Update app.py

app.py

@@ -4,15 +4,13 @@ import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
 from datetime import datetime
-from io import BytesIO
-import base64
 
 def download_stock_data(ticker, start_date, end_date):
     stock = yf.Ticker(ticker)
     df = stock.history(start=start_date, end=end_date)
     return df
 
-def plot_interactive_chart(ticker, start_date, end_date, chart_type):
+def plot_interactive_logarithmic_stock_chart(ticker, start_date, end_date):
     try:
         stock = yf.Ticker(ticker)
         data = stock.history(start=start_date, end=end_date)
@@ -20,212 +18,86 @@ def plot_interactive_chart(ticker, start_date, end_date, chart_type):
         if data.empty:
             return "No data available for the specified date range."
 
-        if chart_type == "Logarithmic":
-            fig = create_logarithmic_chart(data, ticker)
-        else:
-            fig = create_candlestick_chart(data, ticker)
-
-        return fig
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
-
-def create_logarithmic_chart(data, ticker):
-    x = (data.index - data.index[0]).days
-    y = np.log(data['Close'])
-    slope, intercept = np.polyfit(x, y, 1)
-
-    future_days = 365 * 10
-    all_days = np.arange(len(x) + future_days)
-    log_trend = np.exp(intercept + slope * all_days)
-
-    inner_upper_band = log_trend * 2
-    inner_lower_band = log_trend / 2
-    outer_upper_band = log_trend * 4
-    outer_lower_band = log_trend / 4
-
-    extended_dates = pd.date_range(start=data.index[0], periods=len(all_days), freq='D')
-
-    fig = go.Figure()
-
-    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close Price', line=dict(color='blue')))
-    fig.add_trace(go.Scatter(x=extended_dates, y=log_trend, mode='lines', name='Log Trend', line=dict(color='red')))
-    fig.add_trace(go.Scatter(x=extended_dates, y=inner_upper_band, mode='lines', name='Inner Upper Band', line=dict(color='green')))
-    fig.add_trace(go.Scatter(x=extended_dates, y=inner_lower_band, mode='lines', name='Inner Lower Band', line=dict(color='green')))
-    fig.add_trace(go.Scatter(x=extended_dates, y=outer_upper_band, mode='lines', name='Outer Upper Band', line=dict(color='orange')))
-    fig.add_trace(go.Scatter(x=extended_dates, y=outer_lower_band, mode='lines', name='Outer Lower Band', line=dict(color='orange')))
-
-    fig.update_layout(
-        title=f'{ticker} Stock Price (Logarithmic Scale) with Extended Trend Lines and Outer Bands',
-        xaxis_title='Date',
-        yaxis_title='Price (Log Scale)',
-        yaxis_type="log",
-        height=800,
-        legend=dict(x=0.01, y=0.99, bgcolor='rgba(255, 255, 255, 0.8)'),
-        hovermode='x unified'
-    )
-
-    fig.update_xaxes(
-        rangeslider_visible=True,
-        rangeselector=dict(
-            buttons=list([
-                dict(count=1, label="1m", step="month", stepmode="backward"),
-                dict(count=6, label="6m", step="month", stepmode="backward"),
-                dict(count=1, label="YTD", step="year", stepmode="todate"),
-                dict(count=1, label="1y", step="year", stepmode="backward"),
-                dict(step="all")
-            ])
+        x = (data.index - data.index[0]).days
+        y = np.log(data['Close'])
+        slope, intercept = np.polyfit(x, y, 1)
+
+        future_days = 365 * 10
+        all_days = np.arange(len(x) + future_days)
+        log_trend = np.exp(intercept + slope * all_days)
+
+        inner_upper_band = log_trend * 2
+        inner_lower_band = log_trend / 2
+        outer_upper_band = log_trend * 4
+        outer_lower_band = log_trend / 4
+
+        extended_dates = pd.date_range(start=data.index[0], periods=len(all_days), freq='D')
+
+        fig = go.Figure()
+
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close Price', line=dict(color='blue')))
+        fig.add_trace(go.Scatter(x=extended_dates, y=log_trend, mode='lines', name='Log Trend', line=dict(color='red')))
+        fig.add_trace(go.Scatter(x=extended_dates, y=inner_upper_band, mode='lines', name='Inner Upper Band', line=dict(color='green')))
+        fig.add_trace(go.Scatter(x=extended_dates, y=inner_lower_band, mode='lines', name='Inner Lower Band', line=dict(color='green')))
+        fig.add_trace(go.Scatter(x=extended_dates, y=outer_upper_band, mode='lines', name='Outer Upper Band', line=dict(color='orange')))
+        fig.add_trace(go.Scatter(x=extended_dates, y=outer_lower_band, mode='lines', name='Outer Lower Band', line=dict(color='orange')))
+
+        fig.update_layout(
+            title=f'{ticker} Stock Price (Logarithmic Scale) with Extended Trend Lines and Outer Bands',
+            xaxis_title='Date',
+            yaxis_title='Price (Log Scale)',
+            yaxis_type="log",
+            height=800,
+            legend=dict(x=0.01, y=0.99, bgcolor='rgba(255, 255, 255, 0.8)'),
+            hovermode='x unified'
         )
-    )
 
-    return fig
-
-def create_candlestick_chart(data, ticker):
-    fig = go.Figure(data=[go.Candlestick(x=data.index,
-                open=data['Open'],
-                high=data['High'],
-                low=data['Low'],
-                close=data['Close'])])
-
-    fig.update_layout(
-        title=f'{ticker} Stock Price (Candlestick Chart)',
-        xaxis_title='Date',
-        yaxis_title='Price',
-        height=800,
-        hovermode='x unified'
-    )
-
-    fig.update_xaxes(
-        rangeslider_visible=True,
-        rangeselector=dict(
-            buttons=list([
-                dict(count=1, label="1m", step="month", stepmode="backward"),
-                dict(count=6, label="6m", step="month", stepmode="backward"),
-                dict(count=1, label="YTD", step="year", stepmode="todate"),
-                dict(count=1, label="1y", step="year", stepmode="backward"),
-                dict(step="all")
-            ])
+        fig.update_xaxes(
+            rangeslider_visible=True,
+            rangeselector=dict(
+                buttons=list([
+                    dict(count=1, label="1m", step="month", stepmode="backward"),
+                    dict(count=6, label="6m", step="month", stepmode="backward"),
+                    dict(count=1, label="YTD", step="year", stepmode="todate"),
+                    dict(count=1, label="1y", step="year", stepmode="backward"),
+                    dict(step="all")
+                ])
+            )
         )
-    )
 
-    return fig
-
-def export_data(ticker, start_date, end_date, format):
-    data = download_stock_data(ticker, start_date, end_date)
-    
-    if format == 'CSV':
-        output = BytesIO()
-        data.to_csv(output, index=True)
-        b64 = base64.b64encode(output.getvalue()).decode()
-        return f'data:text/csv;base64,{b64}'
-    elif format == 'Excel':
-        output = BytesIO()
-        with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
-            data.to_excel(writer, sheet_name='Stock Data', index=True)
-        b64 = base64.b64encode(output.getvalue()).decode()
-        return f'data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{b64}'
-    elif format == 'PDF':
-        output = BytesIO()
-        fig = go.Figure(data=[go.Table(
-            header=dict(values=list(data.columns)),
-            cells=dict(values=[data[col] for col in data.columns])
-        )])
-        fig.write_image(output, format='pdf')
-        b64 = base64.b64encode(output.getvalue()).decode()
-        return f'data:application/pdf;base64,{b64}'
+        return fig
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
 # Get the current date
 current_date = datetime.now().strftime("%Y-%m-%d")
 
-# Custom CSS to make charts full-width and larger, and apply additional styling
+# Custom CSS to make charts full-width and larger
 custom_css = """
 .container {max-width: 100% !important; padding: 0 !important;}
 .plot-container {height: 800px !important; width: 100% !important;}
 .react-plotly-container {height: 100% !important; width: 100% !important;}
-
-body {
-    background-color: #f0f0f0;
-    font-family: 'Helvetica', sans-serif;
-}
-.gradio-container {
-    margin: auto;
-    padding: 15px;
-    border-radius: 10px;
-    background-color: white;
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-}
-.gr-button {
-    background-color: #4CAF50;
-    border: none;
-    color: white;
-    text-align: center;
-    text-decoration: none;
-    display: inline-block;
-    font-size: 16px;
-    margin: 4px 2px;
-    cursor: pointer;
-    border-radius: 5px;
-    padding: 10px 24px;
-}
-.gr-button:hover {
-    background-color: #45a049;
-}
-.gr-form {
-    border: 1px solid #ddd;
-    border-radius: 5px;
-    padding: 15px;
-    margin-bottom: 20px;
-}
-.gr-input {
-    width: 100%;
-    padding: 12px 20px;
-    margin: 8px 0;
-    display: inline-block;
-    border: 1px solid #ccc;
-    border-radius: 4px;
-    box-sizing: border-box;
-}
-.gr-input:focus {
-    border-color: #4CAF50;
-    outline: none;
-}
-.gr-label {
-    font-weight: bold;
-    margin-bottom: 5px;
-}
 """
 
-# Create Gradio interface with updated theme
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft(), title="Log Charting Tool") as iface:
-    gr.Markdown("# Log Charting Tool")
-    gr.Markdown("Enter a stock ticker and date range to generate a chart and export data.")
+# Create Gradio interface
+with gr.Blocks(css=custom_css, title="Logarithmic Charting Tool") as iface:
+    gr.Markdown("# Alan's Logarithmic Charting Tool")
+    gr.Markdown("Enter a stock ticker and date range to generate a logarithmic chart.")
     
     with gr.Row():
         ticker = gr.Textbox(label="Stock Ticker", value="MSFT")
         start_date = gr.Textbox(label="Start Date", value="2015-01-01")
         end_date = gr.Textbox(label="End Date", value=current_date)
     
-    with gr.Row():
-        chart_type = gr.Radio(["Logarithmic", "Candlestick"], label="Chart Type", value="Logarithmic")
-        export_format = gr.Dropdown(["CSV", "Excel", "PDF"], label="Export Format", value="CSV")
-    
-    with gr.Row():
-        generate_button = gr.Button("Generate Chart")
-        export_button = gr.Button("Export Data")
+    submit_button = gr.Button("Generate Chart")
     
     with gr.Row():
-        chart_output = gr.Plot(label="Stock Chart")
-        export_output = gr.File(label="Exported Data")
-    
-    generate_button.click(
-        plot_interactive_chart,
-        inputs=[ticker, start_date, end_date, chart_type],
-        outputs=[chart_output]
-    )
+        log_plot = gr.Plot(label="Logarithmic Stock Chart")
     
-    export_button.click(
-        export_data,
-        inputs=[ticker, start_date, end_date, export_format],
-        outputs=[export_output]
+    submit_button.click(
+        plot_interactive_logarithmic_stock_chart,
+        inputs=[ticker, start_date, end_date],
+        outputs=[log_plot]
     )
 
 # Launch the app