app.py

import gradio as gr
import os
import tempfile
from typing import List

import PyPDF2
from sentence_transformers import SentenceTransformer
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

from groq import Groq

# -----------------------------
# Configuration
# -----------------------------
GROQ_API_KEY = os.getenv("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")
MODEL_NAME = "llama3-8b-8192"

client = Groq(api_key=GROQ_API_KEY)
embedder = SentenceTransformer("all-MiniLM-L6-v2")

# -----------------------------
# PDF Processing
# -----------------------------
def extract_text_from_pdfs(files: List[tempfile.NamedTemporaryFile]):
    documents = []
    for file in files:
        reader = PyPDF2.PdfReader(file)
        for page_num, page in enumerate(reader.pages):
            text = page.extract_text()
            if text:
                documents.append({
                    "text": text,
                    "source": f"{os.path.basename(file.name)} - page {page_num + 1}"
                })
    return documents

# -----------------------------
# Chunking
# -----------------------------
def chunk_text(documents, chunk_size=500, overlap=50):
    chunks = []
    for doc in documents:
        text = doc["text"]
        words = text.split()
        start = 0
        while start < len(words):
            chunk_words = words[start:start + chunk_size]
            chunk_text = " ".join(chunk_words)
            chunks.append({
                "text": chunk_text,
                "source": doc["source"]
            })
            start += chunk_size - overlap
    return chunks

# -----------------------------
# Embeddings & Retrieval
# -----------------------------
def embed_chunks(chunks):
    texts = [c["text"] for c in chunks]
    embeddings = embedder.encode(texts)
    return embeddings

def retrieve_relevant_chunks(query, chunks, embeddings, top_k=3):
    query_embedding = embedder.encode([query])
    similarities = cosine_similarity(query_embedding, embeddings)[0]
    top_indices = np.argsort(similarities)[-top_k:][::-1]

    results = []
    for idx in top_indices:
        results.append(chunks[idx])
    return results

# -----------------------------
# LLM Call
# -----------------------------
def ask_llm(question, context, history):
    messages = history.copy()

    system_prompt = (
        "You are a helpful assistant. Answer the question strictly using the provided context. "
        "If the answer is not in the context, say so."
    )

    messages.insert(0, {"role": "system", "content": system_prompt})
    messages.append({
        "role": "user",
        "content": f"Context:\n{context}\n\nQuestion:\n{question}"
    })

    response = client.chat.completions.create(
        model=MODEL_NAME,
        messages=messages,
        temperature=0.3
    )

    return response.choices[0].message.content

# -----------------------------
# Main Chat Logic
# -----------------------------
def chat(files, question, chat_history):
    if not files:
        return chat_history, "Please upload PDF files first."

    documents = extract_text_from_pdfs(files)
    chunks = chunk_text(documents)
    embeddings = embed_chunks(chunks)

    relevant_chunks = retrieve_relevant_chunks(question, chunks, embeddings)

    context = ""
    sources = []
    for c in relevant_chunks:
        context += c["text"] + "\n\n"
        sources.append(c["source"])

    answer = ask_llm(question, context, chat_history)

    answer_with_sources = (
        f"{answer}\n\n"
        f"Sources:\n" + "\n".join(set(sources))
    )

    chat_history.append({"role": "user", "content": question})
    chat_history.append({"role": "assistant", "content": answer_with_sources})

    return chat_history, answer_with_sources

# -----------------------------
# Gradio UI
# -----------------------------
with gr.Blocks(title="Enhanced RAG Chatbot") as demo:
    gr.Markdown("## 📚 Enhanced RAG-Based Chatbot (PDF QA)")
    gr.Markdown("Upload multiple PDFs and ask questions based on their content.")

    with gr.Row():
        pdf_files = gr.File(
            label="Upload PDF Files",
            file_types=[".pdf"],
            file_count="multiple"
        )

    chatbot = gr.Chatbot()
    question = gr.Textbox(label="Ask a question")
    state = gr.State([])

    submit = gr.Button("Ask")

    submit.click(
        fn=chat,
        inputs=[pdf_files, question, state],
        outputs=[chatbot, chatbot]
    )

demo.launch()