app.py

# rag_upload_app_local.py
import os
from PyPDF2 import PdfReader
import gradio as gr

# LangChain imports
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import HuggingFacePipeline
from langchain.chains import RetrievalQA

# Transformers imports
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# --- CONFIG ---
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
LOCAL_MODEL = "google/flan-t5-base"  # lightweight model

# Load local HuggingFace model
tokenizer = AutoTokenizer.from_pretrained(LOCAL_MODEL)
model = AutoModelForSeq2SeqLM.from_pretrained(LOCAL_MODEL)
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, max_length=512)
llm = HuggingFacePipeline(pipeline=pipe)

def process_document(file):
    try:
        if file is None:
            return None, "⚠️ Please upload a document."

        # Extract text from PDF
        text = ""
        reader = PdfReader(file)
        for page in reader.pages:
            page_text = page.extract_text()
            if page_text:
                text += page_text + "\n"

        if not text.strip():
            return None, "⚠️ No text could be extracted. Try another PDF."

        # Split text into chunks
        splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
        chunks = splitter.split_text(text)

        # Create embeddings + FAISS index
        embedder = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
        db = FAISS.from_texts(chunks, embedder)

        # Create retriever + QA chain
        retriever = db.as_retriever(search_kwargs={"k": 4})
        qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)

        return qa, f"✅ Document processed successfully with {len(chunks)} chunks!"

    except Exception as e:
        return None, f"❌ Error: {str(e)}"

def answer_question(qa, question):
    if qa is None:
        return "Please upload and process a document first."
    return qa.run(question)

with gr.Blocks() as demo:
    gr.Markdown("## 📄 PDF CHAT ASSISSTANT")

    with gr.Row():
        file_input = gr.File(label="Upload PDF Document", type="filepath")
        status = gr.Textbox(label="Status", interactive=False, lines=6)  # 🔹 bigger

    process_btn = gr.Button("Process Document")

    with gr.Row():
        question = gr.Textbox(label="Ask a Question", lines=3, placeholder="Type your question here...")  # 🔹 taller
        answer = gr.Textbox(label="Answer", lines=8)  # 🔹 taller answer box

    qa_state = gr.State()

    process_btn.click(fn=process_document, inputs=file_input, outputs=[qa_state, status])
    question.submit(fn=answer_question, inputs=[qa_state, question], outputs=answer)

demo.launch()