app.py

# app.py
import streamlit as st
import asyncio
from pathlib import Path
import tempfile
import time
from typing import Dict, List
import pandas as pd
from core.document_processor import DocumentProcessor
from core.embeddings import DocumentEmbedder
from core.vector_store import FAISSVectorStore
from modules.qa_module import EnhancedQAModule

# Page configuration
st.set_page_config(
    page_title="SYNAPTYX - AI Accelerator",
    page_icon="🧠",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS
st.markdown("""
    <style>
        .main {
            background-color: #f5f5f5;
        }
        .stButton>button {
            background-color: #4CAF50;
            color: white;
            border-radius: 5px;
            border: none;
            padding: 10px 24px;
        }
        .stButton>button:hover {
            background-color: #45a049;
        }
        .css-1d391kg {
            padding: 2rem 1rem;
        }
        .stAlert {
            background-color: rgba(255, 255, 255, 0.9);
        }
        .custom-title {
            font-size: 2.5rem;
            font-weight: bold;
            color: #1E3D59;
            text-align: center;
            margin-bottom: 2rem;
        }
        .metric-card {
            background-color: white;
            padding: 1rem;
            border-radius: 10px;
            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
        }
    </style>
""", unsafe_allow_html=True)

# Initialize session state
if 'processed_docs' not in st.session_state:
    st.session_state.processed_docs = 0
if 'total_chunks' not in st.session_state:
    st.session_state.total_chunks = 0
if 'demo' not in st.session_state:
    st.session_state.demo = None
if 'history' not in st.session_state:
    st.session_state.history = []

class SynaptyxDemo:
    def __init__(self):
        self.embedder = DocumentEmbedder()
        self.vector_store = FAISSVectorStore()
        self.qa_module = EnhancedQAModule()
        self.doc_processor = DocumentProcessor()
    
    async def process_document(self, file) -> Dict:
        try:
            # Create temp file path
            with tempfile.NamedTemporaryFile(delete=False, suffix=Path(file.name).suffix) as tmp_file:
                tmp_file.write(file.getbuffer())
                temp_path = tmp_file.name

            # Process document
            doc_content = self.doc_processor.process_document(temp_path)
            
            # Process for vector store
            chunks, embeddings, metadata = self.embedder.process_documents([{
                "content": doc_content["content"],
                "source": file.name
            }])
            
            # Add to vector store
            self.vector_store.add_documents(chunks, embeddings, metadata)
            
            return {
                "status": "success",
                "chunks": len(chunks),
                "metadata": doc_content["metadata"]
            }
        
        except Exception as e:
            return {"status": "error", "error": str(e)}
        
        finally:
            Path(temp_path).unlink(missing_ok=True)

    async def query(self, question: str, k: int = 5) -> Dict:
        try:
            # Get relevant documents
            relevant_docs = self.vector_store.similarity_search(
                question,
                self.embedder,
                k=k
            )
            
            # Get answer
            answer = await self.qa_module.process(question, relevant_docs)
            
            return {
                "status": "success",
                "answer": answer["answer"],
                "confidence": answer["confidence"],
                "sources": answer["sources"]
            }
        except Exception as e:
            return {"status": "error", "error": str(e)}

def main():
    # Initialize demo instance if not exists
    if st.session_state.demo is None:
        st.session_state.demo = SynaptyxDemo()

    # Sidebar
    with st.sidebar:
        st.image("https://via.placeholder.com/150?text=SYNAPTYX", width=150)
        st.markdown("### 🧠 SYNAPTYX")
        st.markdown("#### AI Accelerator Platform")
        st.markdown("---")
        st.markdown("### 📊 Analytics")
        st.markdown(f"Documents Processed: {st.session_state.processed_docs}")
        st.markdown(f"Total Chunks: {st.session_state.total_chunks}")
        st.markdown("---")
        st.markdown("### 🔧 Settings")
        k_value = st.slider("Number of relevant chunks", 1, 10, 5)
        
        # Clear history button
        if st.button("🗑️ Clear History"):
            st.session_state.history = []
            st.success("History cleared!")

    # Main content
    st.markdown("<h1 class='custom-title'>🧠 SYNAPTYX - Document Analytics</h1>", unsafe_allow_html=True)
    
    # Document upload section
    st.markdown("### 📁 Document Upload")
    uploaded_files = st.file_uploader(
        "Upload your documents (PDF, DOCX, or TXT)",
        type=['pdf', 'docx', 'txt'],
        accept_multiple_files=True
    )

    if uploaded_files:
        for file in uploaded_files:
            with st.spinner(f"Processing {file.name}..."):
                result = asyncio.run(st.session_state.demo.process_document(file))
                
                if result["status"] == "success":
                    st.session_state.processed_docs += 1
                    st.session_state.total_chunks += result["chunks"]
                    
                    with st.expander(f"📄 {file.name} - Details"):
                        st.json(result["metadata"])
                    
                    st.success(f"Successfully processed {file.name}")
                else:
                    st.error(f"Error processing {file.name}: {result['error']}")

    # Query section
    st.markdown("### 💬 Ask Questions")
    query = st.text_input("What would you like to know about your documents?")
    
    if query:
        with st.spinner("Analyzing..."):
            result = asyncio.run(st.session_state.demo.query(query, k=k_value))
            
            if result["status"] == "success":
                # Add to history
                st.session_state.history.append({
                    "question": query,
                    "answer": result["answer"],
                    "confidence": result["confidence"],
                    "sources": result["sources"],
                    "timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
                })
                
                # Display answer
                st.markdown("#### Answer")
                st.info(result["answer"])
                
                col1, col2 = st.columns(2)
                with col1:
                    st.markdown("##### Confidence Score")
                    st.progress(result["confidence"])
                
                with col2:
                    st.markdown("##### Sources")
                    for source in result["sources"]:
                        st.markdown(f"- {source}")
            else:
                st.error(f"Error: {result['error']}")

    # History section
    if st.session_state.history:
        st.markdown("### 📜 History")
        for i, item in enumerate(reversed(st.session_state.history)):
            with st.expander(f"Q: {item['question']} ({item['timestamp']})"):
                st.markdown(f"**Answer:** {item['answer']}")
                st.markdown(f"**Confidence:** {item['confidence']:.2f}")
                st.markdown("**Sources:**")
                for source in item['sources']:
                    st.markdown(f"- {source}")

if __name__ == "__main__":
    main()