Create app.py

app.py

@@ -0,0 +1,145 @@
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+import gradio as gr
+import yfinance as yf
+import finnhub
+import logging
+
+# Setup logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Initialize Finnhub client (for financial data)
+finnhub_client = finnhub.Client(api_key="your_finnhub_api_key")
+
+# Define Shariah compliance rules
+def is_shariah_compliant(row):
+    try:
+        # Check debt/asset ratio
+        if row['debt_to_asset_ratio'] > 0.33:
+            return False
+        # Check interest income
+        if row['interest_income'] / row['total_revenue'] > 0.05:
+            return False
+        # Check prohibited business activities
+        if row['alcohol_revenue'] > 0 or row['gambling_revenue'] > 0 or row['tobacco_revenue'] > 0:
+            return False
+        return True
+    except Exception as e:
+        logging.error(f"Error in Shariah compliance check: {e}")
+        return False
+
+def get_stock_data(market):
+    tickers = []
+    if market == 'US':
+        tickers = yf.Tickers("^GSPC").symbols
+    elif market == 'HK':
+        tickers = yf.Tickers("^HSI").symbols
+    elif market == 'IN':
+        tickers = yf.Tickers("^BSESN").symbols
+    else:
+        logging.warning(f"Market {market} is not supported")
+        return pd.DataFrame()
+
+    # Fetch financial data from Finnhub
+    data = []
+    for ticker in tickers:
+        try:
+            company_profile = finnhub_client.company_profile2(symbol=ticker)
+            financials = finnhub_client.company_basic_financials(symbol=ticker, metric='all')
+            
+            row = {
+                'stock_symbol': ticker,
+                'market': market,
+                'sector': company_profile.get('finnhubIndustry', 'Unknown'),
+                'debt_to_asset_ratio': financials['metric'].get('debtToAssets', np.nan),
+                'interest_income': financials['metric'].get('interestIncome', np.nan),
+                'total_revenue': financials['metric'].get('totalRevenue', np.nan),
+                'alcohol_revenue': 0,  # Placeholder, needs additional data source
+                'gambling_revenue': 0,  # Placeholder, needs additional data source
+                'tobacco_revenue': 0  # Placeholder, needs additional data source
+            }
+            data.append(row)
+        except Exception as e:
+            logging.error(f"Failed to fetch data for {ticker}: {e}")
+            continue
+    
+    return pd.DataFrame(data)
+
+def predict_stock_performance(market, sector, investment_horizon, model_type):
+    # Fetch stock data for the selected market
+    df = get_stock_data(market)
+    if df.empty:
+        return "No data available for the selected market."
+    
+    # Apply Shariah compliance filtering
+    df['is_shariah_compliant'] = df.apply(is_shariah_compliant, axis=1)
+    shariah_compliant_df = df[df['is_shariah_compliant']]
+    
+    if shariah_compliant_df.empty:
+        return "No Shariah-compliant stocks found."
+
+    # Check for required columns before training the model
+    required_columns = ['price_to_earnings', 'dividend_yield', 'revenue_growth']
+    for col in required_columns:
+        if col not in shariah_compliant_df.columns:
+            logging.error(f"Missing column: {col}")
+            return f"Error: Missing required financial data ({col})."
+    
+    # Financial analysis and forecasting
+    X = shariah_compliant_df[required_columns]
+    y = shariah_compliant_df.get('future_price', pd.Series())
+
+    if y.empty:
+        return "Error: Future price data not available."
+
+    # Train the selected model
+    try:
+        if model_type == 'linear_regression':
+            model = LinearRegression()
+        elif model_type == 'random_forest':
+            model = RandomForestRegressor()
+        else:
+            return "Invalid model type selected."
+
+        model.fit(X, y)
+
+        # Predict future stock performance
+        shariah_compliant_df['predicted_price'] = model.predict(X)
+    except Exception as e:
+        logging.error(f"Model training failed: {e}")
+        return "Error in model training or prediction."
+
+    # Rank and recommend the top Shariah-compliant stocks
+    top_stocks = shariah_compliant_df[shariah_compliant_df['sector'] == sector].sort_values('predicted_price', ascending=False).head(5)
+    
+    if top_stocks.empty:
+        return "No top stocks found in the selected sector."
+
+    # Prepare the output for the Gradio interface
+    output = '\n'.join(f"{row['stock_symbol']}: {row['predicted_price']}" for _, row in top_stocks.iterrows())
+    return output
+
+def update_sectors(market):
+    df = get_stock_data(market)
+    if df.empty:
+        return []
+
+    return df['sector'].unique().tolist()
+
+# Create the Gradio interface
+with gr.Blocks() as app:
+    with gr.Row():
+        market = gr.Dropdown(['US', 'HK', 'IN'], label='Market', interactive=True)
+        sector = gr.Dropdown([], label='Sector', interactive=True)
+    
+    investment_horizon = gr.Slider(1, 10, step=1, label='Investment Horizon (years)')
+    model_type = gr.Radio(['linear_regression', 'random_forest'], label='Model Type')
+
+    output = gr.Textbox(label='Recommended Shariah-Compliant Stocks')
+
+    market.change(fn=update_sectors, inputs=[market], outputs=[sector])
+    gr.Button('Predict').click(fn=predict_stock_performance, inputs=[market, sector, investment_horizon, model_type], outputs=[output])
+
+app.launch()