app.py

import requests
import fitz
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
from groq import Groq
import gradio as gr
import os

# =========================
# 1. LOAD API KEY (HF SECRET)
# =========================
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
client = Groq(api_key=GROQ_API_KEY)

# =========================
# 2. LOAD PDF
# =========================
pdf_url = "https://huggingface.co/datasets/HuzaifaTech/rag_file/resolve/main/Hands_On_Machine_Learning_with_Scikit_Le.pdf"

pdf_path = "file.pdf"

if not os.path.exists(pdf_path):
    response = requests.get(pdf_url)
    with open(pdf_path, "wb") as f:
        f.write(response.content)

# =========================
# 3. EXTRACT TEXT
# =========================
doc = fitz.open(pdf_path)
text = ""

for page in doc:
    text += page.get_text()

# =========================
# 4. CHUNKING
# =========================
def chunk_text(text, chunk_size=800):
    paragraphs = text.split("\n")
    chunks = []
    current = ""

    for para in paragraphs:
        if len(current) + len(para) < chunk_size:
            current += para + "\n"
        else:
            chunks.append(current.strip())
            current = para

    if current:
        chunks.append(current.strip())

    return chunks

chunks = chunk_text(text)[:300]

# =========================
# 5. EMBEDDINGS
# =========================
model = SentenceTransformer("all-MiniLM-L6-v2")

embeddings = model.encode(chunks, batch_size=32)
faiss.normalize_L2(embeddings)

# =========================
# 6. FAISS
# =========================
dim = embeddings.shape[1]
index = faiss.IndexFlatL2(dim)
index.add(embeddings)

# =========================
# 7. RETRIEVAL
# =========================
def retrieve(query, k=4):
    q_emb = model.encode([query])
    faiss.normalize_L2(q_emb)
    _, idx = index.search(q_emb, k)
    return [chunks[i] for i in idx[0]]

# =========================
# 8. GENERATION
# =========================
def generate_answer(query):
    docs = retrieve(query)
    context = "\n\n".join(docs)

    prompt = f"""
Context:
{context}

Question:
{query}
"""

    try:
        res = client.chat.completions.create(
            model="llama-3.3-70b-versatile",
            messages=[
                {
                    "role": "system",
                    "content": "Answer ONLY from the provided context. If not found, say 'I don't know'."
                },
                {
                    "role": "user",
                    "content": prompt
                }
            ],
            temperature=0,
            max_tokens=500
        )
        return res.choices[0].message.content

    except Exception as e:
        return f"Error: {str(e)}"

# =========================
# 9. UI (PROFESSIONAL)
# =========================
def chat(message, history):
    return generate_answer(message)

with gr.Blocks() as demo:
    gr.Markdown("# 📚 RAG Chatbot (ML Book)")
    gr.Markdown("Ask questions from *Hands-On Machine Learning* PDF")

    chatbot = gr.ChatInterface(
        fn=chat,
        chatbot=gr.Chatbot(height=400),
        textbox=gr.Textbox(placeholder="Ask a question...", container=False),
    )

demo.launch(theme=gr.themes.Soft())