Update app.py

app.py

@@ -13,12 +13,12 @@ from sklearn.metrics import mean_squared_error
 
 # Define periods
 PERIODS = {
-    "1 Month": "1mo", 
-    "3 Months": "3mo", 
-    "6 Months": "6mo", 
-    "1 Year": "1y", 
-    "5 Years": "5y", 
-    "10 Years": "10y", 
+    "1 Month": "1mo",
+    "3 Months": "3mo",
+    "6 Months": "6mo",
+    "1 Year": "1y",
+    "5 Years": "5y",
+    "10 Years": "10y",
     "Max": "max"
 }
 
@@ -39,6 +39,21 @@ def update_output_type(analysis_type):
     else:
         return gr.update(visible=False), gr.update(visible=True)
 
+def fetch_news(ticker):
+    try:
+        stock = yf.Ticker(ticker)
+        news = stock.news
+        news_summary = ""
+        for article in news[:5]:  # Limiting to 5 news articles
+            news_summary += f"### {article['title']}\n"
+            news_summary += f"**Source:** {article['publisher']}  \n"
+            news_summary += f"**Published At:** {article['providerPublishTime']}  \n"
+            news_summary += f"{article['summary']}  \n"
+            news_summary += f"[Read more]({article['link']})  \n\n"
+        return news_summary
+    except Exception as e:
+        return str(e)
+
 def summarize_asset(ticker):
     if ticker.strip() == "":
         return "Invalid input: Ticker symbol cannot be empty."
@@ -46,18 +61,55 @@ def summarize_asset(ticker):
     stock = yf.Ticker(ticker)
     try:
         info = stock.info
-        summary = f"""
-        **Summary for {info.get('shortName', 'N/A')} ({ticker}):**
-        
-        - **Current Price:** ${info.get('currentPrice', 'N/A')}
-        - **Market Cap:** ${info.get('marketCap', 'N/A')}
-        - **PE Ratio (TTM):** {info.get('trailingPE', 'N/A')}
-        - **EPS (TTM):** {info.get('trailingEps', 'N/A')}
-        - **Dividend Yield:** {info.get('dividendYield', 'N/A')*100 if info.get('dividendYield') else 'N/A'}%
-        - **52 Week High:** ${info.get('fiftyTwoWeekHigh', 'N/A')}
-        - **52 Week Low:** ${info.get('fiftyTwoWeekLow', 'N/A')}
-        - **Volume:** {info.get('volume', 'N/A')}
-        """
+        asset_type = info.get('quoteType', 'N/A')
+
+        summary = f"**Summary for {info.get('shortName', 'N/A')} ({ticker}):**\n\n"
+
+        current_price = info.get('currentPrice')
+        if current_price:
+            summary += f"- **Current Price:** ${current_price}\n"
+
+        market_cap = info.get('marketCap')
+        if market_cap:
+            summary += f"- **Market Cap:** ${market_cap}\n"
+
+        trailing_pe = info.get('trailingPE')
+        if trailing_pe:
+            summary += f"- **PE Ratio (TTM):** {trailing_pe}\n"
+
+        trailing_eps = info.get('trailingEps')
+        if trailing_eps:
+            summary += f"- **EPS (TTM):** {trailing_eps}\n"
+
+        dividend_yield = info.get('dividendYield')
+        if dividend_yield:
+            summary += f"- **Dividend Yield:** {dividend_yield * 100}%\n"
+
+        fifty_two_week_high = info.get('fiftyTwoWeekHigh')
+        if fifty_two_week_high:
+            summary += f"- **52 Week High:** ${fifty_two_week_high}\n"
+
+        fifty_two_week_low = info.get('fiftyTwoWeekLow')
+        if fifty_two_week_low:
+            summary += f"- **52 Week Low:** ${fifty_two_week_low}\n"
+
+        volume = info.get('volume')
+        if volume:
+            summary += f"- **Volume:** {volume}\n"
+
+        if asset_type == "ETF" or asset_type == "MUTUALFUND":
+            total_assets = info.get('totalAssets')
+            if total_assets:
+                summary += f"- **Total Assets:** ${total_assets}\n"
+
+            nav_price = info.get('navPrice')
+            if nav_price:
+                summary += f"- **NAV:** ${nav_price}\n"
+
+            expense_ratio = info.get('annualReportExpenseRatio')
+            if expense_ratio:
+                summary += f"- **Expense Ratio:** {expense_ratio * 100}%\n"
+
         return summary
     except Exception as e:
         return str(e)
@@ -233,18 +285,18 @@ def train_linear_regression_model(ticker, period, future_days):
 
     data['Close'] = data['Close'].fillna(method='ffill')
     close_prices = data['Close'].values.reshape(-1, 1)
-    
+
     X = np.array(range(len(close_prices))).reshape(-1, 1)
     y = close_prices
-    
+
     model = LinearRegression()
     model.fit(X, y)
-    
+
     future_X = np.array(range(len(close_prices), len(close_prices) + future_days)).reshape(-1, 1)
     future_predictions = model.predict(future_X)
-    
+
     predictions = model.predict(X)
-    
+
     fig = go.Figure()
     fig.add_trace(go.Scatter(x=data.index, y=close_prices.flatten(), mode='lines', name='Actual'))
     fig.add_trace(go.Scatter(x=data.index, y=predictions.flatten(), mode='lines', name='Predicted'))
@@ -265,18 +317,18 @@ def train_random_forest_model(ticker, period, future_days, n_estimators):
 
     data['Close'] = data['Close'].fillna(method='ffill')
     close_prices = data['Close'].values.reshape(-1, 1)
-    
+
     X = np.array(range(len(close_prices))).reshape(-1, 1)
     y = close_prices
-    
+
     model = RandomForestRegressor(n_estimators=n_estimators)
     model.fit(X, y.ravel())
-    
+
     future_X = np.array(range(len(close_prices), len(close_prices) + future_days)).reshape(-1, 1)
     future_predictions = model.predict(future_X)
-    
+
     predictions = model.predict(X)
-    
+
     fig = go.Figure()
     fig.add_trace(go.Scatter(x=data.index, y=close_prices.flatten(), mode='lines', name='Actual'))
     fig.add_trace(go.Scatter(x=data.index, y=predictions.flatten(), mode='lines', name='Predicted'))
@@ -289,14 +341,14 @@ def train_random_forest_model(ticker, period, future_days, n_estimators):
 with gr.Blocks() as demo:
     gr.Markdown("# Stock Analysis Tool")
     with gr.Tab("Summary"):
-        ticker = gr.Textbox(label="Ticker Symbol", value="AAPL")
+        ticker = gr.Textbox(label="Enter Yahoo Finance Asset Ticker", value="AAPL")
         summary_button = gr.Button("Generate Summary")
         summary_output = gr.Markdown()
         summary_button.click(fn=summarize_asset, inputs=ticker, outputs=summary_output)
-        
+
     with gr.Tab("Technical Analysis"):
         with gr.Column():
-            ticker = gr.Textbox(label="Ticker Symbol", value="AAPL")
+            ticker = gr.Textbox(label="Enter Yahoo Finance Asset Ticker", value="AAPL")
             period = gr.Dropdown(label="Period", choices=list(PERIODS.keys()), value="1 Year")
             analysis_type = gr.Radio(label="Analysis Type", choices=["Candlestick", "Moving Average", "Bollinger Bands", "RSI", "MACD", "ADX"], value="Candlestick")
             ma_length = gr.Number(label="Moving Average Length", value=20, visible=False)
@@ -312,7 +364,7 @@ with gr.Blocks() as demo:
             plot_button.click(fn=plot_technical_analysis, inputs=[ticker, period, analysis_type, ma_length, candle_period, adx_period], outputs=plot_output)
 
     with gr.Tab("Fundamental Analysis"):
-        ticker = gr.Textbox(label="Ticker Symbol", value="AAPL")
+        ticker = gr.Textbox(label="Enter Yahoo Finance Asset Ticker", value="AAPL")
         analysis_type = gr.Radio(label="Analysis Type", choices=["Financials", "Balance Sheet", "Cash Flow", "Dividends"], value="Financials")
         plot_button = gr.Button("Show Fundamental Data")
         plot_output = gr.Plot(visible=False)
@@ -322,7 +374,7 @@ with gr.Blocks() as demo:
         plot_button.click(fn=plot_fundamental_analysis, inputs=[ticker, analysis_type], outputs=[plot_output, html_output])
 
     with gr.Tab("Predictive Model"):
-        ticker = gr.Textbox(label="Ticker Symbol", value="AAPL")
+        ticker = gr.Textbox(label="Enter Yahoo Finance Asset Ticker", value="AAPL")
         period = gr.Dropdown(label="Period", choices=list(PERIODS.keys()), value="1 Year")
         epochs = gr.Number(label="Epochs", value=10)
         batch_size = gr.Number(label="Batch Size", value=32)
@@ -345,4 +397,4 @@ with gr.Blocks() as demo:
         train_rf_button.click(fn=train_random_forest_model, inputs=[ticker, period, future_days_rf, n_estimators], outputs=rf_output)
 
 # Launch the interface
-demo.launch()
+demo.launch(debug=True)