flask_app.py

"""Flask App script for RAG chatbot"""

import gc
import os
import re
import tempfile
from flask import Flask, request, jsonify, render_template

# # Pre-download and save the embedding model
# from sentence_transformers import SentenceTransformer
# model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L3-v2")
# model.save("models/paraphrase-MiniLM-L3-v2")


# Disable CUDA and excessive parallel threads to save memory
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["TRANSFORMERS_OFFLINE"] = "1"

# Flask app initialization
app = Flask(__name__, template_folder="templates", static_folder="static")


# Global states
retriever = None
LLM_model = None
api_key = None  # API key will come from frontend


SYSTEM_MESSAGE = """
You are a RAG Assistant for the uploaded document.
Your role is to help users understand its contents clearly and accurately.

Rules:
1. Prioritize the document context first.
2. If the answer isn’t in the document, say you don’t know.
3. Be friendly, direct, and concise.
4. Avoid adding extra information unless asked.
"""


# routes
@app.route("/")
def home():
    return render_template("chat_page.html")


@app.route("/upload", methods=["POST"])
def upload_file():
    """Route handling document upload, splitting, chunking, and vectorization."""
    
    global retriever, LLM_model, api_key
    
    try:
        # Import heavy dependencies only when needed
        from langchain_text_splitters import RecursiveCharacterTextSplitter
        from langchain_community.vectorstores import FAISS
        from langchain_community.document_loaders import TextLoader, PyPDFLoader
        from langchain_huggingface import HuggingFaceEmbeddings
        from langchain_google_genai import ChatGoogleGenerativeAI
    except Exception as e:
        return jsonify({"error": f"Missing dependency: {e}"}), 500
    
    
    # Get user API key
    api_key = request.form.get("apiKey")
    if not api_key:
        return jsonify({"error": "API key missing!"}), 400

    uploaded = request.files.get("file")
    if not uploaded or uploaded.filename.strip() == "":
        return jsonify({"error": "No file uploaded."}), 400

    ext = uploaded.filename.rsplit(".", 1)[-1].lower()
    with tempfile.NamedTemporaryFile(delete=False, suffix=f".{ext}") as tmp:
        uploaded.save(tmp.name)
        path = tmp.name

    # Load document
    try:
        loader = PyPDFLoader(path) if ext == "pdf" else TextLoader(path)
        documents = loader.load()
    except Exception as e:
        os.unlink(path)
        return jsonify({"error": f"Failed to read document: {e}"}), 400

    if not documents:
        os.unlink(path)
        return jsonify({"error": "No readable content found in the document."}), 400

    # Split document into smaller chunks
    splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
    chunks = splitter.split_documents(documents)

    # Create embeddings & vector store
    try:
        # embeds = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-MiniLM-L3-v2")
        embeds = HuggingFaceEmbeddings(model_name="./models/paraphrase-MiniLM-L3-v2")  # local model (offline)
        vector_store = FAISS.from_documents(chunks, embeds)
        retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
        
    except Exception as e:
        os.unlink(path)
        return jsonify({"error": f"Embedding model failed: {e}"}), 500

    # Initialize Gemini model
    try:
        LLM_model = ChatGoogleGenerativeAI(model="gemini-2.5-flash", google_api_key=api_key)
    except Exception as e:
        return jsonify({"error": f"Failed to initialize chat model: {e}"}), 500

    # Cleanup
    os.unlink(path)
    del documents, chunks, vector_store
    gc.collect()

    return jsonify({"message": "Document processed successfully! You can now ask questions."})


@app.route("/chat", methods=["POST"])
def chat():
    """Q&A route on uploaded document."""
    global retriever, LLM_model

    from langchain_core.prompts import PromptTemplate
    from langchain_core.runnables import RunnableParallel, RunnableLambda, RunnablePassthrough
    from langchain_core.output_parsers import StrOutputParser

    if retriever is None or LLM_model is None:
        return jsonify({"error": "Please upload a document first."}), 400

    question = request.form.get("question") or (request.json and request.json.get("question"))
    if not question:
        return jsonify({"error": "No question provided."}), 400

    # Retrieve documents with retriever
    try:
        docs = retriever.invoke(question)
        context = "\n\n".join(d.page_content for d in docs)
    except Exception as e:
        return jsonify({"error": f"Retriever failed: {e}"}), 500

    # prompt template
    prompt_template = PromptTemplate(
        template=(
            "You are answering strictly based on this document.\n\n"
            "{context}\n\n"
            "Question: {question}\n\n"
            "Answer:"
        ),
        input_variables=["context", "question"],
    )

    # Combine into a pipeline
    chain = (
        RunnableParallel({
            "context": retriever | RunnableLambda(lambda docs: "\n\n".join(d.page_content for d in docs)),
            "question": RunnablePassthrough(),
        })
        | prompt_template
        | LLM_model
        | StrOutputParser()
    )

    try:
        response = chain.invoke(question).strip()
    except Exception as e:
        response = f"Error generating response: {str(e)}"

    # Clean markdown artifacts
    cleaned = re.sub(r"[*_`#]+", "", response)

    gc.collect()
    return jsonify({"answer": cleaned})


if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860))
    app.run(host="0.0.0.0", port=port, debug=False)