Delete app.py

app.py

@@ -1,217 +0,0 @@
-import gradio as gr
-import pandas as pd
-from agents.market_data.agent import MarketDataAgent
-from agents.market_data.storage import MarketDataStorage
-import plotly.graph_objects as go
-from datetime import datetime
-
-# Initialize
-symbols = ['AAPL', 'GOOGL', 'MSFT', 'TSLA', 'AMZN']
-agent = MarketDataAgent(symbols)
-storage = MarketDataStorage()
-
-def fetch_current_prices():
-    """Fetch and display current prices"""
-    data = agent.fetch_all_symbols()
-    
-    # Save to database
-    for symbol, info in data.items():
-        storage.save_realtime_data(info)
-    
-    # Create DataFrame for display
-    df = pd.DataFrame(data).T
-    df = df[['symbol', 'price', 'open', 'high', 'low', 'volume', 'pe_ratio']]
-    df['price'] = df['price'].apply(lambda x: f"${x:.2f}")
-    df['open'] = df['open'].apply(lambda x: f"${x:.2f}" if pd.notna(x) else "N/A")
-    df['high'] = df['high'].apply(lambda x: f"${x:.2f}" if pd.notna(x) else "N/A")
-    df['low'] = df['low'].apply(lambda x: f"${x:.2f}" if pd.notna(x) else "N/A")
-    df['volume'] = df['volume'].apply(lambda x: f"{x:,}" if pd.notna(x) else "N/A")
-    df['pe_ratio'] = df['pe_ratio'].apply(lambda x: f"{x:.2f}" if pd.notna(x) else "N/A")
-    
-    return df
-
-def get_historical_data(symbol, period):
-    """Fetch historical data and create chart"""
-    df = agent.fetch_historical_data(symbol, period)
-    
-    if df is None or df.empty:
-        return None, "No data available"
-    
-    # Save to database
-    storage.save_historical_data(symbol, df)
-    
-    # Create price chart
-    fig = go.Figure()
-    
-    # Candlestick chart
-    fig.add_trace(go.Candlestick(
-        x=df.index,
-        open=df['Open'],
-        high=df['High'],
-        low=df['Low'],
-        close=df['Close'],
-        name='Price'
-    ))
-    
-    fig.update_layout(
-        title=f'{symbol} Price History ({period})',
-        yaxis_title='Price ($)',
-        xaxis_title='Date',
-        template='plotly_white',
-        height=500
-    )
-    
-    # Create volume chart
-    fig_volume = go.Figure()
-    fig_volume.add_trace(go.Bar(
-        x=df.index,
-        y=df['Volume'],
-        name='Volume',
-        marker_color='lightblue'
-    ))
-    
-    fig_volume.update_layout(
-        title=f'{symbol} Trading Volume ({period})',
-        yaxis_title='Volume',
-        xaxis_title='Date',
-        template='plotly_white',
-        height=300
-    )
-    
-    # Create summary table
-    summary = pd.DataFrame({
-        'Metric': ['Current Price', 'Period High', 'Period Low', 'Average Volume', 'Total Days'],
-        'Value': [
-            f"${df['Close'].iloc[-1]:.2f}",
-            f"${df['High'].max():.2f}",
-            f"${df['Low'].min():.2f}",
-            f"{df['Volume'].mean():,.0f}",
-            f"{len(df)}"
-        ]
-    })
-    
-    return fig, fig_volume, summary
-
-def get_database_stats():
-    """Get statistics from database"""
-    latest = storage.get_latest_prices()
-    
-    if latest.empty:
-        return "No data in database yet. Fetch some prices first!"
-    
-    stats = f"""
-    📊 **Database Statistics**
-    
-    - Total Symbols Tracked: {len(latest)}
-    - Last Update: {latest['timestamp'].max()}
-    - Symbols: {', '.join(latest['symbol'].tolist())}
-    """
-    
-    return stats, latest[['symbol', 'price', 'timestamp']]
-
-# Create Gradio Interface
-with gr.Blocks(title="Market Data Agent - Demo", theme=gr.themes.Soft()) as demo:
-    
-    gr.Markdown("""
-    # 📈 Market Data Agent - Live Demo
-    ### Week 2: Multi-Agent Portfolio Optimization System
-    **Team:** Ashwini, Dibyendu Sarkar, Jyoti Ranjan Sethi
-    """)
-    
-    with gr.Tab("💰 Real-Time Prices"):
-        gr.Markdown("### Fetch current market data for tracked stocks")
-        
-        with gr.Row():
-            fetch_btn = gr.Button("🔄 Fetch Current Prices", variant="primary", size="lg")
-        
-        price_output = gr.Dataframe(
-            headers=['Symbol', 'Price', 'Open', 'High', 'Low', 'Volume', 'P/E Ratio'],
-            label="Current Market Data"
-        )
-        
-        fetch_btn.click(
-            fn=fetch_current_prices,
-            outputs=price_output
-        )
-    
-    with gr.Tab("📊 Historical Data"):
-        gr.Markdown("### View historical price charts and analysis")
-        
-        with gr.Row():
-            symbol_input = gr.Dropdown(
-                choices=['AAPL', 'GOOGL', 'MSFT', 'TSLA', 'AMZN'],
-                value='AAPL',
-                label="Select Stock"
-            )
-            period_input = gr.Dropdown(
-                choices=['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y'],
-                value='1mo',
-                label="Select Period"
-            )
-        
-        fetch_hist_btn = gr.Button("📈 Fetch Historical Data", variant="primary")
-        
-        with gr.Row():
-            price_chart = gr.Plot(label="Price Chart")
-        
-        with gr.Row():
-            volume_chart = gr.Plot(label="Volume Chart")
-        
-        summary_table = gr.Dataframe(label="Summary Statistics")
-        
-        fetch_hist_btn.click(
-            fn=get_historical_data,
-            inputs=[symbol_input, period_input],
-            outputs=[price_chart, volume_chart, summary_table]
-        )
-    
-    with gr.Tab("💾 Database Stats"):
-        gr.Markdown("### View data stored in local SQLite database")
-        
-        refresh_btn = gr.Button("🔄 Refresh Database Stats", variant="primary")
-        
-        db_stats = gr.Markdown()
-        db_data = gr.Dataframe(label="Latest Prices in Database")
-        
-        refresh_btn.click(
-            fn=get_database_stats,
-            outputs=[db_stats, db_data]
-        )
-    
-    with gr.Tab("ℹ️ About"):
-        gr.Markdown("""
-        ## Market Data Agent
-        
-        This is the **foundation agent** of our Multi-Agent Portfolio Optimization System.
-        
-        ### Features Implemented:
-        1. ✅ Real-time price fetching from Yahoo Finance
-        2. ✅ Historical data retrieval (1 day to 10 years)
-        3. ✅ Data validation and quality checks
-        4. ✅ SQLite database storage (thread-safe)
-        5. ✅ REST API for other agents
-        
-        ### Technologies Used:
-        - **Data Source:** yfinance (Yahoo Finance API)
-        - **Database:** SQLite with thread-safe connections
-        - **Charts:** Plotly for interactive visualizations
-        - **UI:** Gradio for web interface
-        - **Backend:** Python with FastAPI
-        
-        ### Next Week:
-        We will build the **Risk Management Agent** that will use this Market Data Agent
-        to calculate VaR, CVaR, and portfolio risk metrics.
-        
-        ---
-        **Project:** Intelligent Multi-Agent Portfolio Optimization System  
-        **Week:** 2 of 16  
-        **Date:** February 2026
-        """)
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch(
-        share=True,  # Creates public link you can share
-        server_name="0.0.0.0",
-        server_port=7860
-    )