app.py

import os
import tempfile
import streamlit as st
from pdf2image import convert_from_path
from PIL import Image
import pytesseract
import docx
import faiss
from sentence_transformers import SentenceTransformer
from groq import Groq
import numpy as np

# Load sentence transformer model
model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')

# Initialize Groq client
client = Groq(api_key=os.environ['GROQ_KEY'])

# OCR extraction from PDF
def extract_text_from_pdf(file_path):
    images = convert_from_path(file_path, poppler_path='/usr/bin')  # Make sure Poppler path is correct
    text = ""
    for image in images:
        text += pytesseract.image_to_string(image, lang='urd') + "\n"
    return text

# Text extraction from Word documents
def extract_text_from_docx(file_path):
    doc = docx.Document(file_path)
    return "\n".join([para.text for para in doc.paragraphs])

# Chunking text
def chunk_text(text, max_length=500):
    sentences = text.split("\n")
    chunks = []
    current_chunk = ""
    for sentence in sentences:
        if len(current_chunk) + len(sentence) <= max_length:
            current_chunk += sentence + " "
        else:
            chunks.append(current_chunk.strip())
            current_chunk = sentence + " "
    if current_chunk:
        chunks.append(current_chunk.strip())
    return chunks

# Generate embeddings
def generate_embeddings(chunks):
    return model.encode(chunks)

# Store in FAISS index
def create_faiss_index(embeddings):
    dim = embeddings.shape[1]
    index = faiss.IndexFlatL2(dim)
    index.add(embeddings)
    return index

# Search in FAISS
def search_index(index, query, chunks, k=3):
    query_embedding = model.encode([query])
    distances, indices = index.search(query_embedding, k)
    return [chunks[i] for i in indices[0]]

# Groq query function with Urdu system prompt
def query_groq(query, context):
    messages = [
        {
            "role": "system",
            "content": (
                "آپ ایک مددگار اسسٹنٹ ہیں جو ہمیشہ اردو میں جواب دیتا ہے، چاہے سوال اردو یا انگریزی میں ہو۔ "
                "براہ کرم نیچے دیے گئے سیاق و سباق اور سوال کی بنیاد پر اردو میں تفصیلی جواب دیں۔"
            ),
        },
        {
            "role": "user",
            "content": f"سیاق و سباق:\n{context}\n\nسوال:\n{query}",
        },
    ]

    chat_completion = client.chat.completions.create(
        messages=messages,
        model="llama3-8b-8192",
    )
    return chat_completion.choices[0].message.content

# Streamlit UI
st.title("📚 اردو ڈاکیومنٹس کے لیے RAG ایپ")
uploaded_file = st.file_uploader("پی ڈی ایف یا ورڈ فائل اپلوڈ کریں", type=["pdf", "docx"])

if uploaded_file:
    with tempfile.NamedTemporaryFile(delete=False, suffix="." + uploaded_file.name.split('.')[-1]) as tmp:
        tmp.write(uploaded_file.read())
        tmp_path = tmp.name

    if uploaded_file.name.endswith(".pdf"):
        text = extract_text_from_pdf(tmp_path)
    else:
        text = extract_text_from_docx(tmp_path)

    chunks = chunk_text(text)
    embeddings = generate_embeddings(chunks)
    index = create_faiss_index(np.array(embeddings))

    st.success("ڈاکیومنٹ پروسیس ہو گیا ہے۔ اب سوال پوچھیں۔")
    query = st.text_input("سوال درج کریں")

    if query:
        top_chunks = search_index(index, query, chunks)
        context = "\n".join(top_chunks)
        answer = query_groq(query, context)
        st.markdown("### جواب:")
        st.write(answer)