Initial commit for Hugging Face

.dockerignore

@@ -0,0 +1,11 @@
+.git
+.gitignore
+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+*.log
+.env
+research/
+*.ipynb

.github/workflows/cicd.yaml

@@ -0,0 +1,58 @@
+name: Deploy Application Docker Image to EC2 instance
+
+on:
+  push:
+    branches: [main]
+
+
+jobs:
+  Continuous-Integration:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ secrets.AWS_DEFAULT_REGION }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v1
+
+      - name: Build, tag, and push image to Amazon ECR
+        id: build-image
+        env:
+          ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
+          ECR_REPOSITORY: ${{ secrets.ECR_REPO }}
+          IMAGE_TAG: latest
+        run: |
+          docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG .  
+          docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
+          echo "::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG"
+
+  Continuous-Deployment:
+    needs: Continuous-Integration
+    runs-on: self-hosted
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ secrets.AWS_DEFAULT_REGION }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v1
+
+      - name: Run Docker Image to serve users
+        run: |
+         docker run -d -e AWS_ACCESS_KEY_ID="${{ secrets.AWS_ACCESS_KEY_ID }}" -e AWS_SECRET_ACCESS_KEY="${{ secrets.AWS_SECRET_ACCESS_KEY }}" -e AWS_DEFAULT_REGION="${{ secrets.AWS_DEFAULT_REGION }}" -e PINECONE_API_KEY="${{ secrets.PINECONE_API_KEY }}" -e OPENAI_API_KEY="${{ secrets.OPENAI_API_KEY }}" -p 8080:8080 "${{ steps.login-ecr.outputs.registry }}"/"${{ secrets.ECR_REPO }}":latest

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+.env
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+#poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+#pdm.lock
+#pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+#pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# data
+data/
+*.pdf stuff
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Abstra
+# Abstra is an AI-powered process automation framework.
+# Ignore directories containing user credentials, local state, and settings.
+# Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#  Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#  that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#  and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#  you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Cursor
+#  Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to
+#  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
+#  refer to https://docs.cursor.com/context/ignore-files
+.cursorignore
+.cursorindexingignore
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/

Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY . /app
+
+RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu && \
+    pip install --no-cache-dir -r requirements.txt
+
+EXPOSE 7860
+
+CMD ["sh", "-c", "gunicorn --bind 0.0.0.0:${PORT:-7860} --timeout 120 app:app"]

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -0,0 +1,218 @@
+---
+title: Student Support Chatbot
+emoji: 🎓
+colorFrom: purple
+colorTo: indigo
+sdk: docker
+pinned: false
+---
+
+# 🎓 Student Support Chatbot
+
+A fully deployed AI-powered RAG (Retrieval-Augmented Generation) chatbot that answers student queries about admissions, fees, courses, campus life, and more. Built with LangChain, Google Gemini, Pinecone, and Flask.
+
+---
+
+## 🏗️ Architecture
+
+```
+Student Question
+      ↓
+HuggingFace Embeddings (all-MiniLM-L6-v2)
+      ↓
+Pinecone Vector DB (semantic search → top 3 results)
+      ↓
+Google Gemini LLM (generates final answer)
+      ↓
+Flask Web App (chat interface)
+```
+
+**Dataset**: [`bot-remains/student-assistance-chatbot`](https://huggingface.co/datasets/bot-remains/student-assistance-chatbot) — 217 student Q&A pairs covering admissions, eligibility, fees, academics, and campus life.
+
+---
+
+## 🛠️ Tech Stack
+
+| Layer           | Technology                           |
+| --------------- | ------------------------------------ |
+| LLM             | Google Gemini 2.5 Flash              |
+| Embeddings      | HuggingFace `all-MiniLM-L6-v2`       |
+| Vector Database | Pinecone                             |
+| Framework       | LangChain                            |
+| Web App         | Flask                                |
+| Deployment      | AWS App Runner + Amazon ECR / Docker |
+
+---
+
+## 🚀 How to Run Locally
+
+### Step 1: Clone the repository
+
+```bash
+git clone https://github.com/aak007/Build-a-Complete-Medical-Chatbot-with-LLMs-LangChain-Pinecone-Flask-AWS.git
+cd Build-a-Complete-Medical-Chatbot-with-LLMs-LangChain-Pinecone-Flask-AWS
+```
+
+### Step 2: Create and activate conda environment
+
+```bash
+conda create -n medibot python=3.10 -y
+conda activate medibot
+```
+
+### Step 3: Install dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### Step 4: Set up environment variables
+
+Create a `.env` file in the root directory:
+
+```ini
+PINECONE_API_KEY=your_pinecone_api_key
+GOOGLE_API_KEY=your_gemini_api_key
+
+# Optional: choose your index name
+PINECONE_INDEX_NAME=student-chatbot
+
+# Dataset source options: hf | local | pdf
+DATA_SOURCE=hf
+
+# If DATA_SOURCE=hf
+HF_DATASET_NAME=bot-remains/student-assistance-chatbot
+
+# If DATA_SOURCE=local (csv/json/jsonl/txt/md)
+LOCAL_DATASET_PATH=data/my_dataset.csv
+TEXT_COLUMNS=question,answer
+
+# If DATA_SOURCE=pdf (single file or folder)
+# LOCAL_DATASET_PATH=data/student_buddy_qa.pdf
+```
+
+### Step 5: Index the dataset into Pinecone _(Run only once)_
+
+```bash
+python store_index.py
+```
+
+This downloads the student Q&A dataset from HuggingFace and stores it as vector embeddings in your Pinecone `student-chatbot` index.
+
+### Use Your Own Dataset
+
+You can replace the default dataset with your own data and re-index it.
+
+1. Put your file inside the project, for example `data/my_dataset.csv`.
+2. Update `.env`:
+
+```ini
+DATA_SOURCE=local
+LOCAL_DATASET_PATH=data/my_dataset.csv
+TEXT_COLUMNS=question,answer
+PINECONE_INDEX_NAME=my-student-bot-index
+```
+
+3. Rebuild vectors:
+
+```bash
+python store_index.py
+```
+
+4. Start app:
+
+```bash
+python app.py
+```
+
+Supported local file formats:
+
+- `csv` (recommended)
+- `json` (array of objects)
+- `jsonl` (one JSON object per line)
+- `txt` / `md` (split by blank lines)
+
+For PDF knowledge bases:
+
+```ini
+DATA_SOURCE=pdf
+LOCAL_DATASET_PATH=data/student_buddy_qa.pdf
+PINECONE_INDEX_NAME=student-buddy-index
+```
+
+Then run:
+
+```bash
+python store_index.py
+python app.py
+```
+
+Example CSV schema:
+
+```csv
+question,answer,category
+What is eligibility for B.Tech?,Candidates must pass 10+2 with PCM and required cutoff,admissions
+What are hostel fees?,Hostel fees vary by room type and campus policy,fees
+```
+
+### Step 6: Start the app
+
+```bash
+python app.py
+```
+
+### Step 7: Open in browser
+
+```
+http://localhost:7860
+```
+
+---
+
+## ☁️ Deployment
+
+### AWS App Runner + ECR (Recommended)
+
+```bash
+# 1. Authenticate Docker to AWS ECR
+aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <account-id>.dkr.ecr.us-east-1.amazonaws.com
+
+# 2. Build Docker image
+docker build --no-cache -t medical-bot .
+
+# 3. Tag image
+docker tag medical-bot:latest <account-id>.dkr.ecr.us-east-1.amazonaws.com/medical-bot:latest
+
+# 4. Push to ECR
+docker push <account-id>.dkr.ecr.us-east-1.amazonaws.com/medical-bot:latest
+```
+
+Then deploy via AWS App Runner, set **port 7860**, and add your `PINECONE_API_KEY` and `GOOGLE_API_KEY` as environment variables.
+
+---
+
+## 📁 Project Structure
+
+```
+├── app.py                  # Flask web server
+├── store_index.py          # Downloads dataset & indexes into Pinecone
+├── Dockerfile              # Docker config for deployment
+├── requirements.txt        # Python dependencies
+├── setup.py
+├── src/
+│   ├── helper.py           # load_hf_dataset(), text_split(), embeddings
+│   └── prompt.py           # System prompt for student support
+├── static/
+│   └── style.css           # Premium student-themed UI
+└── templates/
+    └── chat.html           # Chat interface
+```
+
+---
+
+## 🔑 Required API Keys
+
+| Key                | Where to Get                                                  |
+| ------------------ | ------------------------------------------------------------- |
+| `PINECONE_API_KEY` | [app.pinecone.io](https://app.pinecone.io/)                   |
+| `GOOGLE_API_KEY`   | [aistudio.google.com](https://aistudio.google.com/app/apikey) |

app.py

@@ -0,0 +1,141 @@
+from flask import Flask, render_template, jsonify, request
+from src.helper import download_hugging_face_embeddings
+from langchain_pinecone import PineconeVectorStore
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from dotenv import load_dotenv
+from src.prompt import *
+import os
+import traceback
+
+
+app = Flask(__name__)
+
+
+load_dotenv(override=True)
+
+PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
+GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
+
+os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
+if GOOGLE_API_KEY:
+    os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
+
+
+embeddings = download_hugging_face_embeddings()
+
+index_name = os.environ.get("PINECONE_INDEX_NAME", "student-chatbot")
+# Embed each chunk and upsert the embeddings into your Pinecone index.
+docsearch = PineconeVectorStore.from_existing_index(
+    index_name=index_name, embedding=embeddings
+)
+
+
+retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+
+chatModel = ChatGoogleGenerativeAI(
+    model="gemini-2.5-flash",
+    temperature=0,
+    max_retries=2,
+)
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        ("human", "{input}"),
+    ]
+)
+
+question_answer_chain = create_stuff_documents_chain(chatModel, prompt)
+rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+
+def build_context_fallback_answer(user_query: str) -> str:
+    """Return a best-effort answer using retrieved context only (no LLM call)."""
+    try:
+        docs = retriever.invoke(user_query)
+    except Exception:
+        return "Gemini quota is reached, and I could not fetch context right now. Please try again shortly."
+
+    if not docs:
+        return "Gemini quota is reached, and I could not find relevant context for this question right now."
+
+    top_doc_text = (docs[0].page_content or "").strip()
+    if not top_doc_text:
+        return "Gemini quota is reached, but retrieved context is empty. Please try again later."
+
+    answer_line = None
+    for line in top_doc_text.splitlines():
+        if line.lower().startswith("answer:"):
+            answer_line = line.split(":", 1)[1].strip()
+            break
+
+    if answer_line:
+        return f"Gemini quota reached, so I am answering from stored context: {answer_line}"
+
+    snippet = " ".join(
+        part.strip() for part in top_doc_text.splitlines() if part.strip()
+    )
+    snippet = snippet[:450]
+    return "Gemini quota reached, so I am answering from stored context: " f"{snippet}"
+
+
+@app.route("/")
+def index():
+    return render_template("chat.html")
+
+
+@app.route("/get", methods=["GET", "POST"])
+def chat():
+    msg = request.values.get("msg", "").strip()
+
+    if not msg:
+        return "Please enter a question.", 200
+
+    print(msg)
+
+    if not GOOGLE_API_KEY:
+        return (
+            "GOOGLE_API_KEY is missing. Add it to your .env file and restart the app.",
+            200,
+        )
+
+    try:
+        response = rag_chain.invoke({"input": msg})
+        answer = response.get("answer") if isinstance(response, dict) else None
+        if not answer:
+            return (
+                "I could not generate a response right now. Please try rephrasing your question.",
+                200,
+            )
+
+        print("Response : ", answer)
+        return str(answer), 200
+    except Exception as e:
+        print("Error: ", str(e))
+        traceback.print_exc()
+
+        error_text = str(e).lower()
+        if (
+            "api key" in error_text
+            or "permission" in error_text
+            or "unauthorized" in error_text
+        ):
+            return (
+                "Your Gemini API key is invalid or missing permissions. Please verify GOOGLE_API_KEY.",
+                200,
+            )
+        if "quota" in error_text or "rate" in error_text or "429" in error_text:
+            fallback_answer = build_context_fallback_answer(msg)
+            return fallback_answer, 200
+
+        return (
+            "I am having trouble reaching the AI service right now. Please try again in a few seconds.",
+            200,
+        )
+
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    app.run(host="0.0.0.0", port=port, debug=False)

requirements.txt

@@ -0,0 +1,12 @@
+datasets
+langchain>=0.3.0,<0.4.0
+flask>=3.0.0
+sentence-transformers>=3.0.0
+pypdf>=4.0.0
+python-dotenv>=1.0.0
+langchain-pinecone>=0.2.0
+langchain-openai>=0.2.0
+langchain-google-genai>=2.0.0
+langchain-community>=0.3.0,<0.4.0
+gunicorn>=22.0.0
+-e .

setup.py

@@ -0,0 +1,10 @@
+from setuptools import find_packages, setup
+
+setup(
+    name="medical_chatbot",
+    version="0.1.0",
+    author="Boktiar Ahmed Bappy",
+    author_email="entbappy73@gmail.com",
+    packages=find_packages(),
+    install_requires=[]
+)

src/helper.py

@@ -0,0 +1,202 @@
+from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_core.documents import Document
+from datasets import load_dataset
+from typing import List, Optional, Dict, Any
+import csv
+import json
+from pathlib import Path
+
+
+# Extract Data From PDF file(s)
+def load_pdf_file(data_path: str) -> List[Document]:
+    path = Path(data_path)
+    if not path.exists():
+        raise FileNotFoundError(f"PDF path not found: {data_path}")
+
+    if path.is_file():
+        if path.suffix.lower() != ".pdf":
+            raise ValueError(
+                "When DATA_SOURCE=pdf and path is a file, it must be a .pdf file"
+            )
+        return PyPDFLoader(str(path)).load()
+
+    loader = DirectoryLoader(str(path), glob="*.pdf", loader_cls=PyPDFLoader)
+    return loader.load()
+
+
+# Load Student Q&A Dataset from Hugging Face
+def load_hf_dataset(dataset_name: str) -> List[Document]:
+    dataset = load_dataset(dataset_name, split="train")
+    documents = []
+    for row in dataset:
+        # Build a rich text blob from all available fields in the row
+        content_parts = []
+        for key, value in row.items():
+            if value:
+                content_parts.append(f"{key.capitalize()}: {value}")
+        page_content = "\n".join(content_parts)
+        documents.append(
+            Document(page_content=page_content, metadata={"source": dataset_name})
+        )
+    return documents
+
+
+def _parse_columns(columns: Optional[str]) -> Optional[List[str]]:
+    if not columns:
+        return None
+    parsed = [col.strip() for col in columns.split(",") if col.strip()]
+    return parsed or None
+
+
+def _row_to_document(
+    row: Dict[str, Any],
+    source_name: str,
+    text_columns: Optional[List[str]] = None,
+) -> Optional[Document]:
+    content_parts: List[str] = []
+
+    if text_columns:
+        for key in text_columns:
+            value = row.get(key)
+            if value is not None and str(value).strip():
+                content_parts.append(f"{key.capitalize()}: {value}")
+    else:
+        for key, value in row.items():
+            if value is not None and str(value).strip():
+                content_parts.append(f"{key.capitalize()}: {value}")
+
+    if not content_parts:
+        return None
+
+    return Document(
+        page_content="\n".join(content_parts),
+        metadata={"source": source_name},
+    )
+
+
+def load_local_dataset(
+    local_path: str, text_columns: Optional[str] = None
+) -> List[Document]:
+    path = Path(local_path)
+    if not path.exists():
+        raise FileNotFoundError(f"Dataset file not found: {local_path}")
+
+    parsed_text_columns = _parse_columns(text_columns)
+    ext = path.suffix.lower()
+    documents: List[Document] = []
+
+    if ext == ".csv":
+        with path.open("r", encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                doc = _row_to_document(
+                    row, source_name=str(path.name), text_columns=parsed_text_columns
+                )
+                if doc:
+                    documents.append(doc)
+        return documents
+
+    if ext == ".json":
+        with path.open("r", encoding="utf-8") as f:
+            payload = json.load(f)
+
+        if isinstance(payload, dict):
+            payload = [payload]
+        if not isinstance(payload, list):
+            raise ValueError("JSON dataset must be an object or a list of objects")
+
+        for row in payload:
+            if isinstance(row, dict):
+                doc = _row_to_document(
+                    row, source_name=str(path.name), text_columns=parsed_text_columns
+                )
+                if doc:
+                    documents.append(doc)
+        return documents
+
+    if ext == ".jsonl":
+        with path.open("r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                row = json.loads(line)
+                if isinstance(row, dict):
+                    doc = _row_to_document(
+                        row,
+                        source_name=str(path.name),
+                        text_columns=parsed_text_columns,
+                    )
+                    if doc:
+                        documents.append(doc)
+        return documents
+
+    if ext in {".txt", ".md"}:
+        text = path.read_text(encoding="utf-8")
+        blocks = [block.strip() for block in text.split("\n\n") if block.strip()]
+        for block in blocks:
+            documents.append(
+                Document(page_content=block, metadata={"source": str(path.name)})
+            )
+        return documents
+
+    raise ValueError(
+        "Unsupported dataset format. Use .csv, .json, .jsonl, .txt, or .md"
+    )
+
+
+def load_dataset_by_config(
+    data_source: str,
+    hf_dataset_name: Optional[str] = None,
+    local_dataset_path: Optional[str] = None,
+    text_columns: Optional[str] = None,
+) -> List[Document]:
+    source = (data_source or "hf").strip().lower()
+
+    if source == "hf":
+        if not hf_dataset_name:
+            raise ValueError("HF_DATASET_NAME is required when DATA_SOURCE=hf")
+        return load_hf_dataset(hf_dataset_name)
+
+    if source == "local":
+        if not local_dataset_path:
+            raise ValueError("LOCAL_DATASET_PATH is required when DATA_SOURCE=local")
+        return load_local_dataset(local_dataset_path, text_columns=text_columns)
+
+    if source == "pdf":
+        if not local_dataset_path:
+            raise ValueError("LOCAL_DATASET_PATH is required when DATA_SOURCE=pdf")
+        return load_pdf_file(local_dataset_path)
+
+    raise ValueError("DATA_SOURCE must be one of: hf, local, pdf")
+
+
+def filter_to_minimal_docs(docs: List[Document]) -> List[Document]:
+    """
+    Given a list of Document objects, return a new list of Document objects
+    containing only 'source' in metadata and the original page_content.
+    """
+    minimal_docs: List[Document] = []
+    for doc in docs:
+        src = doc.metadata.get("source")
+        minimal_docs.append(
+            Document(page_content=doc.page_content, metadata={"source": src})
+        )
+    return minimal_docs
+
+
+# Split the Data into Text Chunks
+def text_split(extracted_data):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
+    text_chunks = text_splitter.split_documents(extracted_data)
+    return text_chunks
+
+
+# Download the Embeddings from HuggingFace
+def download_hugging_face_embeddings():
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2"
+    )  # this model return 384 dimensions
+    return embeddings

src/prompt.py

@@ -0,0 +1,8 @@
+system_prompt = (
+    "You are a friendly and helpful student support assistant. "
+    "Use the following pieces of retrieved context to answer the student's question accurately. "
+    "If you don't know the answer, say that you don't know. "
+    "Keep your answer concise and easy to understand for a student."
+    "\n\n"
+    "{context}"
+)

static/style.css

@@ -0,0 +1,360 @@
+@import url('https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;500;600;700&display=swap');
+
+* {
+	margin: 0;
+	padding: 0;
+	box-sizing: border-box;
+}
+
+:root {
+	--primary: #7c3aed;
+	--primary-light: #a78bfa;
+	--primary-dark: #5b21b6;
+	--secondary: #f59e0b;
+	--bg-deep: #0f0a1e;
+	--bg-card: rgba(26, 17, 52, 0.85);
+	--bg-message: rgba(49, 32, 95, 0.7);
+	--border: rgba(167, 139, 250, 0.15);
+	--text-primary: #f5f3ff;
+	--text-muted: #a78bfa;
+	--user-gradient: linear-gradient(135deg, #7c3aed, #a855f7);
+	--accent-glow: rgba(124, 58, 237, 0.4);
+}
+
+body, html {
+	height: 100%;
+	font-family: 'Plus Jakarta Sans', 'Inter', sans-serif;
+	background: var(--bg-deep);
+	color: var(--text-primary);
+	overflow: hidden;
+}
+
+/* Animated background */
+body::before {
+	content: '';
+	position: fixed;
+	top: -50%;
+	left: -50%;
+	width: 200%;
+	height: 200%;
+	background:
+		radial-gradient(ellipse at 20% 20%, rgba(124, 58, 237, 0.15) 0%, transparent 50%),
+		radial-gradient(ellipse at 80% 80%, rgba(168, 85, 247, 0.1) 0%, transparent 50%),
+		radial-gradient(ellipse at 50% 50%, rgba(245, 158, 11, 0.05) 0%, transparent 60%);
+	z-index: -1;
+	animation: bgPulse 8s ease-in-out infinite alternate;
+}
+
+@keyframes bgPulse {
+	0% { transform: scale(1) rotate(0deg); }
+	100% { transform: scale(1.05) rotate(2deg); }
+}
+
+/* Floating particles */
+body::after {
+	content: '🎓 📚 ✏️ 🏫 📝';
+	position: fixed;
+	top: 5%;
+	right: 3%;
+	font-size: 1.2rem;
+	opacity: 0.08;
+	letter-spacing: 8px;
+	writing-mode: vertical-rl;
+	z-index: -1;
+}
+
+.chat-container {
+	display: flex;
+	flex-direction: column;
+	max-width: 820px;
+	height: 92vh;
+	margin: 4vh auto;
+	background: var(--bg-card);
+	border-radius: 24px;
+	box-shadow:
+		0 32px 64px -12px rgba(0, 0, 0, 0.6),
+		0 0 0 1px var(--border),
+		inset 0 1px 0 rgba(167, 139, 250, 0.1);
+	backdrop-filter: blur(20px);
+	overflow: hidden;
+}
+
+/* ─── HEADER ─────────────────────────────── */
+.chat-header {
+	padding: 18px 28px;
+	background: linear-gradient(135deg, rgba(91, 33, 182, 0.6), rgba(124, 58, 237, 0.4));
+	border-bottom: 1px solid var(--border);
+	display: flex;
+	align-items: center;
+	justify-content: space-between;
+	position: relative;
+	overflow: hidden;
+}
+
+.chat-header::before {
+	content: '';
+	position: absolute;
+	top: 0; left: 0; right: 0; bottom: 0;
+	background: url("data:image/svg+xml,%3Csvg width='60' height='60' viewBox='0 0 60 60' xmlns='http://www.w3.org/2000/svg'%3E%3Cg fill='none' fill-rule='evenodd'%3E%3Cg fill='%23a78bfa' fill-opacity='0.04'%3E%3Cpath d='M36 34v-4h-2v4h-4v2h4v4h2v-4h4v-2h-4zm0-30V0h-2v4h-4v2h4v4h2V6h4V4h-4zM6 34v-4H4v4H0v2h4v4h2v-4h4v-2H6zM6 4V0H4v4H0v2h4v4h2V6h4V4H6z'/%3E%3C/g%3E%3C/g%3E%3C/svg%3E");
+	opacity: 0.5;
+}
+
+.bot-info {
+	display: flex;
+	align-items: center;
+	gap: 14px;
+	position: relative;
+}
+
+.avatar-container {
+	position: relative;
+}
+
+.bot-avatar {
+	width: 52px;
+	height: 52px;
+	border-radius: 16px;
+	object-fit: cover;
+	background: linear-gradient(135deg, #7c3aed, #a855f7);
+	padding: 6px;
+	box-shadow: 0 0 20px var(--accent-glow), 0 0 0 2px rgba(167, 139, 250, 0.3);
+}
+
+.avatar {
+	width: 38px;
+	height: 38px;
+	border-radius: 12px;
+	object-fit: cover;
+	background: linear-gradient(135deg, #7c3aed, #a855f7);
+	padding: 4px;
+	flex-shrink: 0;
+}
+
+.user-message .avatar {
+	background: linear-gradient(135deg, #f59e0b, #fbbf24);
+	padding: 3px;
+}
+
+.status-indicator {
+	position: absolute;
+	bottom: -2px;
+	right: -2px;
+	width: 14px;
+	height: 14px;
+	background: #10b981;
+	border-radius: 50%;
+	border: 2px solid #0f0a1e;
+	box-shadow: 0 0 8px rgba(16, 185, 129, 0.7);
+	animation: pulse-status 2s ease-in-out infinite;
+}
+
+@keyframes pulse-status {
+	0%, 100% { box-shadow: 0 0 8px rgba(16, 185, 129, 0.7); }
+	50% { box-shadow: 0 0 16px rgba(16, 185, 129, 1); }
+}
+
+.text-info h1 {
+	font-size: 1.2rem;
+	font-weight: 700;
+	color: #f5f3ff;
+	letter-spacing: 0.3px;
+	margin-bottom: 3px;
+}
+
+.text-info p {
+	font-size: 0.8rem;
+	color: var(--text-muted);
+	font-weight: 500;
+}
+
+/* Header badge */
+.header-badge {
+	background: rgba(245, 158, 11, 0.15);
+	border: 1px solid rgba(245, 158, 11, 0.3);
+	color: #fbbf24;
+	font-size: 0.72rem;
+	font-weight: 600;
+	padding: 5px 12px;
+	border-radius: 20px;
+	letter-spacing: 0.5px;
+	text-transform: uppercase;
+}
+
+/* ─── CHAT BOX ─────────────────────────────── */
+.chat-box {
+	flex: 1;
+	overflow-y: auto;
+	padding: 28px;
+	display: flex;
+	flex-direction: column;
+	gap: 18px;
+	scroll-behavior: smooth;
+}
+
+.chat-box::-webkit-scrollbar { width: 6px; }
+.chat-box::-webkit-scrollbar-track { background: transparent; }
+.chat-box::-webkit-scrollbar-thumb {
+	background: rgba(167, 139, 250, 0.2);
+	border-radius: 4px;
+}
+.chat-box::-webkit-scrollbar-thumb:hover {
+	background: rgba(167, 139, 250, 0.4);
+}
+
+/* ─── MESSAGES ─────────────────────────────── */
+.message {
+	display: flex;
+	gap: 12px;
+	max-width: 82%;
+	align-items: flex-end;
+}
+
+.user-message { align-self: flex-end; }
+.bot-message  { align-self: flex-start; }
+
+.message-content {
+	padding: 14px 18px;
+	border-radius: 18px;
+	font-size: 0.93rem;
+	line-height: 1.6;
+	position: relative;
+	box-shadow: 0 4px 20px rgba(0, 0, 0, 0.15);
+}
+
+.bot-message .message-content {
+	background: var(--bg-message);
+	color: #ede9fe;
+	border-bottom-left-radius: 4px;
+	border: 1px solid var(--border);
+}
+
+.user-message .message-content {
+	background: var(--user-gradient);
+	color: #ffffff;
+	border-bottom-right-radius: 4px;
+	box-shadow: 0 4px 20px var(--accent-glow);
+}
+
+.error-message .message-content {
+	background: rgba(239, 68, 68, 0.15);
+	border: 1px solid rgba(239, 68, 68, 0.35);
+	color: #fca5a5;
+}
+
+.time {
+	display: block;
+	font-size: 0.68rem;
+	margin-top: 7px;
+	opacity: 0.6;
+	text-align: right;
+	font-weight: 500;
+}
+
+/* ─── FOOTER / INPUT ─────────────────────────── */
+.chat-footer {
+	padding: 18px 28px 22px;
+	background: rgba(91, 33, 182, 0.12);
+	border-top: 1px solid var(--border);
+}
+
+.input-form {
+	display: flex;
+	gap: 12px;
+	background: rgba(167, 139, 250, 0.07);
+	border: 1.5px solid rgba(167, 139, 250, 0.2);
+	border-radius: 32px;
+	padding: 8px 8px 8px 22px;
+	transition: all 0.3s ease;
+}
+
+.input-form:focus-within {
+	border-color: rgba(167, 139, 250, 0.6);
+	background: rgba(167, 139, 250, 0.1);
+	box-shadow: 0 0 0 4px rgba(124, 58, 237, 0.12);
+}
+
+.input-form input {
+	flex: 1;
+	background: transparent;
+	border: none;
+	color: #f5f3ff;
+	font-size: 0.95rem;
+	outline: none;
+	font-family: inherit;
+	font-weight: 500;
+}
+
+.input-form input::placeholder { color: #6d5cae; }
+
+.input-form button {
+	background: var(--user-gradient);
+	border: none;
+	border-radius: 50%;
+	width: 46px;
+	height: 46px;
+	color: white;
+	font-size: 1rem;
+	cursor: pointer;
+	display: flex;
+	align-items: center;
+	justify-content: center;
+	transition: transform 0.2s ease, box-shadow 0.2s ease;
+	flex-shrink: 0;
+	box-shadow: 0 4px 12px var(--accent-glow);
+}
+
+.input-form button:hover {
+	transform: scale(1.08);
+	box-shadow: 0 6px 20px var(--accent-glow);
+}
+
+.input-form button:active { transform: scale(0.94); }
+
+/* ─── ANIMATIONS ─────────────────────────────── */
+@keyframes slideIn {
+	from { opacity: 0; transform: translateY(16px); }
+	to   { opacity: 1; transform: translateY(0); }
+}
+
+.slide-in { animation: slideIn 0.3s cubic-bezier(0.25, 0.46, 0.45, 0.94) forwards; }
+
+.intro-animation {
+	animation: slideIn 0.5s cubic-bezier(0.25, 0.46, 0.45, 0.94) both;
+	animation-delay: 0.2s;
+}
+
+/* ─── TYPING INDICATOR ─────────────────────────── */
+.typing-dots {
+	display: flex;
+	gap: 5px;
+	padding: 4px 6px;
+}
+
+.typing-dots span {
+	width: 8px;
+	height: 8px;
+	background: var(--primary-light);
+	border-radius: 50%;
+	animation: blink 1.4s infinite both;
+}
+
+.typing-dots span:nth-child(1) { animation-delay: 0.2s; }
+.typing-dots span:nth-child(2) { animation-delay: 0.4s; }
+.typing-dots span:nth-child(3) { animation-delay: 0.6s; }
+
+@keyframes blink {
+	0%   { opacity: 0.2; transform: scale(0.8) translateY(0); }
+	40%  { opacity: 1;   transform: scale(1.1) translateY(-3px); }
+	100% { opacity: 0.2; transform: scale(0.8) translateY(0); }
+}
+
+/* ─── RESPONSIVE ─────────────────────────────── */
+@media (max-width: 768px) {
+	.chat-container {
+		margin: 0;
+		height: 100vh;
+		border-radius: 0;
+		max-width: 100%;
+	}
+	.header-badge { display: none; }
+}

store_index.py

@@ -0,0 +1,75 @@
+from dotenv import load_dotenv
+import os
+from src.helper import (
+    load_dataset_by_config,
+    filter_to_minimal_docs,
+    text_split,
+    download_hugging_face_embeddings,
+)
+from pinecone import Pinecone
+from pinecone import ServerlessSpec
+from langchain_pinecone import PineconeVectorStore
+
+load_dotenv(override=True)
+
+
+PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
+os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
+
+
+DATA_SOURCE = os.environ.get("DATA_SOURCE", "hf")
+HF_DATASET_NAME = os.environ.get(
+    "HF_DATASET_NAME", "bot-remains/student-assistance-chatbot"
+)
+LOCAL_DATASET_PATH = os.environ.get("LOCAL_DATASET_PATH", "")
+TEXT_COLUMNS = os.environ.get("TEXT_COLUMNS", "")
+PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX_NAME", "student-chatbot")
+PINECONE_CLOUD = os.environ.get("PINECONE_CLOUD", "aws")
+PINECONE_REGION = os.environ.get("PINECONE_REGION", "us-east-1")
+RESET_INDEX = os.environ.get("RESET_INDEX", "false").strip().lower() == "true"
+
+
+extracted_data = load_dataset_by_config(
+    data_source=DATA_SOURCE,
+    hf_dataset_name=HF_DATASET_NAME,
+    local_dataset_path=LOCAL_DATASET_PATH,
+    text_columns=TEXT_COLUMNS,
+)
+if not extracted_data:
+    raise ValueError("No documents were loaded from the selected dataset source")
+
+filter_data = filter_to_minimal_docs(extracted_data)
+text_chunks = text_split(filter_data)
+
+embeddings = download_hugging_face_embeddings()
+
+pinecone_api_key = PINECONE_API_KEY
+pc = Pinecone(api_key=pinecone_api_key)
+
+
+index_name = PINECONE_INDEX_NAME
+
+if RESET_INDEX and pc.has_index(index_name):
+    pc.delete_index(index_name)
+    print(f"Deleted existing Pinecone index '{index_name}' because RESET_INDEX=true")
+
+if not pc.has_index(index_name):
+    pc.create_index(
+        name=index_name,
+        dimension=384,
+        metric="cosine",
+        spec=ServerlessSpec(cloud=PINECONE_CLOUD, region=PINECONE_REGION),
+    )
+
+index = pc.Index(index_name)
+
+
+docsearch = PineconeVectorStore.from_documents(
+    documents=text_chunks,
+    index_name=index_name,
+    embedding=embeddings,
+)
+
+print(
+    f"Indexed {len(text_chunks)} chunks into Pinecone index '{index_name}' from source '{DATA_SOURCE}'."
+)

template.sh

@@ -0,0 +1,16 @@
+# Creating directory 
+mkdir -p src
+mkdir -p research 
+
+# Creating files
+touch src/__init__.py
+touch src/helper.py 
+touch src/prompt.py
+touch .env 
+touch setup.py
+touch app.py
+touch research/trials.ipynb 
+touch requirements.txt
+
+
+echo "Directory and files created successfully!."

templates/chat.html

@@ -0,0 +1,143 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+	<title>Student Support Chatbot</title>
+	<meta name="viewport" content="width=device-width, initial-scale=1">
+	<link href="https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;500;600;700&display=swap" rel="stylesheet">
+	<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+	<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.6.0/jquery.min.js"></script>
+	<link rel="stylesheet" type="text/css" href="{{ url_for('static', filename='style.css')}}" />
+</head>
+
+<body>
+	<div class="chat-container">
+		<header class="chat-header">
+			<div class="bot-info">
+				<div class="avatar-container">
+					<img src="https://cdn-icons-png.flaticon.com/512/3135/3135715.png" class="bot-avatar"
+						alt="Student Bot">
+					<span class="status-indicator"></span>
+				</div>
+				<div class="text-info">
+					<h1>Student Support Assistant</h1>
+					<p>Ask me anything about college & academics</p>
+				</div>
+			</div>
+			<span class="header-badge">🎓 AI Powered</span>
+		</header>
+
+		<main id="chat-box" class="chat-box">
+			<div class="message bot-message intro-animation">
+					<img src="https://cdn-icons-png.flaticon.com/512/3135/3135715.png" class="avatar" alt="Bot">
+				<div class="message-content">
+					<p>Hello! I am your Student Support Assistant 🎓 Ask me anything about admissions, courses, fees, or campus life!</p>
+				</div>
+			</div>
+		</main>
+
+		<footer class="chat-footer">
+			<form id="messageArea" class="input-form">
+				<input type="text" id="text" name="msg" placeholder="Type your message here..." autocomplete="off"
+					required>
+				<button type="submit" id="send"><i class="fas fa-paper-plane"></i></button>
+			</form>
+		</footer>
+	</div>
+
+	<script>
+		$(document).ready(function () {
+			const chatBox = $('#chat-box');
+
+			function scrollToBottom() {
+				chatBox.animate({ scrollTop: chatBox.prop("scrollHeight") }, 300);
+			}
+
+			function formatTime() {
+				const date = new Date();
+				let hours = date.getHours();
+				let minutes = date.getMinutes();
+				const ampm = hours >= 12 ? 'PM' : 'AM';
+				hours = hours % 12;
+				hours = hours ? hours : 12;
+				minutes = minutes < 10 ? '0' + minutes : minutes;
+				return hours + ':' + minutes + ' ' + ampm;
+			}
+
+			function showTypingIndicator() {
+				const html = `
+                <div class="message bot-message typing-indicator slide-in" id="typing">
+                    <img src="https://cdn-icons-png.flaticon.com/512/3135/3135715.png" class="avatar" alt="Bot">
+                    <div class="message-content">
+                        <div class="typing-dots">
+                            <span></span><span></span><span></span>
+                        </div>
+                    </div>
+                </div>`;
+				chatBox.append(html);
+				scrollToBottom();
+			}
+
+			function removeTypingIndicator() {
+				$('#typing').remove();
+			}
+
+			$("#messageArea").on("submit", function (event) {
+				event.preventDefault();
+
+				const rawText = $("#text").val().trim();
+				if (!rawText) return;
+
+				const time = formatTime();
+				const userHtml = `
+                <div class="message user-message slide-in">
+                    <div class="message-content">
+                        <p>${rawText}</p>
+                        <span class="time">${time}</span>
+                    </div>
+                    <img src="https://i.ibb.co/d5b84Xw/Untitled-design.png" class="avatar" alt="User">
+                </div>`;
+
+				$("#text").val("");
+				chatBox.append(userHtml);
+				scrollToBottom();
+				showTypingIndicator();
+
+				$.ajax({
+					data: { msg: rawText },
+					type: "POST",
+					url: "/get",
+				}).done(function (data) {
+					removeTypingIndicator();
+					const botHtml = `
+                    <div class="message bot-message slide-in">
+                        <img src="https://cdn-icons-png.flaticon.com/512/387/387569.png" class="avatar" alt="Bot">
+                        <div class="message-content">
+                            <p>${data}</p>
+                            <span class="time">${formatTime()}</span>
+                        </div>
+                    </div>`;
+					chatBox.append(botHtml);
+					scrollToBottom();
+				}).fail(function (jqXHR) {
+					removeTypingIndicator();
+					let errorMessage = "Sorry, I am currently facing an internal error. Please check your API keys or try again later.";
+					if (jqXHR.responseText) {
+						errorMessage = jqXHR.responseText;
+					}
+					const errorHtml = `
+                    <div class="message bot-message error-message slide-in">
+                        <img src="https://cdn-icons-png.flaticon.com/512/387/387569.png" class="avatar" alt="Bot">
+                        <div class="message-content">
+                            <p><i class="fas fa-exclamation-triangle"></i> ${errorMessage}</p>
+                        </div>
+                    </div>`;
+					chatBox.append(errorHtml);
+					scrollToBottom();
+				});
+			});
+		});
+	</script>
+</body>
+
+</html>