Deploy Gradio app to HF Spaces

- Remove Streamlit/FastAPI/Celery
- Add single-file Gradio UI
- Add SQLite for prediction history
- Update docs and CI

.env.example

@@ -1,22 +1 @@
-# Database
-DATABASE_URL=postgresql://user:pass@localhost:5432/absa_db
-POSTGRES_USER=absa_user
-POSTGRES_PASSWORD=change_this_to_strong_password
-POSTGRES_DB=absa_db
-
-# Redis / Celery
-REDIS_URL=redis://localhost:6379/0
-REDIS_PASSWORD=change_this_to_strong_password
-
-# Model
-MODEL_PATH=models/onnx/
-MAX_BATCH_SIZE=10000
-
-# Security
-CORS_ORIGINS=http://localhost:3000,http://localhost:8000
-CSRF_SECRET=change_this_to_strong_random_secret
-GRAFANA_ADMIN_PASSWORD=change_this_to_strong_password
-
-# Logging
-LOG_LEVEL=INFO
-ENABLE_METRICS=true
+MODEL_PATH=./models/xlm-roberta-quantized

.gitattributes

@@ -23,5 +23,8 @@ dist/** linguist-generated=true
 build/** linguist-generated=true
 node_modules/** linguist-generated=true
 **/node_modules/** linguist-generated=true
+# Hugging Face Spaces LFS (model artifacts)
+models/**/*.onnx filter=lfs diff=lfs merge=lfs -text
+models/**/*.bin filter=lfs diff=lfs merge=lfs -text
 *.ftz filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text

.github/workflows/ci.yml

@@ -25,9 +25,9 @@ jobs:
       - name: Install ruff
         run: pip install ruff==0.15.11
       - name: Ruff check
-        run: ruff check api src/absa tests scripts
+        run: ruff check src/absa tests scripts
       - name: Ruff format check
-        run: ruff format --check api src/absa tests scripts
+        run: ruff format --check src/absa tests scripts
 
   typecheck:
     name: Typecheck (mypy)
@@ -42,7 +42,7 @@ jobs:
           pip install --upgrade pip
           pip install -e ".[dev]"
       - name: Mypy
-        run: mypy api src/absa
+        run: mypy src/absa
 
   security:
     name: Security (bandit)
@@ -55,7 +55,7 @@ jobs:
       - name: Install bandit
         run: pip install bandit
       - name: Bandit scan
-        run: bandit -r api src/absa
+        run: bandit -r src/absa
 
   test:
     name: Tests (pytest) — py${{ matrix.python }}
@@ -75,4 +75,4 @@ jobs:
           pip install --upgrade pip
           pip install -e ".[dev]"
       - name: Run tests
-        run: pytest tests/ -v
+        run: pytest tests/unit/ -v

.gitignore

@@ -65,3 +65,6 @@ logs/
 
 # Node
 node_modules/
+
+absa.db
+absa.db-journal

.pre-commit-config.yaml

@@ -10,7 +10,6 @@ repos:
     rev: v4.6.0
     hooks:
       - id: check-yaml
-        exclude: ^docker/docker-compose.*\.ya?ml$
       - id: check-json
       - id: end-of-file-fixer
         exclude: ^notebooks/

README.md

@@ -1,227 +1,51 @@
-# Multilingual Aspect-Based Sentiment Analysis (ABSA)
-
-[![Python](https://img.shields.io/badge/Python-3.10%2B-blue.svg)](https://python.org)
-[![FastAPI](https://img.shields.io/badge/FastAPI-0.115-00a393.svg)](https://fastapi.tiangolo.com)
-[![Streamlit](https://img.shields.io/badge/Streamlit-1.37-FF4B4B.svg)](https://streamlit.io)
-[![DVC](https://img.shields.io/badge/DVC-3.51-945dd6.svg)](https://dvc.org)
-[![MLflow](https://img.shields.io/badge/MLflow-2.15-0194E2.svg)](https://mlflow.org)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
-
-**Analyse product reviews in English, Hindi, and Hinglish — extract aspects and their sentiment in real time.**
-
-A pure-Python stack: a Streamlit dashboard on top of a FastAPI JSON API, backed by a multilingual ML pipeline (ONNX inference with a rule-based fallback).
-
----
-
-## Quick Start
-
-```bash
-git clone <repo-url> && cd multilingual-absa
-python -m venv .venv && source .venv/bin/activate
-pip install -e ".[dev]"
-```
-
-Start the API (port 8000) and the dashboard (port 8501) in two terminals:
-
-```bash
-# Terminal 1 — REST API
-uvicorn api.main:app --reload --host 0.0.0.0 --port 8000
-
-# Terminal 2 — Streamlit dashboard
-streamlit run frontend/Home.py
-```
-
-Open:
-- **Dashboard** → [`http://localhost:8501`](http://localhost:8501)
-- **API docs (Swagger)** → [`http://localhost:8000/docs`](http://localhost:8000/docs)
-
 ---
-
-## What This Does
-
-The system identifies **aspects** (specific features like "battery life", "sound quality") and their **sentiment** (positive, negative, neutral) from product reviews. It supports three languages:
-
-| Language | Aspect Extraction | Sentiment |
-|----------|:-:|:-:|
-| English   | ✅ | ✅ |
-| Hindi     | ✅ | ✅ |
-| Hinglish  | ✅ | ✅ |
-
-**Production performance** (ONNX INT8 quantised):
-
-| Language | F1 Score | Latency p95 |
-|----------|:--------:|:-----------:|
-| English  | 78.1%    | 185 ms      |
-| Hindi    | 67.8%    | 185 ms      |
-
+title: Multilingual ABSA
+emoji: 🎯
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
 ---
 
-## Project Structure
+# Multilingual ABSA
 
-```
-.
-├── api/                        # FastAPI REST service
-│   ├── main.py                 # Entry point — uvicorn api.main:app
-│   ├── models/                 # SQLAlchemy ORM models (Review, AspectResult, BatchJob)
-│   ├── middleware/             # Rate limiting, metrics (Prometheus), DB deps
-│   ├── routes/                 # /predict, /batch, /status, /download, /health, /info
-│   ├── schemas/                # Pydantic request/response models
-│   ├── services/               # ABSA inference pipeline, language detection
-│   └── tasks/                  # Celery batch processing workers
-├── src/absa/                   # Core ML library (src-layout, pip-installable)
-│   ├── data/                   # Loading, preprocessing, augmentation, transliteration
-│   ├── models/                 # Training scripts (ONNX, Transformers, baselines)
-│   ├── evaluation/             # Cross-lingual eval, latency benchmarking
-│   ├── training/               # MLflow experiment tracking
-│   └── utils/                  # Path + environment configuration
-├── frontend/                   # Streamlit dashboard (pure Python, no HTML templates)
-│   ├── Home.py                 # Entry point — streamlit run frontend/Home.py
-│   ├── absa_client.py          # Thin HTTP client for the FastAPI backend
-│   ├── ui.py                   # Native Streamlit UI helpers
-│   └── views/                  # Pages: predict, admin (overview/batch/monitor)
-├── tests/                      # Pytest suite
-│   ├── conftest.py             # sys.path bootstrap — no PYTHONPATH hacks required
-│   ├── api/                    # API endpoint tests
-│   ├── web/                    # Streamlit page + client tests
-│   └── unit/                   # Unit tests (bio tagger, lang detect)
-├── scripts/                    # Operational utility scripts
-├── notebooks/                  # Exploration & Colab training notebooks (numbered 01–05)
-├── monitoring/                 # Prometheus + Grafana config
-├── docs/                       # Documentation
-├── docker/                     # Containerisation
-├── data/                       # Datasets (managed by DVC)
-├── models/                     # ONNX model artifacts (DVC-tracked)
-├── .github/workflows/          # CI pipeline (lint, typecheck, security, tests)
-├── .env.example                # Environment variable template
-├── dvc.yaml                    # DVC data pipeline
-├── pyproject.toml              # Project metadata + tool config
-└── Makefile                    # Developer command shortcuts
-```
+Real-time aspect-based sentiment analysis for English, Hindi, Hinglish product reviews.
 
----
+🔗 **[Live Demo on Hugging Face Spaces](https://huggingface.co/spaces/Aryanmdev/multilingual-absa)**
+📦 [GitHub repo](https://github.com/Aryanmishra-dev/Multilingual-Absa)
 
-## Architecture
-
-```
-┌──────────┐   native Streamlit   ┌──────────────────────────────┐
-│  Browser │◄────────────────────►│  Streamlit Dashboard (8501)   │
-└──────────┘                      │  frontend/ — pure Python UI    │
-                                  └──────────────┬───────────────┘
-                                                 │ HTTP (httpx)
-                                  ┌──────────────▼───────────────┐
-                                  │  FastAPI REST API (8000)      │
-                                  │  api/main.py — JSON endpoints │
-                                  └──────────────┬───────────────┘
-                                                 │
-                     ┌───────────────────────────┼────────────────────┐
-                     │                           │                    │
-              ┌──────▼──────┐          ┌─────────▼────────┐   ┌────────▼───────┐
-              │ PostgreSQL / │          │ Redis / Celery   │   │ Prometheus +   │
-              │  SQLite      │          │ (batch jobs)     │   │ Grafana        │
-              │ (results)    │          └──────────────────┘   └────────────────┘
-              └─────────────┘
-```
-
-### Inference Stack
-
-| Component | Layer | Notes |
-|-----------|-------|-------|
-| ONNX INT8 | Primary | Production — 185 ms p95 latency |
-| ONNX FP32 | Fallback | Same model, no quantisation — 520 ms |
-| Rule-based | Fallback | Keyword lexicon + context-window scoring — zero download |
-
-The pipeline auto-selects: **ONNX INT8** → **ONNX FP32** → **Rule-based**. No external model downloads required.
-
----
-
-## Features
-
-### Dashboard (Streamlit)
-
-- **Sentiment Analyzer** — Real-time aspect/sentiment analysis with confidence bars
-- **Batch Analytics** — CSV upload, async Celery job, live progress, result download
-- **System Monitor** — Health checks, service metadata, auto-refresh diagnostics
-- **Admin Lock** — Optional password-gated admin section (`ADMIN_PASSWORD`)
-
-### API (RESTful JSON)
-
-| Endpoint | Method | Description |
-|----------|--------|-------------|
-| `/predict` | POST | Analyse a single review |
-| `/batch` | POST | Upload CSV for bulk analysis |
-| `/status/{job_id}` | GET | Check batch job progress |
-| `/download/{job_id}` | GET | Download batch results (CSV) |
-| `/health` | GET | API health check |
-| `/info` | GET | Model metadata |
-| `/metrics` | GET | Prometheus metrics |
-| `/docs` | GET | Swagger UI |
-
-### ML Pipeline (DVC)
+## Run Locally
 
 ```bash
-dvc pull       # Download datasets
-dvc repro      # Reproduce preprocessing
-dvc push       # Upload to remote storage
+git clone https://github.com/Aryanmishra-dev/Multilingual-Absa
+cd Multilingual-Absa
+pip install -r requirements.txt
+python app.py
 ```
 
-### Monitoring
+Open http://localhost:7860
 
-- **Prometheus** metrics at `/metrics`
-- **Grafana** dashboards for request volume, latency, error rate
-- **Evidently** drift detection (`scripts/drift_monitor.py`)
-- **MLflow** experiment tracking (`scripts/mlflow_ui.sh`)
+## What it does
 
----
+Extracts **aspects** (e.g. "battery", "camera") and their **sentiment** (positive/negative/neutral) from product reviews in 3 languages.
 
-## Development
+| Language | F1 | Latency p95 |
+|----------|:--:|:-----------:|
+| English  | 78.1% | 185 ms |
+| Hindi    | 67.8% | 185 ms |
 
-### Run Tests
+ONNX INT8 quantized → FP32 → rule-based fallback.
 
-```bash
-make test        # pytest
-make lint        # ruff
-make typecheck   # mypy
-make security    # bandit
-```
+## Stack
 
-### Run Full Stack (Docker)
-
-```bash
-docker compose -f docker/docker-compose.yml up --build
-```
-
-Starts: API (8000), PostgreSQL, Redis, Celery worker, Prometheus (9090), Grafana (3001).
-
-### Environment Variables
-
-Copy `.env.example` to `.env` and configure:
-
-| Variable | Required | Description |
-|----------|:--------:|-------------|
-| `DATABASE_URL` | ✅ | PostgreSQL or SQLite connection string |
-| `REDIS_URL` | ✅ | Redis connection for Celery |
-| `CORS_ORIGINS` | ❌ | Allowed CORS origins (default: localhost) |
-| `MODEL_PATH` | ❌ | Path to ONNX model directory |
-| `MAX_BATCH_SIZE` | ❌ | Max reviews per batch job |
-| `ADMIN_PASSWORD` | ❌ | Password for admin dashboard sections |
-| `API_BASE_URL` | ❌ | Base URL the dashboard uses to reach the API |
-
----
-
-## Documentation
-
-| Document | Contents |
-|----------|----------|
-| [API](docs/API_DOCUMENTATION.md) | Full API reference with schemas and examples |
-| [Architecture](docs/architecture.md) | System design, data flow, deployment |
-| [Deployment](docs/DEPLOYMENT.md) | Production setup, Docker, Railway |
-| [Database](docs/DATABASE.md) | Schema, migrations, query patterns |
-| [Security](docs/SECURITY.md) | Threat model, audit results, mitigations |
-| [Tech Stack](docs/TECH_STACK.md) | Framework versions, rationale, trade-offs |
-| [System Design](docs/SYSTEM_DESIGN.md) | End-to-end design and data flow |
-
----
+- Gradio 4.44 (UI)
+- ONNX Runtime (inference)
+- XLM-RoBERTa (multilingual backbone)
+- SQLite (prediction history)
 
 ## License
 
-MIT License. See [LICENSE](LICENSE) for details.
+MIT

api/main.py

@@ -1,56 +0,0 @@
-import os
-from contextlib import asynccontextmanager
-
-from dotenv import load_dotenv
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from slowapi import Limiter, _rate_limit_exceeded_handler
-from slowapi.errors import RateLimitExceeded
-from slowapi.util import get_remote_address
-
-load_dotenv()
-
-from api.middleware.dependencies import engine  # noqa: E402
-from api.middleware.metrics import instrumentator  # noqa: E402
-from api.models.db_models import Base  # noqa: E402
-from api.routes import predict, results  # noqa: E402
-from api.services.absa_pipeline import pipeline  # noqa: E402
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    # Startup
-    print("Initializing Database tables...")
-    Base.metadata.create_all(bind=engine)
-
-    print("Loading Models...")
-    pipeline.load_models()
-
-    yield
-    # Shutdown
-    print("Shutting down...")
-
-
-limiter = Limiter(key_func=get_remote_address)
-
-app = FastAPI(
-    title="Multilingual ABSA API",
-    description="Aspect-Based Sentiment Analysis for English and Hindi",
-    version="1.0.0",
-    lifespan=lifespan,
-)
-
-app.state.limiter = limiter
-app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)  # type: ignore[arg-type]
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=os.getenv("CORS_ORIGINS", "http://localhost:8000,http://localhost:8501").split(","),
-    allow_credentials=True,
-    allow_methods=["GET", "POST"],
-    allow_headers=["*"],
-)
-app.include_router(predict.router, tags=["Predict"])
-app.include_router(results.router, tags=["System"])
-
-instrumentator.instrument(app).expose(app, endpoint="/metrics")

api/middleware/dependencies.py

@@ -1,27 +0,0 @@
-import os
-
-from dotenv import load_dotenv
-from sqlalchemy import create_engine
-from sqlalchemy.orm import sessionmaker
-
-load_dotenv()
-
-DATABASE_URL = os.getenv("DATABASE_URL")
-
-if not DATABASE_URL:
-    raise RuntimeError("DATABASE_URL environment variable is not set. Please set it in your .env file or environment.")
-
-connect_args = {}
-if DATABASE_URL.startswith("sqlite"):
-    connect_args["check_same_thread"] = False
-
-engine = create_engine(DATABASE_URL, pool_pre_ping=True, connect_args=connect_args)
-SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
-
-
-def get_db():
-    db = SessionLocal()
-    try:
-        yield db
-    finally:
-        db.close()

api/middleware/metrics.py

@@ -1,12 +0,0 @@
-from prometheus_fastapi_instrumentator import Instrumentator
-
-instrumentator = Instrumentator(
-    should_group_status_codes=False,
-    should_ignore_untemplated=True,
-    should_respect_env_var=True,
-    should_instrument_requests_inprogress=True,
-    excluded_handlers=[".*admin.*", "/metrics"],
-    env_var_name="ENABLE_METRICS",
-    inprogress_name="inprogress",
-    inprogress_labels=True,
-)

api/routes/predict.py

@@ -1,129 +0,0 @@
-import os
-import re
-import tempfile
-import time
-import uuid
-
-import pandas as pd
-from fastapi import APIRouter, Depends, File, HTTPException, UploadFile
-from sqlalchemy.orm import Session
-
-from api.middleware.dependencies import get_db
-from api.models.db_models import AspectResult, BatchJob, Review
-from api.schemas.schemas import BatchJobResponse, PredictionResponse, ReviewInput
-from api.services.absa_pipeline import pipeline
-from api.tasks.batch_tasks import process_batch
-
-router = APIRouter()
-
-
-@router.post("/predict", response_model=PredictionResponse)
-async def predict(request: ReviewInput, db: Session = Depends(get_db)):
-    try:
-        time.time()
-
-        # Inference
-        prediction = pipeline.predict(request.text, request.language)
-
-        # Save to DB
-        db_review = Review(
-            text=prediction.text,
-            language=prediction.language,
-            processing_time_ms=prediction.processing_time_ms,
-        )
-        db.add(db_review)
-        db.commit()
-        db.refresh(db_review)
-
-        for asp in prediction.aspects:
-            db_aspect = AspectResult(
-                review_id=db_review.id,
-                aspect=asp.aspect,
-                sentiment=asp.sentiment,
-                confidence=asp.confidence,
-                start_pos=asp.start,
-                end_pos=asp.end,
-            )
-            db.add(db_aspect)
-        db.commit()
-
-        return prediction
-    except Exception:
-        raise HTTPException(status_code=500, detail="Model inference failed. Please try again.")
-
-
-@router.post("/batch", response_model=BatchJobResponse)
-async def predict_batch(file: UploadFile = File(...), db: Session = Depends(get_db)):
-    max_upload_size = 50 * 1024 * 1024  # 50MB
-
-    if not file.filename or not file.filename.endswith(".csv"):
-        raise HTTPException(status_code=422, detail="Only CSV files are allowed.")
-
-    if file.content_type and file.content_type not in ("text/csv", "application/vnd.ms-excel", "text/plain", ""):
-        raise HTTPException(status_code=422, detail="Invalid file type. CSV required.")
-
-    try:
-        content = await file.read()
-        if len(content) > max_upload_size:
-            raise HTTPException(status_code=422, detail="File exceeds 50MB maximum size.")
-
-        # Create temp file to read
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp:
-            tmp.write(content)
-            tmp_path = tmp.name
-
-        # Validate CSV structure before processing
-        try:
-            df = pd.read_csv(tmp_path, nrows=1)
-        except Exception:
-            os.unlink(tmp_path)
-            raise HTTPException(status_code=422, detail="Invalid CSV format.")
-
-        df = pd.read_csv(tmp_path)
-        if "text" not in df.columns:
-            os.unlink(tmp_path)
-            raise HTTPException(status_code=422, detail="CSV must contain a 'text' column.")
-
-        if len(df) > 10000:
-            os.unlink(tmp_path)
-            raise HTTPException(status_code=422, detail="Max 10,000 rows allowed per batch.")
-
-        job_id_obj = uuid.uuid4()
-        job_id = str(job_id_obj)
-        db_job = BatchJob(id=job_id_obj, status="queued", total=len(df), processed=0)
-        db.add(db_job)
-        db.commit()
-
-        # Queue Celery task
-        process_batch.delay(job_id, tmp_path)
-
-        return BatchJobResponse(job_id=job_id, status="queued", total_reviews=len(df), processed=0)
-    except HTTPException:
-        raise
-    except Exception:
-        raise HTTPException(status_code=500, detail="Batch processing failed. Please try again.")
-
-
-@router.get("/status/{job_id}", response_model=BatchJobResponse)
-async def get_batch_status(job_id: str, db: Session = Depends(get_db)):
-    if not re.match(r"^[a-fA-F0-9\-]{36}$", job_id):
-        raise HTTPException(status_code=400, detail="Invalid job ID format")
-    try:
-        job_id_uuid = uuid.UUID(job_id)
-    except ValueError:
-        raise HTTPException(status_code=400, detail="Invalid job ID format")
-    job = db.query(BatchJob).filter(BatchJob.id == job_id_uuid).first()
-    if not job:
-        raise HTTPException(status_code=404, detail="Job not found")
-
-    result_url = None
-    if job.status == "completed":
-        result_url = f"/results/download/{job_id}"
-
-    return BatchJobResponse(
-        job_id=str(job.id),
-        status=job.status,
-        total_reviews=job.total,
-        processed=job.processed,
-        result_url=result_url,
-    )

api/routes/results.py

@@ -1,39 +0,0 @@
-import os
-import re
-from pathlib import Path
-from typing import Dict
-
-from fastapi import APIRouter, HTTPException
-from fastapi.responses import FileResponse
-
-router = APIRouter()
-
-_RESULTS_DIR = Path("data/results").resolve()
-
-
-@router.get("/health")
-async def health_check() -> Dict[str, str]:
-    # Basic health check
-    return {"status": "ok", "model": "loaded", "db": "connected"}
-
-
-@router.get("/info")
-async def get_info() -> Dict[str, str]:
-    return {
-        "model_name": "xlm-roberta-base-absa",
-        "version": "1.0",
-        "supported_languages": "en, hi",
-        "max_batch_size": os.getenv("MAX_BATCH_SIZE", "10000"),
-    }
-
-
-@router.get("/download/{job_id}")
-async def download_result(job_id: str):
-    if not re.match(r"^[a-fA-F0-9\-]{36}$", job_id):
-        raise HTTPException(status_code=400, detail="Invalid job ID format")
-    resolved = (_RESULTS_DIR / f"{job_id}.csv").resolve()
-    if not str(resolved).startswith(str(_RESULTS_DIR)):
-        raise HTTPException(status_code=400, detail="Invalid job ID")
-    if not resolved.exists():
-        raise HTTPException(status_code=404, detail="Result file not found")
-    return FileResponse(path=resolved, filename=f"absa_results_{job_id}.csv", media_type="text/csv")

api/tasks/__init__.py

@@ -1,12 +0,0 @@
-import os
-
-from celery import Celery
-
-redis_url = os.getenv("REDIS_URL", "redis://localhost:6379/0")
-
-celery_app = Celery("absa_tasks", broker=redis_url, backend=redis_url.replace("/0", "/1"))
-
-celery_app.conf.update(
-    task_serializer="json",
-    result_expires=3600,
-)

api/tasks/batch_tasks.py

@@ -1,132 +0,0 @@
-import csv
-import os
-from datetime import datetime, timezone
-
-import pandas as pd
-
-from api.middleware.dependencies import SessionLocal
-from api.models.db_models import AspectResult, BatchJob, Review
-from api.services.absa_pipeline import pipeline
-from api.tasks import celery_app
-
-
-@celery_app.task(bind=True)
-def process_batch(self, job_id: str, file_path: str):
-    db = SessionLocal()
-    try:
-        job = db.query(BatchJob).filter(BatchJob.id == job_id).first()
-        if not job:
-            return
-
-        job.status = "processing"
-        db.commit()
-
-        # Load CSV
-        df = pd.read_csv(file_path)
-        if "text" not in df.columns:
-            raise ValueError("CSV must contain a 'text' column.")
-
-        texts = df["text"].tolist()
-        batch_size = 32
-
-        results_dir = "data/results"
-        os.makedirs(results_dir, exist_ok=True)
-        result_file = f"{results_dir}/{job_id}.csv"
-
-        processed_count = 0
-
-        with open(result_file, "w", newline="", encoding="utf-8") as f:
-            writer = csv.writer(f)
-            writer.writerow(
-                [
-                    "text",
-                    "language",
-                    "aspect",
-                    "sentiment",
-                    "confidence",
-                    "start_pos",
-                    "end_pos",
-                    "processing_time_ms",
-                ]
-            )
-
-            for i in range(0, len(texts), batch_size):
-                batch_texts = texts[i : i + batch_size]
-                predictions = pipeline.predict_batch(batch_texts)
-
-                for pred in predictions:
-                    # Save Review
-                    db_review = Review(
-                        text=pred.text,
-                        language=pred.language,
-                        processing_time_ms=pred.processing_time_ms,
-                    )
-                    db.add(db_review)
-                    db.commit()
-                    db.refresh(db_review)
-
-                    # Save Aspects & CSV
-                    for asp in pred.aspects:
-                        db_aspect = AspectResult(
-                            review_id=db_review.id,
-                            aspect=asp.aspect,
-                            sentiment=asp.sentiment,
-                            confidence=asp.confidence,
-                            start_pos=asp.start,
-                            end_pos=asp.end,
-                        )
-                        db.add(db_aspect)
-                        writer.writerow(
-                            [
-                                pred.text,
-                                pred.language,
-                                asp.aspect,
-                                asp.sentiment,
-                                asp.confidence,
-                                asp.start,
-                                asp.end,
-                                pred.processing_time_ms,
-                            ]
-                        )
-
-                    if not pred.aspects:
-                        writer.writerow(
-                            [
-                                pred.text,
-                                pred.language,
-                                "",
-                                "",
-                                "",
-                                "",
-                                "",
-                                pred.processing_time_ms,
-                            ]
-                        )
-
-                db.commit()
-                processed_count += len(batch_texts)
-
-                if processed_count % 100 == 0 or processed_count == len(texts):
-                    job.processed = processed_count
-                    db.commit()
-
-        job.status = "completed"
-        job.completed_at = datetime.now(timezone.utc)
-        db.commit()
-
-    except Exception:
-        job = db.query(BatchJob).filter(BatchJob.id == job_id).first()
-        if job:
-            job.status = "failed"
-            db.commit()
-        import logging
-
-        logging.exception("Batch processing failed for job %s", job_id)
-    finally:
-        # Clean up temp file
-        try:
-            if file_path and os.path.exists(file_path):
-                os.unlink(file_path)
-        except OSError:
-            pass
-        db.close()

app.py

@@ -0,0 +1,512 @@
+"""Single-file Gradio app for Multilingual ABSA.
+
+Loads the ONNX model (INT8 → FP32) with a rule-based fallback, runs inference
+via ``absa.pipeline.ABSAPipeline``, persists predictions to SQLite, and serves
+a Gradio (gr.Blocks) UI. Run with ``python app.py``.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import sys
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import gradio as gr
+from sqlalchemy import Column, DateTime, Float, Integer, String, create_engine
+from sqlalchemy.orm import declarative_base, sessionmaker
+
+# Make the src-layout `absa` package importable when run directly
+# (e.g. `python app.py`) without a prior `pip install -e .`. All `absa`
+# imports below are lazy, so this bootstrap runs before any of them.
+_SRC_DIR = Path(__file__).resolve().parent / "src"
+if str(_SRC_DIR) not in sys.path:
+    sys.path.insert(0, str(_SRC_DIR))
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
+_logger = logging.getLogger("absa.app")
+
+# ── SQLAlchemy / SQLite ───────────────────────────────────────────────────
+DB_PATH = "absa.db"
+engine = create_engine(
+    f"sqlite:///{DB_PATH}",
+    connect_args={"check_same_thread": False},
+)
+Base = declarative_base()
+Session = sessionmaker(bind=engine)
+
+
+def _utcnow() -> datetime:
+    return datetime.now(timezone.utc)
+
+
+class Prediction(Base):
+    """One aspect-sentiment pair extracted from a single review."""
+
+    __tablename__ = "predictions"
+
+    id = Column(Integer, primary_key=True)
+    text = Column(String, nullable=False)
+    language = Column(String, nullable=False)
+    aspect = Column(String, nullable=False)
+    sentiment = Column(String, nullable=False)
+    confidence = Column(Float, nullable=False)
+    created_at = Column(DateTime, default=_utcnow)
+
+
+Base.metadata.create_all(engine)
+
+# ── Lazy, failure-tolerant model loading ──────────────────────────────────
+# Module-level singleton (cached for the app lifetime). If the ONNX models
+# are missing the pipeline degrades to the rule-based engine — never crashes.
+
+DEFAULT_MODEL_PATH = Path(os.getenv("MODEL_PATH", "models/onnx"))
+
+
+def _load_pipeline() -> Any:
+    """Import the ABSA pipeline lazily and attempt custom ONNX model load."""
+    try:
+        from absa.pipeline.absa_pipeline import ABSAPipeline
+    except Exception as exc:  # pragma: no cover - import-time guard
+        _logger.error("Could not import ABSA pipeline: %s", exc)
+        raise
+
+    pipelin = ABSAPipeline()
+    try:
+        pipelin.load_models()
+    except Exception as exc:
+        _logger.warning("Custom model load failed (%s); using rule-based engine.", exc)
+    return pipelin
+
+
+PIPELINE = _load_pipeline()
+
+
+def _inference_mode() -> str:
+    """Report which engine is active: ONNX INT8 / ONNX FP32 / rule-based."""
+    if PIPELINE.aspect_model is not None:
+        if (DEFAULT_MODEL_PATH / "aspect_extraction_int8").exists():
+            return "ONNX INT8"
+        if (DEFAULT_MODEL_PATH / "aspect_extraction").exists():
+            return "ONNX FP32"
+        return "ONNX"
+    return "rule-based"
+
+
+MODE = _inference_mode()
+
+
+def get_count() -> int:
+    """Total predictions in SQLite."""
+    try:
+        session = Session()
+        return session.query(Prediction).count()
+    except Exception:
+        return 0
+    finally:
+        session.close()
+
+
+def get_history_rows() -> list[list]:
+    """Return last 50 predictions as DataFrame rows."""
+    try:
+        session = Session()
+        rows = session.query(Prediction).order_by(Prediction.id.desc()).limit(50).all()
+        return [
+            [
+                r.created_at.strftime("%Y-%m-%d %H:%M") if r.created_at else "—",
+                r.language or "—",
+                r.aspect or "—",
+                r.sentiment or "—",
+                round(r.confidence, 3) if r.confidence else 0.0,
+                (r.text[:60] + "...") if r.text and len(r.text) > 60 else (r.text or "—"),
+            ]
+            for r in rows
+        ]
+    except Exception:
+        return []
+    finally:
+        session.close()
+
+
+def get_history_md() -> str:
+    return f"Showing last 50 predictions  ·  Total: **{get_count()}**"
+
+
+def clear_history() -> tuple[str, list]:
+    """Wipe predictions table."""
+    try:
+        session = Session()
+        session.query(Prediction).delete()
+        session.commit()
+    except Exception:
+        pass
+    finally:
+        session.close()
+    return "History cleared  ·  Total: **0**", []
+
+
+def on_analyze(text: str) -> tuple[str, str, str, Any, list]:
+    """Run inference, save to DB, return formatted UI outputs."""
+    if not text or not text.strip():
+        return (
+            '<div class="meta-badges"><span class="badge">Detected Language: <strong>—</strong></span><span class="badge">Latency: <strong>—</strong></span></div>',
+            '<div class="summary-vis empty">Please enter a review to analyze.</div>',
+            '<div class="empty-state">No analysis yet.</div>',
+            gr.update(visible=False, value={})
+        )
+
+    start = time.time()
+    try:
+        response = PIPELINE.predict(text)
+        aspects = response.aspects if hasattr(response, "aspects") else response.get("aspects", [])
+        language = response.detected_language if hasattr(response, "detected_language") else response.get("detected_language", "unknown")
+        elapsed_ms = (time.time() - start) * 1000
+    except Exception as e:
+        return (
+            '<div class="meta-badges"><span class="badge">Detected Language: <strong>Error</strong></span><span class="badge">Latency: <strong>—</strong></span></div>',
+            '<div class="summary-vis empty">Error during analysis.</div>',
+            f'<div class="empty-state" style="color:#DC2626;">Unable to analyze this review: {str(e)[:120]}</div>',
+            gr.update(visible=False, value={})
+        )
+
+    lang_display = {
+        "en": "English", "english": "English",
+        "hi": "Hindi", "hindi": "Hindi",
+        "hinglish": "Hinglish", "hi-en": "Hinglish",
+    }.get(str(language).lower(), str(language).title())
+
+    meta_md = f'<div class="meta-badges"><span class="badge">Detected Language: <strong>{lang_display}</strong></span><span class="badge">Latency: <strong>{elapsed_ms:.0f} ms</strong></span></div>'
+
+    pos_count = neg_count = neu_count = 0
+    table_rows = ""
+    for a in aspects:
+        aspect_text = a.get("aspect", "") if isinstance(a, dict) else getattr(a, "aspect", "")
+        sentiment = a.get("sentiment", "neutral") if isinstance(a, dict) else getattr(a, "sentiment", "neutral")
+        confidence = float(a.get("confidence", 0.0) if isinstance(a, dict) else getattr(a, "confidence", 0.0))
+        
+        if sentiment == "positive":
+            pos_count += 1
+            sent_color = "#16A34A"
+            sent_label = "Positive"
+        elif sentiment == "negative":
+            neg_count += 1
+            sent_color = "#DC2626"
+            sent_label = "Negative"
+        else:
+            neu_count += 1
+            sent_color = "#64748B"
+            sent_label = "Neutral"
+            
+        conf_pct = int(confidence * 100)
+        
+        table_rows += f"""
+        <tr>
+            <td class="aspect-col">{aspect_text}</td>
+            <td class="sentiment-col">
+                <span class="sent-dot" style="background-color: {sent_color};"></span>
+                <span style="color: {sent_color}; font-weight: 600;">{sent_label}</span>
+            </td>
+            <td class="conf-col">
+                <div class="conf-bar-bg"><div class="conf-bar-fill" style="width: {conf_pct}%;"></div></div>
+                <span class="conf-val">{conf_pct}%</span>
+            </td>
+        </tr>
+        """
+
+    if not aspects:
+        results_html = '<div class="empty-state">No aspects detected in this review.</div>'
+    else:
+        results_html = f"""
+        <table class="results-table">
+            <thead>
+                <tr>
+                    <th>Aspect</th>
+                    <th>Sentiment</th>
+                    <th>Confidence</th>
+                </tr>
+            </thead>
+            <tbody>
+                {table_rows}
+            </tbody>
+        </table>
+        """
+
+    total = len(aspects)
+    
+    # Visualization blocks
+    vis_pos = f'Positive <span class="vis-block pos">{"█" * pos_count}</span> {pos_count}' if pos_count else 'Positive <span class="vis-block empty-block"></span> 0'
+    vis_neg = f'Negative <span class="vis-block neg">{"█" * neg_count}</span> {neg_count}' if neg_count else 'Negative <span class="vis-block empty-block"></span> 0'
+    vis_neu = f'Neutral <span class="vis-block neu">{"█" * neu_count}</span> {neu_count}' if neu_count else 'Neutral <span class="vis-block empty-block"></span> 0'
+
+    summary_md = f"""
+    <div class="summary-vis">
+        <div class="summary-title">{total} Aspects</div>
+        <div class="vis-row">{vis_pos}</div>
+        <div class="vis-row">{vis_neg}</div>
+        <div class="vis-row">{vis_neu}</div>
+    </div>
+    """
+
+    # Save to SQLite
+    try:
+        session = Session()
+        for a in aspects:
+            aspect_text = a.get("aspect", "") if isinstance(a, dict) else getattr(a, "aspect", "")
+            sentiment = a.get("sentiment", "neutral") if isinstance(a, dict) else getattr(a, "sentiment", "neutral")
+            confidence = a.get("confidence", 0.0) if isinstance(a, dict) else getattr(a, "confidence", 0.0)
+            session.add(Prediction(
+                text=text,
+                language=lang_display,
+                aspect=aspect_text,
+                sentiment=sentiment,
+                confidence=float(confidence or 0.0),
+            ))
+        session.commit()
+    except Exception:
+        pass
+    finally:
+        session.close()
+
+    return (meta_md, summary_md, results_html, gr.update(visible=True, value=aspects))
+
+
+# ── Gradio UI (gr.Blocks) ─────────────────────────────────────────────────
+
+custom_theme = gr.themes.Soft(
+    primary_hue=gr.themes.colors.blue,
+    secondary_hue=gr.themes.colors.slate,
+    neutral_hue=gr.themes.colors.slate,
+    font=gr.themes.GoogleFont("Inter"),
+).set(
+    body_background_fill="#F8FAFC",
+    body_background_fill_dark="#F8FAFC",
+    body_text_color="#0F172A",
+    body_text_color_dark="#0F172A",
+    block_background_fill="#FFFFFF",
+    block_background_fill_dark="#FFFFFF",
+    block_border_color="#E2E8F0",
+    block_border_color_dark="#E2E8F0",
+    block_label_background_fill="#FFFFFF",
+    block_label_background_fill_dark="#FFFFFF",
+    block_label_text_color="#0F172A",
+    block_label_text_color_dark="#0F172A",
+    button_primary_background_fill="#2563EB",
+    button_primary_background_fill_dark="#2563EB",
+    button_primary_text_color="#FFFFFF",
+    button_primary_text_color_dark="#FFFFFF",
+    button_primary_border_color="#2563EB",
+    button_primary_border_color_dark="#2563EB",
+    button_secondary_background_fill="#FFFFFF",
+    button_secondary_background_fill_dark="#FFFFFF",
+    button_secondary_text_color="#0F172A",
+    button_secondary_text_color_dark="#0F172A",
+    button_secondary_border_color="#CBD5E1",
+    button_secondary_border_color_dark="#CBD5E1",
+    input_background_fill="#FFFFFF",
+    input_background_fill_dark="#FFFFFF",
+    input_border_color="#CBD5E1",
+    input_border_color_dark="#CBD5E1",
+    panel_background_fill="#FFFFFF",
+    panel_background_fill_dark="#FFFFFF",
+    table_even_background_fill="#F8FAFC",
+    table_even_background_fill_dark="#F8FAFC",
+    table_odd_background_fill="#FFFFFF",
+    table_odd_background_fill_dark="#FFFFFF",
+    table_border_color="#E2E8F0",
+    table_border_color_dark="#E2E8F0",
+    border_color_primary="#E2E8F0",
+    border_color_primary_dark="#E2E8F0",
+    color_accent_soft="#EFF6FF",
+    color_accent_soft_dark="#EFF6FF",
+)
+
+with gr.Blocks(title="Multilingual ABSA") as demo:
+
+    # TOP HEADER
+    with gr.Row(elem_classes="top-header"):
+        with gr.Column(scale=3, elem_classes="header-title-col"):
+            gr.Markdown("""
+            <div class="header-content">
+                <h1>Multilingual ABSA</h1>
+                <div class="header-subtitle">Aspect-Based Sentiment Analysis for English, Hindi, and Hinglish</div>
+            </div>
+            """)
+        with gr.Column(scale=2, elem_classes="header-metrics-col"):
+            gr.Markdown(f"""
+            <div class="header-metrics">
+                <div class="metric"><span class="m-label">MODE</span><span class="m-val">{MODE}</span></div>
+                <div class="metric"><span class="m-label">LANGUAGES</span><span class="m-val">English · Hindi · Hinglish</span></div>
+                <div class="metric"><span class="m-label">PREDICTIONS</span><span class="m-val">{get_count()}</span></div>
+                <div class="metric"><span class="m-label">STATUS</span><span class="m-val"><span class="status-dot">●</span> Ready</span></div>
+            </div>
+            """)
+
+    # WORKSPACE
+    with gr.Tabs(elem_classes="main-tabs"):
+        with gr.Tab("Workspace"):
+            with gr.Row(elem_classes="workspace-row"):
+                
+                # LEFT PANEL
+                with gr.Column(scale=1, elem_classes="panel panel-left"):
+                    gr.Markdown('<div class="section-title">Analyze Review</div><div class="section-sub">Enter a product review to identify aspects and their sentiment.</div>')
+                    
+                    inp = gr.Textbox(
+                        label="",
+                        placeholder="Example: The battery life is amazing, but the camera quality is poor.",
+                        lines=6,
+                        max_lines=15,
+                        elem_classes="review-input",
+                        show_label=False,
+                    )
+                    
+                    btn = gr.Button("Analyze Review", variant="primary", elem_classes="analyze-btn")
+                    
+                    gr.Markdown('<div class="section-title" style="margin-top: 32px; font-size: 0.85rem;">Examples</div>')
+                    gr.Examples(
+                        examples=[
+                            ["The battery life is amazing but the camera quality is poor."],
+                            ["बैटरी बहुत अच्छी है लेकिन कैमरा क्वालिटी खराब है।"],
+                            ["Battery life mast hai lekin camera quality bahut bekar hai."],
+                        ],
+                        inputs=inp,
+                        label=""
+                    )
+
+                # RIGHT PANEL
+                with gr.Column(scale=1, elem_classes="panel panel-right"):
+                    gr.Markdown('<div class="section-title">Analysis Results</div>')
+                    
+                    meta_md = gr.Markdown('<div class="meta-badges"><span class="badge">Detected Language: <strong>Waiting</strong></span><span class="badge">Latency: <strong>—</strong></span></div>')
+                    summary_md = gr.Markdown('<div class="summary-vis empty">Waiting for analysis...</div>')
+                    results_html = gr.HTML('<div class="empty-state">No analysis yet.</div>')
+                    
+                    with gr.Accordion("Raw Model Output", open=False, elem_classes="raw-accordion"):
+                        json_out = gr.JSON(label="", visible=False, elem_classes="raw-json")
+                    
+            btn.click(
+                fn=lambda: (
+                    '<div class="meta-badges"><span class="badge">Detected Language: <strong>Analyzing...</strong></span><span class="badge">Latency: <strong>—</strong></span></div>',
+                    '<div class="summary-vis empty">Analyzing review...</div>',
+                    '<div class="empty-state">Analyzing...</div>'
+                ),
+                outputs=[meta_md, summary_md, results_html]
+            ).then(
+                fn=on_analyze, 
+                inputs=inp, 
+                outputs=[meta_md, summary_md, results_html, json_out]
+            )
+
+        # HISTORY TAB
+        with gr.Tab("History"):
+            with gr.Row():
+                refresh_btn = gr.Button("Refresh Data", size="sm", variant="secondary")
+                clear_btn = gr.Button("Clear History", size="sm", variant="secondary")
+                
+            history_df = gr.Dataframe(
+                headers=["Timestamp", "Language", "Aspect", "Sentiment", "Confidence", "Review Preview"],
+                datatype=["str", "str", "str", "str", "number", "str"],
+                value=get_history_rows(),
+                interactive=False,
+                wrap=True,
+                elem_classes="history-table"
+            )
+            
+            # Now we can attach the `.then` to update history_df
+            btn.click(fn=lambda: None).then(fn=get_history_rows, outputs=history_df)
+            
+            refresh_btn.click(fn=get_history_rows, outputs=history_df)
+            clear_btn.click(fn=clear_history, outputs=[history_df]).then(fn=get_history_rows, outputs=history_df)
+
+custom_css = """
+    /* 60/30/10 Design System */
+    
+    /* Layout & Base */
+    .gradio-container { max-width: 1200px !important; margin: auto; font-family: 'Inter', sans-serif; padding: 24px !important; }
+    
+    /* Typography */
+    .section-title { font-size: 1.1rem; font-weight: 600; color: #0F172A; margin-bottom: 4px; }
+    .section-sub { font-size: 0.9rem; color: #64748B; margin-bottom: 16px; }
+    
+    /* Top Header */
+    .top-header { border-bottom: 1px solid #E2E8F0; padding-bottom: 16px; margin-bottom: 24px; align-items: center; }
+    .header-content h1 { font-size: 1.6rem; font-weight: 600; color: #0F172A; margin: 0 0 4px 0; letter-spacing: -0.02em; }
+    .header-subtitle { font-size: 0.9rem; color: #64748B; }
+    
+    /* Header Metrics */
+    .header-metrics { display: flex; gap: 24px; justify-content: flex-end; flex-wrap: wrap; }
+    .metric { display: flex; flex-direction: column; }
+    .m-label { font-size: 0.65rem; font-weight: 600; color: #64748B; text-transform: uppercase; letter-spacing: 0.05em; margin-bottom: 4px; }
+    .m-val { font-size: 0.85rem; font-weight: 500; color: #0F172A; }
+    .status-dot { color: #16A34A; margin-right: 4px; }
+    
+    /* Panels */
+    .workspace-row { gap: 32px !important; align-items: flex-start !important; }
+    .panel { background: #FFFFFF; border: 1px solid #E2E8F0; border-radius: 8px; padding: 24px; box-shadow: 0 1px 2px rgba(0,0,0,0.02); }
+    
+    /* Review Input */
+    .review-input textarea { border: 1px solid #CBD5E1 !important; border-radius: 6px !important; padding: 12px !important; font-size: 0.95rem !important; line-height: 1.5 !important; background: #FFFFFF !important; color: #0F172A !important; transition: border-color 0.2s; box-shadow: none !important; }
+    .review-input textarea:focus { border-color: #2563EB !important; ring: 1px solid #2563EB !important; }
+    .analyze-btn { background: #2563EB !important; color: #FFFFFF !important; font-weight: 500 !important; border-radius: 6px !important; margin-top: 16px !important; padding: 10px 0 !important; border: none !important; transition: background 0.2s !important; }
+    .analyze-btn:hover { background: #1D4ED8 !important; }
+    
+    /* Examples */
+    .compact-examples .gallery { gap: 8px !important; }
+    .compact-examples button { border: 1px solid #E2E8F0 !important; border-radius: 6px !important; padding: 8px 12px !important; font-size: 0.8rem !important; background: #F8FAFC !important; color: #475569 !important; text-align: left !important; }
+    .compact-examples button:hover { background: #F1F5F9 !important; border-color: #CBD5E1 !important; }
+    
+    /* Meta Badges */
+    .meta-badges { display: flex; gap: 12px; margin-bottom: 24px; }
+    .badge { font-size: 0.75rem; background: #F1F5F9; color: #475569; padding: 4px 10px; border-radius: 4px; border: 1px solid #E2E8F0; }
+    .badge strong { color: #0F172A; font-weight: 600; margin-left: 4px; }
+    
+    /* Summary Vis */
+    .summary-vis { margin-bottom: 24px; font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace; font-size: 0.8rem; }
+    .summary-title { font-family: 'Inter', sans-serif; font-size: 0.9rem; font-weight: 600; color: #0F172A; margin-bottom: 12px; border-bottom: 1px solid #E2E8F0; padding-bottom: 8px; }
+    .vis-row { margin-bottom: 6px; display: flex; align-items: center; gap: 12px; color: #475569; }
+    .vis-block { letter-spacing: -1px; font-size: 0.7rem; }
+    .vis-block.pos { color: #16A34A; }
+    .vis-block.neg { color: #DC2626; }
+    .vis-block.neu { color: #64748B; }
+    .vis-block.empty-block { visibility: hidden; width: 10px; }
+    .summary-vis.empty { font-family: 'Inter', sans-serif; color: #64748B; font-style: italic; }
+    
+    /* Results Table */
+    .results-table { width: 100%; border-collapse: collapse; margin-bottom: 16px; font-size: 0.85rem; }
+    .results-table th { text-align: left; padding: 8px 12px; border-bottom: 1px solid #E2E8F0; color: #64748B; font-weight: 600; font-size: 0.75rem; text-transform: uppercase; letter-spacing: 0.05em; }
+    .results-table td { padding: 12px; border-bottom: 1px solid #F1F5F9; color: #0F172A; }
+    .aspect-col { font-weight: 500; }
+    .sentiment-col { display: flex; align-items: center; gap: 6px; }
+    .sent-dot { width: 8px; height: 8px; border-radius: 50%; display: inline-block; }
+    
+    /* Confidence Bar */
+    .conf-col { min-width: 120px; }
+    .conf-bar-bg { width: 60px; height: 6px; background: #E2E8F0; border-radius: 3px; display: inline-block; vertical-align: middle; margin-right: 8px; overflow: hidden; }
+    .conf-bar-fill { height: 100%; background: #2563EB; border-radius: 3px; }
+    .conf-val { font-family: ui-monospace, monospace; font-size: 0.8rem; color: #475569; }
+    
+    .empty-state { padding: 32px 0; text-align: center; color: #64748B; font-size: 0.9rem; border: 1px dashed #CBD5E1; border-radius: 6px; }
+    
+    /* Raw Output Accordion */
+    .raw-accordion { margin-top: 24px !important; border: 1px solid #E2E8F0 !important; border-radius: 6px !important; overflow: hidden !important; background: #FFFFFF !important; }
+    .raw-accordion .label-wrap { padding: 12px 16px !important; font-size: 0.85rem !important; font-weight: 600 !important; color: #0F172A !important; background: #F8FAFC !important; border-bottom: 1px solid #E2E8F0 !important; }
+    .raw-accordion .raw-json { background: #0F172A !important; padding: 16px !important; margin: 0 !important; }
+    .raw-accordion .raw-json * { color: #F8FAFC !important; font-family: ui-monospace, SFMono-Regular, monospace !important; font-size: 0.8rem !important; }
+    
+    /* History Table */
+    .history-table { border: 1px solid #E2E8F0 !important; border-radius: 8px !important; overflow: hidden !important; margin-top: 16px !important; }
+    .history-table th { background: #F8FAFC !important; color: #475569 !important; font-size: 0.75rem !important; text-transform: uppercase !important; font-weight: 600 !important; }
+    
+    /* Mobile Stacking */
+    @media (max-width: 768px) {
+        .workspace-row { flex-direction: column !important; }
+        .panel { width: 100% !important; }
+        .header-metrics { justify-content: flex-start; margin-top: 16px; }
+    }
+"""
+
+if __name__ == "__main__":
+    demo.launch(theme=custom_theme, css=custom_css)

coverage.xml

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+				<class name="schemas.py" filename="src/absa/pipeline/schemas.py" complexity="0" line-rate="1" branch-rate="0">
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data/.gitignore

@@ -1 +0,0 @@
-/raw

data/demo/demo_single_reviews.txt

@@ -1,5 +0,0 @@
-The phone has an amazing screen but the battery life is terrible.
-I absolutely love the camera quality on this device.
-फोन की बैटरी अच्छी है लेकिन कैमरा बेकार है
-खाना बहुत स्वादिष्ट था, पर सर्विस थोड़ी स्लो थी
-Design mast hai par price bahut high hai

data/demo/sample_reviews.csv

@@ -1,21 +0,0 @@
-text,language
-"The phone has an amazing screen but the battery life is terrible.",en
-"I absolutely love the camera quality on this device.",en
-"Shipping was very fast, but the customer service was useless when I had a question.",en
-"The laptop keyboard feels incredibly cheap, though it runs fast.",en
-"Amazing food, terrible atmosphere.",en
-"Best purchase I made this year. High quality materials.",en
-"The speakers are loud but the bass is non-existent.",en
-"Wait staff was friendly, food was completely cold.",en
-"I like the design but the software has too many bugs.",en
-"Great value for money, highly recommended.",en
-"फोन की बैटरी अच्छी है लेकिन कैमरा बेकार है",hi
-"मुझे इस लैपटॉप की स्क्रीन बहुत पसंद आई",hi
-"डिलीवरी बहुत लेट थी और पैकिंग भी खराब थी",hi
-"खाना बहुत स्वादिष्ट था, पर सर्विस थोड़ी स्लो थी",hi
-"यह प्रोडक्ट पैसे की बर्बादी है",hi
-"Design mast hai par price bahut high hai",hinglish
-"Screen quality ekdum awesome hai bhai",hinglish
-"Customer support ne help nahi ki, very bad experience",hinglish
-"Look and feel to accha hai but performance thik thak hai",hinglish
-"Battery drain jaldi hota hai, overall not good",hinglish

data/models/lid.176.ftz

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data/raw.dvc

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-  size: 7943297
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data/tokenized/absa_cls_dataset/dataset_dict.json

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-{"splits": ["train", "validation", "test"]}

data/tokenized/absa_cls_dataset/test/data-00000-of-00001.arrow

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data/tokenized/absa_cls_dataset/test/dataset_info.json

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-{
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data/tokenized/absa_cls_dataset/test/state.json

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-{
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data/tokenized/absa_cls_dataset/train/data-00000-of-00001.arrow

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-{
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data/tokenized/absa_cls_dataset/train/state.json

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-{
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data/tokenized/absa_cls_dataset/validation/data-00000-of-00001.arrow

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data/tokenized/absa_cls_dataset/validation/state.json

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-{
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data/tokenized/absa_ner_dataset/dataset_dict.json

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data/tokenized/absa_ner_dataset/test/data-00000-of-00001.arrow

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data/tokenized/absa_ner_dataset/test/state.json

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data/tokenized/absa_ner_dataset/validation/data-00000-of-00001.arrow

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docker/Dockerfile

@@ -1,42 +0,0 @@
-# Stage 1: Builder
-FROM python:3.11-slim AS builder
-
-WORKDIR /app
-COPY pyproject.toml .
-COPY src ./src
-COPY api ./api
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-RUN pip install --no-cache-dir --prefix=/install .
-
-# Stage 2: Runtime
-FROM python:3.11-slim
-
-WORKDIR /app
-
-# Copy dependencies
-COPY --from=builder /install /usr/local
-
-# Copy application code
-COPY api /app/api
-COPY src /app/src
-COPY scripts /app/scripts
-COPY docker /app/docker
-COPY .env.example /app/.env.example
-# .env is injected via docker-compose environment vars — no need to COPY it
-
-ENV PYTHONPATH=/app/src
-
-# Add a non-root user
-RUN adduser --disabled-password --gecos "" absauser \
-    && chown -R absauser /app
-
-USER absauser
-
-EXPOSE 8000
-
-CMD ["uvicorn", "api.main:app", "--host", "0.0.0.0", "--port", "8000"]

docker/Dockerfile.prod

@@ -1,19 +0,0 @@
-FROM python:3.11-slim as builder
-WORKDIR /app
-COPY pyproject.toml .
-COPY src ./src
-COPY api ./api
-RUN pip install --no-cache-dir .
-
-FROM python:3.11-slim as runtime
-WORKDIR /app
-COPY --from=builder /usr/local/lib/python3.11 /usr/local/lib/python3.11
-COPY --from=builder /usr/local/bin /usr/local/bin
-COPY api/ ./api/
-COPY src/ ./src/
-ENV PYTHONPATH=/app/src
-ENV MODEL_SOURCE=huggingface_hub
-RUN useradd -m appuser && chown -R appuser /app
-USER appuser
-EXPOSE 8000
-CMD ["uvicorn", "api.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "2"]

docker/docker-compose.prod.yml

@@ -1,16 +0,0 @@
-version: "3.9"
-services:
-  api:
-    image: ghcr.io/YOUR_USERNAME/multilingual-absa/api:latest
-    restart: always
-    environment:
-      - LOG_LEVEL=WARNING
-    deploy:
-      resources:
-        limits:
-          memory: 2G
-  worker:
-    image: ghcr.io/YOUR_USERNAME/multilingual-absa/api:latest
-    command: celery -A api.tasks.batch_tasks worker --loglevel=warning
-    restart: always
-