app.py

import streamlit as st
from PyPDF2 import PdfReader
from transformers import AutoTokenizer, AutoModel
import torch
import faiss
import numpy as np
import os
import requests

# -------------------- Config --------------------
GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # Add this as a secret in Hugging Face
GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
EMBED_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"

# -------------------- PDF Processing --------------------
def read_pdf(file):
    pdf = PdfReader(file)
    text = ""
    for page in pdf.pages:
        text += page.extract_text() + "\n"
    return text

# -------------------- Chunking --------------------
def chunk_text(text, chunk_size=500, overlap=50):
    words = text.split()
    chunks = []
    start = 0
    while start < len(words):
        end = start + chunk_size
        chunk = " ".join(words[start:end])
        chunks.append(chunk)
        start += chunk_size - overlap
    return chunks

# -------------------- Embedding --------------------
@st.cache_resource
def load_embedding_model():
    tokenizer = AutoTokenizer.from_pretrained(EMBED_MODEL_NAME)
    model = AutoModel.from_pretrained(EMBED_MODEL_NAME)
    return tokenizer, model

def get_embeddings(text_chunks, tokenizer, model):
    embeddings = []
    for chunk in text_chunks:
        inputs = tokenizer(chunk, return_tensors="pt", truncation=True, padding=True)
        with torch.no_grad():
            outputs = model(**inputs)
        emb = outputs.last_hidden_state[:, 0, :].numpy()[0]
        embeddings.append(emb)
    return np.array(embeddings)

# -------------------- FAISS --------------------
def build_faiss_index(embeddings):
    dimension = embeddings.shape[1]
    index = faiss.IndexFlatL2(dimension)
    index.add(embeddings)
    return index

def search_index(index, query, tokenizer, model, chunks, top_k=3):
    inputs = tokenizer(query, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    query_emb = outputs.last_hidden_state[:, 0, :].numpy()
    distances, indices = index.search(query_emb, top_k)
    return [chunks[i] for i in indices[0]]

# -------------------- GROQ Query --------------------
def query_groq(context, question):
    prompt = f"""You are a helpful engineering assistant. Use the following context to answer the question.

Context:
{context}

Question:
{question}
"""
    headers = {
        "Authorization": f"Bearer {GROQ_API_KEY}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": "llama3-8b-8192",
        "messages": [
            {"role": "system", "content": "You are a helpful engineering tutor."},
            {"role": "user", "content": prompt}
        ],
        "temperature": 0.3,
        "max_tokens": 512
    }
    response = requests.post(GROQ_API_URL, headers=headers, json=payload)
    response.raise_for_status()
    return response.json()["choices"][0]["message"]["content"]

# -------------------- Streamlit UI --------------------
st.title("📚 engGlass RAG Assistant")
st.write("Upload a PDF, ask engineering questions, and get smart answers!")

uploaded_file = st.file_uploader("Upload PDF", type="pdf")
question = st.text_input("Ask a question based on the uploaded document:")

if uploaded_file and question:
    with st.spinner("Reading and processing PDF..."):
        text = read_pdf(uploaded_file)
        chunks = chunk_text(text)
        tokenizer, model = load_embedding_model()
        embeddings = get_embeddings(chunks, tokenizer, model)
        index = build_faiss_index(embeddings)
        top_chunks = search_index(index, question, tokenizer, model, chunks)
        context = "\n".join(top_chunks)

    with st.spinner("Generating answer from Groq..."):
        try:
            answer = query_groq(context, question)
            st.markdown("### 💡 Answer")
            st.write(answer)
        except Exception as e:
            st.error(f"Error: {str(e)}")