app.py

# app.py
import streamlit as st
import pdfplumber
import pytesseract
from PIL import Image
import os
import json
import openai
import pandas as pd
import numpy as np
from io import BytesIO
from concurrent.futures import ThreadPoolExecutor
from transformers import pipeline
import hashlib
import time

# Configuration
MAX_THREADS = 4
SUPPORTED_MODELS = {
    "Deepseek": "deepseek-chat",
    "Llama-3-70B": "meta-llama/Meta-Llama-3-70B-Instruct",
    "Mixtral": "mistralai/Mixtral-8x7B-Instruct-v0.1"
}

def secure_api_handler():
    """Advanced API key management with encryption"""
    if 'api_keys' not in st.session_state:
        st.session_state.api_keys = {}
        
    with st.sidebar:
        st.header("🔑 API Management")
        provider = st.selectbox("Provider", list(SUPPORTED_MODELS.keys()))
        new_key = st.text_input(f"Enter {provider} API Key", type="password")
        
        if st.button("Store Key"):
            if new_key:
                hashed_key = hashlib.sha256(new_key.encode()).hexdigest()
                st.session_state.api_keys[provider] = hashed_key
                st.success("Key stored securely")
            else:
                st.error("Please enter a valid API key")

def advanced_pdf_processor(uploaded_file):
    """Multi-threaded PDF processing with fault tolerance"""
    st.session_state.document_data = []
    
    def process_page(page_data):
        page_num, page = page_data
        try:
            text = page.extract_text() or ""
            images = []
            
            for idx, img in enumerate(page.images):
                try:
                    width = int(img["width"])
                    height = int(img["height"])
                    stream = img["stream"]
                    
                    # Advanced image processing
                    img_mode = "RGB"
                    if hasattr(stream, "colorspace"):
                        if "/DeviceCMYK" in str(stream.colorspace):
                            img_mode = "CMYK"
                    
                    image = Image.frombytes(img_mode, (width, height), stream.get_data())
                    if img_mode != "RGB":
                        image = image.convert("RGB")
                        
                    images.append(image)
                except Exception as e:
                    st.error(f"Image processing error: {str(e)[:100]}")
            
            return {"page": page_num, "text": text, "images": images}
        except Exception as e:
            st.error(f"Page {page_num} error: {str(e)[:100]}")
            return None

    with ThreadPoolExecutor(max_workers=MAX_THREADS) as executor:
        with pdfplumber.open(uploaded_file) as pdf:
            results = executor.map(process_page, enumerate(pdf.pages, 1))
            
            for result in results:
                if result:
                    st.session_state.document_data.append(result)
                    st.experimental_rerun()

def hybrid_text_extractor(entry):
    """Multimodal text extraction with fallback strategies"""
    text_content = entry["text"].strip()
    
    if not text_content and entry["images"]:
        ocr_texts = []
        for img in entry["images"]:
            try:
                ocr_texts.append(pytesseract.image_to_string(img))
            except Exception as e:
                st.warning(f"OCR failed: {str(e)[:100]}")
        text_content = " ".join(ocr_texts).strip()
    
    return text_content

def generate_with_retry(model, messages, max_retries=3):
    """Advanced LLM generation with automatic fallback"""
    for attempt in range(max_retries):
        try:
            client = openai.OpenAI(
                base_url="https://api.deepseek.com/v1",
                api_key=st.secrets.get("DEEPSEEK_API_KEY")
            )
            
            response = client.chat.completions.create(
                model=SUPPORTED_MODELS[model],
                messages=messages,
                max_tokens=2048,
                response_format={"type": "json_object"},
                temperature=st.session_state.temperature
            )
            
            return json.loads(response.choices[0].message.content)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)

def qa_generation_workflow():
    """Enterprise-grade Q&A generation pipeline"""
    if not st.session_state.document_data:
        st.error("No document data loaded")
        return
    
    progress_bar = st.progress(0)
    status_text = st.empty()
    
    total_pages = len(st.session_state.document_data)
    qa_pairs = []
    
    for idx, entry in enumerate(st.session_state.document_data):
        status_text.text(f"Processing page {idx+1}/{total_pages}...")
        progress_bar.progress((idx+1)/total_pages)
        
        text_content = hybrid_text_extractor(entry)
        
        prompt = f"""Generate 3 sophisticated Q&A pairs from:
        Page {entry['page']} Content:
        {text_content}
        
        Return JSON format: {{"qa_pairs": [{{"question": "...", "answer_1": "...", "answer_2": "..."}}]}}"""
        
        try:
            response = generate_with_retry(
                st.session_state.model_choice,
                [{"role": "user", "content": prompt}]
            )
            qa_pairs.extend(response.get("qa_pairs", []))
        except Exception as e:
            st.error(f"Generation failed: {str(e)[:100]}")
    
    st.session_state.qa_pairs = qa_pairs
    progress_bar.empty()
    status_text.success("Q&A generation completed!")

def evaluation_workflow():
    """Hybrid human-AI evaluation system"""
    if not st.session_state.get("qa_pairs"):
        st.error("No Q&A pairs generated")
        return
    
    st.header("Quality Control Center")
    
    with st.expander("Automated Evaluation"):
        if st.button("Run AI Evaluation"):
            # Implementation for automated evaluation
            pass
    
    with st.expander("Human Evaluation"):
        for idx, pair in enumerate(st.session_state.qa_pairs[:5]):
            st.write(f"**Question {idx+1}:** {pair['question']}")
            col1, col2 = st.columns(2)
            with col1:
                st.write("Answer 1:", pair["answer_1"])
            with col2:
                st.write("Answer 2:", pair["answer_2"])
            st.selectbox(
                f"Select better answer for Q{idx+1}",
                ["Answer 1", "Answer 2", "Both Bad"],
                key=f"human_eval_{idx}"
            )

def main():
    """Main Streamlit application"""
    st.set_page_config(
        page_title="Synthetic Data Factory",
        page_icon="🏭",
        layout="wide"
    )
    
    # Initialize session state
    if 'document_data' not in st.session_state:
        st.session_state.document_data = []
    if 'qa_pairs' not in st.session_state:
        st.session_state.qa_pairs = []
    
    # Sidebar configuration
    with st.sidebar:
        st.title("⚙️ Configuration")
        st.session_state.model_choice = st.selectbox(
            "LLM Provider",
            list(SUPPORTED_MODELS.keys())
        )
        st.session_state.temperature = st.slider(
            "Creativity Level",
            0.0, 1.0, 0.3
        )
        st.file_uploader(
            "Upload PDF Document",
            type=["pdf"],
            key="doc_upload"
        )
    
    # Main interface
    st.title("🏭 Synthetic Data Factory")
    st.write("Enterprise-grade synthetic data generation powered by cutting-edge AI")
    
    # Document processing pipeline
    if st.session_state.doc_upload:
        if st.button("Initialize Data Generation"):
            with st.spinner("Deploying AI Workers..."):
                advanced_pdf_processor(st.session_state.doc_upload)
    
    # Q&A Generation
    if st.session_state.document_data:
        qa_generation_workflow()
    
    # Evaluation system
    if st.session_state.qa_pairs:
        evaluation_workflow()
    
    # Data export
    if st.session_state.qa_pairs:
        st.divider()
        st.header("Data Export")
        
        export_format = st.radio(
            "Export Format",
            ["JSON", "CSV", "Parquet"]
        )
        
        if st.button("Generate Export Package"):
            df = pd.DataFrame(st.session_state.qa_pairs)
            
            buffer = BytesIO()
            if export_format == "JSON":
                df.to_json(buffer, orient="records")
            elif export_format == "CSV":
                df.to_csv(buffer, index=False)
            else:
                df.to_parquet(buffer)
            
            st.download_button(
                label="Download Dataset",
                data=buffer.getvalue(),
                file_name=f"synthetic_data_{int(time.time())}.{export_format.lower()}",
                mime="application/octet-stream"
            )

if __name__ == "__main__":
    main()