Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import yfinance as yf
+import plotly.graph_objects as go
+from plotly.colors import n_colors
+from datetime import datetime, timedelta
+
+
+# Load stock data
+def load_stocks():
+    df = pd.read_csv('TASE_stock_list_2023.csv')
+    df['Symbol'] = df['Symbol'] + '.TA'
+    return df
+
+
+# Get stock data and calculate percentage change
+def get_stock_data(start_date, end_date, stocks):
+    data = {}
+    for _, row in stocks.iterrows():
+        symbol = row['Symbol']
+        name = row['Name']
+        stock = yf.Ticker(symbol)
+        hist = stock.history(start=start_date, end=end_date)
+        if not hist.empty:
+            initial_price = hist['Close'].iloc[0]
+            final_price = hist['Close'].iloc[-1]
+            pct_change = ((final_price - initial_price) / initial_price) * 100
+            if pct_change < 100:  # Only include stocks with pct change below 100
+                data[symbol] = {
+                    'name': name,
+                    'initial_price': initial_price,
+                    'final_price': final_price,
+                    'pct_change': pct_change
+                }
+    return data
+
+
+# Create bar chart
+def create_chart(data):
+    sorted_data = dict(sorted(data.items(), key=lambda x: x[1]['pct_change'], reverse=True)[:100])  # Top 100 stocks
+
+    fig = go.Figure()
+
+    # Generate a color scale from light green to dark green
+    colors = n_colors('rgb(144,238,144)', 'rgb(0,100,0)', len(sorted_data), colortype='rgb')
+
+    # Reverse the color order so that higher values get darker colors
+    colors = colors[::-1]
+
+    fig.add_trace(go.Bar(
+        x=list(sorted_data.keys()),
+        y=[v['pct_change'] for v in sorted_data.values()],
+        text=[f"{v['pct_change']:.2f}%" for v in sorted_data.values()],
+        textposition='inside',
+        textfont=dict(color='white'),  # Set text color to white for all bars
+        hovertext=[
+            f"{v['name']}<br>Symbol: {k}<br>Initial: {v['initial_price']:.2f}<br>Final: {v['final_price']:.2f}<br>Change: {v['pct_change']:.2f}%"
+            for k, v in sorted_data.items()],
+        hoverinfo='text',
+        marker_color=colors
+    ))
+    title = 'Stocks Performance'
+    fig.update_layout(
+        title=title,
+        xaxis_title='Stocks',
+        yaxis_title='Percentage Change',
+        hoverlabel=dict(
+            bgcolor="black",
+            font_size=12,
+            font_family="Rockwell",
+            font_color="white"  # This ensures all hover text is white
+        )
+    )
+    return fig
+
+
+# Main function
+def analyze_stocks(year, month, day, num_days):
+    stocks = load_stocks()
+    start_date = datetime(year, month, day)
+    end_date = start_date + timedelta(days=num_days)
+    data = get_stock_data(start_date, end_date, stocks)
+
+    chart = create_chart(data)
+
+    return chart
+
+
+# Set up Gradio interface
+current_date = datetime.now()
+
+with gr.Blocks() as demo:
+    gr.Markdown("# TASE Stock Performance Analyzer")
+    gr.Markdown("Analyze stock performances based on date range.")
+
+    with gr.Row():
+        year = gr.Dropdown(choices=list(range(2000, 2025)), label="Year", value=current_date.year)
+        month = gr.Dropdown(choices=list(range(1, 13)), label="Month", value=current_date.month)
+        day = gr.Dropdown(choices=list(range(1, 32)), label="Day", value=1)
+
+    num_days = gr.Slider(minimum=1, maximum=365, step=1, label="Number of Days", value=14)
+
+    submit_btn = gr.Button("Analyze")
+
+    output = gr.Plot()
+
+    submit_btn.click(
+        fn=analyze_stocks,
+        inputs=[year, month, day, num_days],
+        outputs=output
+    )
+
+demo.launch()