app.py

import gradio as gr
import os
import requests
import numpy as np
from pypdf import PdfReader
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity


# ---------------- CONFIG ----------------
GROQ_API_KEY = os.environ.get("smartdoc_rag_chatbot")  # HF Secrets me add hona chahiye
GROQ_URL = "https://api.groq.com/openai/v1/chat/completions"
MODEL_NAME = "llama-3.1-8b-instant"

embedder = SentenceTransformer("all-MiniLM-L6-v2")

chunks = []
chunk_embeddings = []

# ---------------- PDF LOADING ----------------
def load_pdfs(pdf_files):
    global chunks, chunk_embeddings

    if not pdf_files:
        return "❌ Please upload at least one PDF."

    documents = []

    for doc_id, pdf in enumerate(pdf_files):
        reader = PdfReader(pdf)
        for page_num, page in enumerate(reader.pages):
            text = page.extract_text()
            if text:
                documents.append({
                    "text": text,
                    "page": page_num + 1,
                    "doc": f"Document {doc_id + 1}"
                })

    # chunking
    chunks = []
    for doc in documents:
        text = doc["text"]
        for i in range(0, len(text), 500):
            chunks.append({
                "content": text[i:i+500],
                "page": doc["page"],
                "doc": doc["doc"]
            })

    texts = [c["content"] for c in chunks]
    chunk_embeddings = embedder.encode(texts)

    return f"✅ Loaded {len(pdf_files)} PDF(s) with {len(chunks)} chunks."

# ---------------- RETRIEVAL ----------------
def retrieve_context(query, k=3):
    query_embedding = embedder.encode([query])
    similarities = cosine_similarity(query_embedding, chunk_embeddings)[0]
    top_k = np.argsort(similarities)[-k:]
    selected = [chunks[i] for i in top_k]
    context = "\n".join([c["content"] for c in selected])
    source = selected[-1]
    return context, source

# ---------------- GROQ CALL ----------------
def ask_question(question):
    if not chunks:
        return "⚠️ Please load PDFs first."

    context, source = retrieve_context(question)

    prompt = f"""
You are SmartDoc RAG Chatbot.
Answer the question using ONLY the context below.

Context:
{context}

Question:
{question}
"""

    headers = {
        "Authorization": f"Bearer {GROQ_API_KEY}",
        "Content-Type": "application/json"
    }

    response = requests.post(
        GROQ_URL,
        headers=headers,
        json={
            "model": MODEL_NAME,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": 0.2
        }
    )

    answer = response.json()["choices"][0]["message"]["content"]

    return f"""{answer}

📄 Source: {source['doc']} — Page {source['page']}"""

# ---------------- UI ----------------
css = """
body {
    background: linear-gradient(120deg, #e0f2ff, #f8fbff);
}
h1, h3 {
    text-align: center;
}
.gr-textbox textarea {
    font-size: 15px;
}
.gr-button-primary {
    font-weight: bold;
}
"""

with gr.Blocks(
    theme=gr.themes.Soft(
        primary_hue="blue",
        secondary_hue="cyan",
        neutral_hue="slate",
        font=["Inter", "sans-serif"]
    ),
    css=css
) as demo:

    gr.Markdown("""
    # 📄 SmartDoc RAG Chatbot  
    ### Retrieval‑Augmented AI for Document Question Answering  
    Upload PDFs and ask questions based **only** on their content.
    """)

    with gr.Row():

        # LEFT PANEL
        with gr.Column(scale=1):
            pdf_files = gr.File(
                file_types=[".pdf"],
                file_count="multiple",
                label="📂 Upload PDF Documents"
            )
            load_btn = gr.Button("📥 Load Documents", variant="primary")
            status = gr.Textbox(label="Status", interactive=False)

        # RIGHT PANEL
        with gr.Column(scale=2):
            with gr.Row():
                question = gr.Textbox(
                    placeholder="Type your question here…",
                    lines=1,
                    scale=8
                )
                send_btn = gr.Button("➤", scale=1)

            answer = gr.Textbox(
                label="Answer",
                lines=8
            )

    # EVENTS
    load_btn.click(load_pdfs, inputs=pdf_files, outputs=status)

    send_btn.click(
        ask_question,
        inputs=question,
        outputs=answer
    ).then(lambda: "", None, question)

    question.submit(
        ask_question,
        inputs=question,
        outputs=answer
    ).then(lambda: "", None, question)

demo.launch()