configs:
+      - targets: ["localhost:7860"]
+    metrics_path: /api/metrics
diff --git a/script/__pycache__/__init__.cpython-311.pyc b/script/__pycache__/__init__.cpython-311.pyc
index bd4e40ab1f4c601cb5243c15ed049f420c544603..87802ae6dfb07b2c6b6bf7db105c34cb9e39277e 100644
Binary files a/script/__pycache__/__init__.cpython-311.pyc and b/script/__pycache__/__init__.cpython-311.pyc differ
diff --git a/script/__pycache__/model_wrapper.cpython-311.pyc b/script/__pycache__/model_wrapper.cpython-311.pyc
index 4a07d10d27b216f309e254a9ca4b51e7901b0d9f..9180893b2be0932a629c3e23a447a117abd03665 100644
Binary files a/script/__pycache__/model_wrapper.cpython-311.pyc and b/script/__pycache__/model_wrapper.cpython-311.pyc differ
diff --git a/script/model_wrapper.py b/script/model_wrapper.py
index 014d9ca27c65aeb4484409993649911229696bc3..fb16fdefd8f080bae5e890ff6a11c5273112343e 100644
--- a/script/model_wrapper.py
+++ b/script/model_wrapper.py
@@ -1,46 +1,46 @@
-# This script wraps the ML model and the vectorizer into a single Python model object and 
-# prepares it for registeration in MLflow. 
-# This implementation is deprecated and is not used in the current version of the API (V2)
-
-import pandas as pd
-from mlflow.pyfunc.model import PythonModel
-
-# Custom model class to wrap the ML model and vectorizer
-class CustomModel(PythonModel):
-    def __init__(self, model, vectorizer):
-        """
-        Initializes the CustomModel instance with a machine learning model and a vectorizer.
-
-        Args:
-            model: The machine learning model to be used for prediction.
-            vectorizer: The vectorizer to transform input data for the model.
-        """
-        self.model = model
-        self.vectorizer = vectorizer
-
-    def predict(self, context, model_input: pd.DataFrame):
-        
-        """
-        Predicts the class probability scores and class labels for the given input data.
-
-        Args:
-            context (dict): Context containing additional information that may be useful for prediction.
-            model_input (pd.DataFrame): Input data containing the text column.
-        
-        Returns:
-            dict: A dictionary containing the class probability scores and class labels.
-        """
-        texts = model_input["text"]
-        if self.vectorizer is not None and self.model is not None:
-            X = self.vectorizer.transform(texts)
-            class_label = self.model.predict(X)
-
-        return class_label
-    
-    def predict_proba(self, context, model_input: pd.DataFrame):        
-        text = model_input["text"]
-        if self.vectorizer is not None and self.model is not None:
-            X = self.vectorizer.transform(text)
-            class_proba = self.model.predict_proba(X)
-        
+# This script wraps the ML model and the vectorizer into a single Python model object and 
+# prepares it for registeration in MLflow. 
+# This implementation is deprecated and is not used in the current version of the API (V2)
+
+import pandas as pd
+from mlflow.pyfunc.model import PythonModel
+
+# Custom model class to wrap the ML model and vectorizer
+class CustomModel(PythonModel):
+    def __init__(self, model, vectorizer):
+        """
+        Initializes the CustomModel instance with a machine learning model and a vectorizer.
+
+        Args:
+            model: The machine learning model to be used for prediction.
+            vectorizer: The vectorizer to transform input data for the model.
+        """
+        self.model = model
+        self.vectorizer = vectorizer
+
+    def predict(self, context, model_input: pd.DataFrame):
+        
+        """
+        Predicts the class probability scores and class labels for the given input data.
+
+        Args:
+            context (dict): Context containing additional information that may be useful for prediction.
+            model_input (pd.DataFrame): Input data containing the text column.
+        
+        Returns:
+            dict: A dictionary containing the class probability scores and class labels.
+        """
+        texts = model_input["text"]
+        if self.vectorizer is not None and self.model is not None:
+            X = self.vectorizer.transform(texts)
+            class_label = self.model.predict(X)
+
+        return class_label
+    
+    def predict_proba(self, context, model_input: pd.DataFrame):        
+        text = model_input["text"]
+        if self.vectorizer is not None and self.model is not None:
+            X = self.vectorizer.transform(text)
+            class_proba = self.model.predict_proba(X)
+        
         return class_proba
\ No newline at end of file
diff --git a/toxictweet_tagger.egg-info/PKG-INFO b/toxictweet_tagger.egg-info/PKG-INFO
deleted file mode 100644
index 064e2324580b150058609622c3584bcaf6c02ae2..0000000000000000000000000000000000000000
--- a/toxictweet_tagger.egg-info/PKG-INFO
+++ /dev/null
@@ -1,401 +0,0 @@
-Metadata-Version: 2.4
-Name: toxictweet-tagger
-Version: 2.0.0
-Summary: End-to-End Hate-Tweet Detection Application.
-Author-email: Subinoy Bera <subinoyberadgp@gmail.com>
-License:                                  Apache License
-                                   Version 2.0, January 2004
-                                http://www.apache.org/licenses/
-        
-        
-           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-        
-           1. Definitions.
-        
-              "License" shall mean the terms and conditions for use, reproduction,
-              and distribution as defined by Sections 1 through 9 of this document.
-        
-              "Licensor" shall mean the copyright owner or entity authorized by
-              the copyright owner that is granting the License.
-        
-              "Legal Entity" shall mean the union of the acting entity and all
-              other entities that control, are controlled by, or are under common
-              control with that entity. For the purposes of this definition,
-              "control" means (i) the power, direct or indirect, to cause the
-              direction or management of such entity, whether by contract or
-              otherwise, or (ii) ownership of fifty percent (50%) or more of the
-              outstanding shares, or (iii) beneficial ownership of such entity.
-        
-              "You" (or "Your") shall mean an individual or Legal Entity
-              exercising permissions granted by this License.
-        
-              "Source" form shall mean the preferred form for making modifications,
-              including but not limited to software source code, documentation
-              source, and configuration files.
-        
-              "Object" form shall mean any form resulting from mechanical
-              transformation or translation of a Source form, including but
-              not limited to compiled object code, generated documentation,
-              and conversions to other media types.
-        
-              "Work" shall mean the work of authorship, whether in Source or
-              Object form, made available under the License, as indicated by a
-              copyright notice that is included in or attached to the work
-              (an example is provided in the Appendix below).
-        
-              "Derivative Works" shall mean any work, whether in Source or Object
-              form, that is based on (or derived from) the Work and for which the
-              editorial revisions, annotations, elaborations, or other modifications
-              represent, as a whole, an original work of authorship. For the purposes
-              of this License, Derivative Works shall not include works that remain
-              separable from, or merely link (or bind by name) to the interfaces of,
-              the Work and Derivative Works thereof.
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-              "Contribution" shall mean any work of authorship, including
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-              to that Work or Derivative Works thereof, that is intentionally
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-        
-           2. Grant of Copyright License. Subject to the terms and conditions of
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-              copyright license to reproduce, prepare Derivative Works of,
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-           3. Grant of Patent License. Subject to the terms and conditions of
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-           4. Redistribution. You may reproduce and distribute copies of the
-              Work or Derivative Works thereof in any medium, with or without
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-              meet the following conditions:
-        
-              (a) You must give any other recipients of the Work or
-                  Derivative Works a copy of this License; and
-        
-              (b) You must cause any modified files to carry prominent notices
-                  stating that You changed the files; and
-        
-              (c) You must retain, in the Source form of any Derivative Works
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-        
-              (d) If the Work includes a "NOTICE" text file as part of its
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-                  documentation, if provided along with the Derivative Works; or,
-                  within a display generated by the Derivative Works, if and
-                  wherever such third-party notices normally appear. The contents
-                  of the NOTICE file are for informational purposes only and
-                  do not modify the License. You may add Your own attribution
-                  notices within Derivative Works that You distribute, alongside
-                  or as an addendum to the NOTICE text from the Work, provided
-                  that such additional attribution notices cannot be construed
-                  as modifying the License.
-        
-              You may add Your own copyright statement to Your modifications and
-              may provide additional or different license terms and conditions
-              for use, reproduction, or distribution of Your modifications, or
-              for any such Derivative Works as a whole, provided Your use,
-              reproduction, and distribution of the Work otherwise complies with
-              the conditions stated in this License.
-        
-           5. Submission of Contributions. Unless You explicitly state otherwise,
-              any Contribution intentionally submitted for inclusion in the Work
-              by You to the Licensor shall be under the terms and conditions of
-              this License, without any additional terms or conditions.
-              Notwithstanding the above, nothing herein shall supersede or modify
-              the terms of any separate license agreement you may have executed
-              with Licensor regarding such Contributions.
-        
-           6. Trademarks. This License does not grant permission to use the trade
-              names, trademarks, service marks, or product names of the Licensor,
-              except as required for reasonable and customary use in describing the
-              origin of the Work and reproducing the content of the NOTICE file.
-        
-           7. Disclaimer of Warranty. Unless required by applicable law or
-              agreed to in writing, Licensor provides the Work (and each
-              Contributor provides its Contributions) on an "AS IS" BASIS,
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-              PARTICULAR PURPOSE. You are solely responsible for determining the
-              appropriateness of using or redistributing the Work and assume any
-              risks associated with Your exercise of permissions under this License.
-        
-           8. Limitation of Liability. In no event and under no legal theory,
-              whether in tort (including negligence), contract, or otherwise,
-              unless required by applicable law (such as deliberate and grossly
-              negligent acts) or agreed to in writing, shall any Contributor be
-              liable to You for damages, including any direct, indirect, special,
-              incidental, or consequential damages of any character arising as a
-              result of this License or out of the use or inability to use the
-              Work (including but not limited to damages for loss of goodwill,
-              work stoppage, computer failure or malfunction, or any and all
-              other commercial damages or losses), even if such Contributor
-              has been advised of the possibility of such damages.
-        
-           9. Accepting Warranty or Additional Liability. While redistributing
-              the Work or Derivative Works thereof, You may choose to offer,
-              and charge a fee for, acceptance of support, warranty, indemnity,
-              or other liability obligations and/or rights consistent with this
-              License. However, in accepting such obligations, You may act only
-              on Your own behalf and on Your sole responsibility, not on behalf
-              of any other Contributor, and only if You agree to indemnify,
-              defend, and hold each Contributor harmless for any liability
-              incurred by, or claims asserted against, such Contributor by reason
-              of your accepting any such warranty or additional liability.
-        
-           END OF TERMS AND CONDITIONS
-        
-        
-           Copyright 2025 : SUBINOY BERA 
-        
-           Licensed under the Apache License, Version 2.0 (the "License");
-           you may not use this file except in compliance with the License.
-           You may obtain a copy of the License at
-        
-               http://www.apache.org/licenses/LICENSE-2.0
-        
-           Unless required by applicable law or agreed to in writing, software
-           distributed under the License is distributed on an "AS IS" BASIS,
-           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-           See the License for the specific language governing permissions and
-           limitations under the License.
-Classifier: Development Status :: 4 - Beta
-Classifier: Intended Audience :: Developers
-Classifier: License :: OSI Approved :: Apache Software License
-Classifier: Operating System :: OS Independent
-Classifier: Programming Language :: Python :: 3.11
-Classifier: Programming Language :: Python :: 3.12
-Requires-Python: >=3.11
-Description-Content-Type: text/markdown
-License-File: LICENSE
-Requires-Dist: numpy
-Requires-Dist: pandas
-Requires-Dist: scikit-learn
-Requires-Dist: xgboost
-Requires-Dist: nltk
-Requires-Dist: emoji
-Requires-Dist: pymongo
-Requires-Dist: PyYAML
-Requires-Dist: requests
-Requires-Dist: pydantic
-Requires-Dist: python-dotenv
-Requires-Dist: tqdm
-Requires-Dist: httpx
-Requires-Dist: huggingface-hub
-Requires-Dist: fastapi
-Requires-Dist: uvicorn
-Requires-Dist: gunicorn
-Provides-Extra: mlops
-Requires-Dist: dvc; extra == "mlops"
-Requires-Dist: dagshub; extra == "mlops"
-Requires-Dist: mlflow; extra == "mlops"
-Requires-Dist: evidently; extra == "mlops"
-Requires-Dist: lime; extra == "mlops"
-Requires-Dist: prometheus-client; extra == "mlops"
-Provides-Extra: dev
-Requires-Dist: pytest; extra == "dev"
-Requires-Dist: tox; extra == "dev"
-Requires-Dist: docker; extra == "dev"
-Requires-Dist: scipy; extra == "dev"
-Requires-Dist: matplotlib; extra == "dev"
-Requires-Dist: ipykernel; extra == "dev"
-Requires-Dist: tensorflow; extra == "dev"
-Requires-Dist: python-box; extra == "dev"
-Requires-Dist: ensure; extra == "dev"
-Provides-Extra: testing
-Requires-Dist: pytest>=8.0.0; extra == "testing"
-Requires-Dist: black; extra == "testing"
-Requires-Dist: flake8; extra == "testing"
-Requires-Dist: mypy; extra == "testing"
-Dynamic: license-file
-
-# Toxic-Tweet Tagger
-
-## 📑 Technical System Documentation :
-
-<u><b>End to End Production-Grade Hate Speech Detection Application</b></u>
-
-The Toxic Tweet Tagger is a production-oriented machine learning system designed to detect hate speech and toxic content in social media text. Unlike <b>typical ML Demos</b> that focus only on model training and accuracy, this project aims to represent <b>real-world production level workflows!</b> It covers the <b>complete lifecycle</b> from developing <b>core ML pipeline</b>, implementing <b>MLOps principles</b>, creating <b>inference API</b>, including <b>automated testing, experimentation tracking, CI/CD, monitoring</b> and much more! Built to demonstrate how industries deploy ML solutions at scale, - ensuring <b> scalability </b>, <b>reproducibility</b> and <b> maintainability </b>. <br>
-In essence, its <b>not just about building a machine learning model</b> with some fancy accuracy — it is about building a <b>complete ecosystem</b> around the model. It demonstrates how to design an ML system in line with best practices used by ML teams and Engineers in <b>production environments</b>.
-
-The system covers the complete lifecycle of an ML application:
-- Data ingestion and validation.
-- Model training and experimentation.
-- Model versioning and registry management
-- Automated testing and CI pipelines
-- Containerized inference services
-- Deployment and monitoring readiness
-
-## ✍️ Highlights :
-
-- 📂 <b>End-to-End ML Pipeline</b> – Complete ML lifecycle starting from data ingestion and preprocessing to model training, evaluation. Every stage is modular and reusable, making the pipeline easy to extend or adapt to new datasets.
-
-- ⚓ <b>Data Drift check with Evidently</b> – Right after data ingestion, Evidently is used for data validation to ensure data quality, schema consistency, and distribution checks. This helps catch potential issues early and ensures only valid data flows into the training pipeline.
-
-- ✒️ <b>Data & Pipeline Tracking with DVC</b> – Datasets, artifacts, and pipelines are all tracked with DVC, providing transparency, version control, and detecting changes in any data or pipeline. Prevents recomputation if no changes are detected.
-
-- 🔍 <b>Experiment Tracking, Model Versioning & Registration in MLflow</b> – Every training run is logged with metrics, hyperparameters, artifacts, and model versions, ensuring that results are fully reproducible and enabling easy comparison of experiments. Models are also saved in the model registry with stages for reproducibility and identification which model is currently at production.
-
-- 🧪 <b>Automated Testing & Code Quality Checks</b> – The project is production-hardened with unit tests, integration tests, and linting/formatting tools. CI ensures that code remains clean, reliable, and maintainable at scale.
-
-- ⌛ <b>CI/CD Automation with GitHub Actions</b> – The pipeline is integrated with continuous integration and deployment, enabling automatic testing, model retraining, and deployment with every new update, following true MLOps principles.
-
-- ⚡ <b>FastAPI Inference Service</b> – A high-performance REST API built with FastAPI for real-time predictions. Designed with scalability in mind- it provides the production-ready serving layer that is containerized and deployed in HuggingFace Spaces.
-
-- 🔭 <b>Model Explainability with LIME</b> – Integrated LIME (Local Interpretable Model-agnostic Explanations) to interpret individual predictions and understand why the model made a decision. This improves transparency, trust, and debuggability of the deployed ML system.
-
-- 🖥️ <b>Monitoring-ready Architecture</b> – Used logging and exception modules for proper tracking and error handling. While Evidently is already used at data validation, the system is also designed to extend monitoring into production with Prometheus and Grafana, continuous drift detection, performance tracking, and retraining triggers.
-
-- 🧩 <b>Scalable, Modular and Industry-Grade Design</b> – Developed and engineered following best practices from modern ML teams, ensuring the project is maintainable, extensible, and future-proof, going far beyond typical portfolio ML demos.
-
-## 🛠️ Tech Stack :
-
-- <b>Languages</b> : Python, Jupyter | HTML, CSS, JavaScript (for UI development)
-- <b>ML /DL/ NLP</b> : Pandas, numpy, scipy, scikit-learn, nltk, tensorflow
-- <b>MLOps Tools</b> : Github Actions, Docker, MLflow, DVC, Evidently, Prometheus
-- <b>Database</b> : MongoDB Atlas
-- <b>API & Serving</b> : FastAPI, HuggingFace spaces (model inference server)
-- <b>Testing & Code Quality</b> : tox, pytest, black, flake8, mypy
-- <b>Deployment</b> : Vercel
-
-## 🚀 Version 2.0 Update :
-- Inference time reduced from ~200 ms to <10 ms.
-- Implemented feedback and prediction explanation services.
-- Exposes Prometheus metrics endpoint for monitoring and visualization using Grafana dashboards.
-- API architecture improvements and UI enhancements.
-- Implemented buffered message queues and worker processes to handle background tasks asynchronously, reducing API latency and improving overall system scalability.
-
-## 🎯 Application Preview :
-
-<p>
-    <img src="images/app_preview.png" alt="Application image" height="300" width="600">
-</p>
-
-## 🔗 Links :
-
-- ### [Visit App ↗️](https://toxic-tweet-tagger.vercel.app/)
-- ### [Hugging Face 🤗](https://huggingface.co/Subi003)
-
-📌 <b>PLEASE NOTE</b> :
-This project focuses on Machine Learning engineering, MLOps, and model deployment aspects rather than full-stack web development.
-The application interface is built using basic HTML & CSS only, just enough to demonstrate how end users can interact with the deployed model.
-<br> The core emphasis of this work is on:
-
-* Data pipeline design.
-* Model versioning & experiment management with MLflow.
-* MLOps best practices (CI/CD, testing, observability, deployment)
-
-Despite several optimizations, it might still have latency issues and cold start delays up to 5-10 secs.
-<b> 📍 All open source tools and free services have been used to develop this application!! 🙏✨</b>
-<br>
-
-## 📊 Experiment Results :
-
-<p>
-    <img src="images/vizualization1.png" alt="Pair-plot visualization" height="300" width="600" style="margin-bottom: 10px;">
-    <img src="images/data_drift-report.png" alt="MLflow Experiment Plot" height="300" width="600" style="margin-bottom: 10px;">
-    <img src="images/all_experiments.png" alt="MLflow Experiment Plot" height="300" width="600" style="margin-bottom: 10px;">
-    <img src="images/lime_report.png" alt="MLflow Experiment Plot" height="300" width="600">
-    </p>
-</p>
-
-## ⚙️ Installation :
-
-STEP: 01 - Clone this repository
-
-```cmd
-git clone https://github.com/SubinoyBera/ToxicTweet-Tagger
-cd ToxicTweet-Tagger
-```
-
-STEP: 02 - Create and activate conda environment
-
-```bash
-conda create -p venv python=3.11.5 -y
-conda activate venv
-```
-
-STEP: 03 - Install project requirements. <br>
-To run the application, only install the app requirements
-
-```bash
-pip install -r app/requirements.txt
-```
-
-For development or running ML pipeline, install the full dev requirements
-
-```bash
-pip install uv  # Using uv for fast download [Optional]
-uv pip install -r requirements-dev.txt
-```
-
-STEP: 04 - Build the package
-
-```bash
-python -m build
-```
-
-STEP: 05 - If you want to try out the ML pipeline, perform this step only; otherwise, please `skip this` and go directly to `STEP: 06`. <br>
-Create a `.env` file in the root directory and add the required environment variables as mentioned in the given `.env.example` file. Make sure you have accounts in Dagshub, MongoDB Atlas, and HuggingFace. Get your hate-tweet dataset, perform EDA, experiments, and upload it to MongoDB. Update the files in the `src/` folder with your implementation and model parameters in the `params.yaml` file. Run this command to execute your ML pipeline :
-
-```bash
-dvc repro
-```
-
-STEP: 06 - Run the Application
-
-```bash
-python run_frontend.py
-```
-
-Open your browser and open the local URL : `http://localhost:8000/`.<br>
-Write your comment and click on the `predict` button. Click on "Extra Details" to get more info.
-
-<hr>
-
-### 💥 Future Updates :
-1. Deploying the application in production-grade servers (AWS).
-2. Upgrading model performances using transformer-based models such as BERT or RoBERTa.
-3. Implement automated model retraining pipeline and data drift detection in production.
-4. Adding request rate limiting and API security
-<br>
-
-🎗️🙏 **THANK YOU !!** :) <br>
-<b>-_with love & regards : Subinoy Bera (developer)_</b><br>
-🧡🤍💚
diff --git a/toxictweet_tagger.egg-info/SOURCES.txt b/toxictweet_tagger.egg-info/SOURCES.txt
deleted file mode 100644
index e4b88fb5741460c1cef6dc1a128f418435705de1..0000000000000000000000000000000000000000
--- a/toxictweet_tagger.egg-info/SOURCES.txt
+++ /dev/null
@@ -1,48 +0,0 @@
-LICENSE
-README.md
-pyproject.toml
-setup.py
-src/app/__init__.py
-src/app/main.py
-src/app/api/__init__.py
-src/app/api/api_routes.py
-src/app/api/dependencies.py
-src/app/api/schemas.py
-src/app/middleware/__init__.py
-src/app/monitoring/__init__.py
-src/app/monitoring/http_metrics.py
-src/app/monitoring/service_metrics.py
-src/app/services/__init__.py
-src/app/services/explainer.py
-src/app/services/feedback.py
-src/app/services/inference.py
-src/app/workers/__init__.py
-src/components/__init__.py
-src/components/data_ingestion.py
-src/components/data_preprocessing.py
-src/components/data_validation.py
-src/components/feature_engineering.py
-src/components/model_evaluation.py
-src/components/model_training.py
-src/core/__init__.py
-src/core/config_entity.py
-src/core/configuration.py
-src/core/constants.py
-src/core/exception.py
-src/core/logger.py
-src/core/mongo_client.py
-src/operations/__init__.py
-src/operations/load_model.py
-src/operations/promote_model.py
-src/operations/register_model.py
-src/operations/serve_model.py
-src/pipeline/__init__.py
-src/pipeline/ml_pipeline.py
-src/script/__init__.py
-src/script/model_wrapper.py
-src/toxictweet_tagger.egg-info/PKG-INFO
-src/toxictweet_tagger.egg-info/SOURCES.txt
-src/toxictweet_tagger.egg-info/dependency_links.txt
-src/toxictweet_tagger.egg-info/requires.txt
-src/toxictweet_tagger.egg-info/top_level.txt
-src/utils/__init__.py
\ No newline at end of file
diff --git a/toxictweet_tagger.egg-info/requires.txt b/toxictweet_tagger.egg-info/requires.txt
deleted file mode 100644
index 456c947664145d446e03cf5123640fbd816cbda9..0000000000000000000000000000000000000000
--- a/toxictweet_tagger.egg-info/requires.txt
+++ /dev/null
@@ -1,42 +0,0 @@
-numpy
-pandas
-scikit-learn
-xgboost
-nltk
-emoji
-pymongo
-PyYAML
-requests
-pydantic
-python-dotenv
-tqdm
-httpx
-huggingface-hub
-fastapi
-uvicorn
-gunicorn
-
-[dev]
-pytest
-tox
-docker
-scipy
-matplotlib
-ipykernel
-tensorflow
-python-box
-ensure
-
-[mlops]
-dvc
-dagshub
-mlflow
-evidently
-lime
-prometheus-client
-
-[testing]
-pytest>=8.0.0
-black
-flake8
-mypy
diff --git a/toxictweet_tagger.egg-info/top_level.txt b/toxictweet_tagger.egg-info/top_level.txt
deleted file mode 100644
index 162e075197759c780454e8054e13df160d30dd0f..0000000000000000000000000000000000000000
--- a/toxictweet_tagger.egg-info/top_level.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-app
-components
-core
-operations
-pipeline
-script
-utils
diff --git a/utils/__init__.py b/utils/__init__.py
index 1fc46e1940d656ccdf1a825216fbea6f9654d5c1..864b267fee420b09ea00f9e590f302608b066f89 100644
--- a/utils/__init__.py
+++ b/utils/__init__.py
@@ -1,96 +1,96 @@
-import os
-import sys
-import yaml
-import joblib
-from pathlib import Path
-from box import ConfigBox
-from ensure import ensure_annotations
-from src.core.logger import logging
-from src.core.exception import AppException
-
-
-@ensure_annotations
-def read_yaml(path_to_yaml: Path) -> ConfigBox:
-    """
-    Reads a YAML file and returns its contents as a ConfigBox object.
-    Args:
-        path_to_yaml (Path): The path to the YAML file to be read.
-    Returns:
-        ConfigBox: A ConfigBox object containing the parsed content of the YAML file.
-    """
-    try:
-        with open(path_to_yaml, 'r') as yaml_file:
-            content = yaml.safe_load(yaml_file)
-            logging.info(f"YAML file read successfully from {path_to_yaml}")
-
-            return ConfigBox(content)
-        
-    except Exception as e:
-        logging.error(f"Failed to read YAML file at {path_to_yaml}: {e}", exc_info=True)
-        raise AppException(e, sys) 
-        
-    
-def create_directory(path_to_directory: Path, verbose=True):
-    """
-    Creates directory at the specified location path.
-    Args:
-        path_to_directory : Directory path location where it is to be created.
-        verbose (bool, optional): If True, logs the creation of each directory. Defaults to True.
-    """
-    try:
-        if not isinstance(path_to_directory, Path):
-            raise TypeError("path_to_directory must be a Path object")
-        
-        if path_to_directory.exists():
-            logging.info(f"Directory already exists at {path_to_directory}")
-            return
-        
-        os.makedirs(path_to_directory, exist_ok=True)
-        logging.info(f"Directory created at {path_to_directory}") if verbose else None
-
-    except Exception as e:
-        logging.error(f"Failed to create directory at {path_to_directory}: {e}", exc_info=True)
-        raise AppException(e, sys)
-    
-
-def save_obj(location_path: Path, obj, obj_name: str):
-    """
-    Saves a given object to the given path in joblib format.
-    Args:
-        location_path (Path): The path to the directory where the object should be saved.
-        obj (object): The object to be saved.
-        obj_name (str): The name of the object to be saved (should include .joblib extension).
-    """
-    try:
-        if not obj_name.endswith(".joblib"):
-            logging.error(f"Invalid object format.")
-            raise ValueError(f"Invalid file format for object: {obj_name}")
-        
-        with open(Path(location_path, obj_name), 'wb') as f:
-            joblib.dump(obj, f)
-            logging.info(f"{obj_name} saved at {location_path}")
-
-    except Exception as e:
-        logging.error(f"Failed to save object at {location_path}: {e}", exc_info=True)
-        raise AppException(e, sys)
-
-
-def load_obj(location_path: Path, obj_name: str):
-    """
-    Loads a given object from the given path.
-    Args:
-        location_path (Path): The directory path from where the object is to be loaded.
-        obj_name (str): The name of the object to be loaded (should have .joblib extension).
-    """
-    try:
-        if not obj_name.endswith(".joblib"):
-            logging.error(f"Invalid object format.")
-            raise ValueError(f"Invalid file format for object: {obj_name}")
-        with open(Path(location_path, obj_name), 'rb') as f:
-            obj = joblib.load(f)
-            logging.info(f"{obj_name} loaded from {location_path}")
-            return obj
-
-    except Exception as e:
-        logging.error(f"Failed to load object from {location_path}: {e}", exc_info=True)
-        raise AppException(e, sys)
+import os
+import sys
+import yaml
+import joblib
+from pathlib import Path
+from box import ConfigBox
+from ensure import ensure_annotations
+from src.core.logger import logging
+from src.core.exception import AppException
+
+
+@ensure_annotations
+def read_yaml(path_to_yaml: Path) -> ConfigBox:
+    """
+    Reads a YAML file and returns its contents as a ConfigBox object.
+    Args:
+        path_to_yaml (Path): The path to the YAML file to be read.
+    Returns:
+        ConfigBox: A ConfigBox object containing the parsed content of the YAML file.
+    """
+    try:
+        with open(path_to_yaml, 'r') as yaml_file:
+            content = yaml.safe_load(yaml_file)
+            logging.info(f"YAML file read successfully from {path_to_yaml}")
+
+            return ConfigBox(content)
+        
+    except Exception as e:
+        logging.error(f"Failed to read YAML file at {path_to_yaml}: {e}", exc_info=True)
+        raise AppException(e, sys) 
+        
+    
+def create_directory(path_to_directory: Path, verbose=True):
+    """
+    Creates directory at the specified location path.
+    Args:
+        path_to_directory : Directory path location where it is to be created.
+        verbose (bool, optional): If True, logs the creation of each directory. Defaults to True.
+    """
+    try:
+        if not isinstance(path_to_directory, Path):
+            raise TypeError("path_to_directory must be a Path object")
+        
+        if path_to_directory.exists():
+            logging.info(f"Directory already exists at {path_to_directory}")
+            return
+        
+        os.makedirs(path_to_directory, exist_ok=True)
+        logging.info(f"Directory created at {path_to_directory}") if verbose else None
+
+    except Exception as e:
+        logging.error(f"Failed to create directory at {path_to_directory}: {e}", exc_info=True)
+        raise AppException(e, sys)
+    
+
+def save_obj(location_path: Path, obj, obj_name: str):
+    """
+    Saves a given object to the given path in joblib format.
+    Args:
+        location_path (Path): The path to the directory where the object should be saved.
+        obj (object): The object to be saved.
+        obj_name (str): The name of the object to be saved (should include .joblib extension).
+    """
+    try:
+        if not obj_name.endswith(".joblib"):
+            logging.error(f"Invalid object format.")
+            raise ValueError(f"Invalid file format for object: {obj_name}")
+        
+        with open(Path(location_path, obj_name), 'wb') as f:
+            joblib.dump(obj, f)
+            logging.info(f"{obj_name} saved at {location_path}")
+
+    except Exception as e:
+        logging.error(f"Failed to save object at {location_path}: {e}", exc_info=True)
+        raise AppException(e, sys)
+
+
+def load_obj(location_path: Path, obj_name: str):
+    """
+    Loads a given object from the given path.
+    Args:
+        location_path (Path): The directory path from where the object is to be loaded.
+        obj_name (str): The name of the object to be loaded (should have .joblib extension).
+    """
+    try:
+        if not obj_name.endswith(".joblib"):
+            logging.error(f"Invalid object format.")
+            raise ValueError(f"Invalid file format for object: {obj_name}")
+        with open(Path(location_path, obj_name), 'rb') as f:
+            obj = joblib.load(f)
+            logging.info(f"{obj_name} loaded from {location_path}")
+            return obj
+
+    except Exception as e:
+        logging.error(f"Failed to load object from {location_path}: {e}", exc_info=True)
+        raise AppException(e, sys)
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