Add application file

app.py

@@ -0,0 +1,267 @@
+# 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()