Create app.py

app.py

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+import yfinance as yf
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import gradio as gr
+import timesfm
+
+# Function to fetch stock data, generate forecast, and create an interactive plot
+def stock_forecast(ticker, start_date, end_date):
+    try:
+        # Fetch historical data
+        stock_data = yf.download(ticker, start=start_date, end=end_date)
+
+        # If the DataFrame has a MultiIndex for columns, drop the 'Ticker' level
+        if isinstance(stock_data.columns, pd.MultiIndex):
+            stock_data.columns = stock_data.columns.droplevel(level=1)
+
+        # Explicitly set column names
+        stock_data.columns = ['Close', 'High', 'Low', 'Open', 'Volume']
+
+        # Reset index to have 'Date' as a column
+        stock_data.reset_index(inplace=True)
+
+        # Select relevant columns and rename them
+        df = stock_data[['Date', 'Close']].rename(columns={'Date': 'ds', 'Close': 'y'})
+
+        # Ensure the dates are in datetime format
+        df['ds'] = pd.to_datetime(df['ds'])
+
+        # Add a unique identifier for the time series
+        df['unique_id'] = ticker
+
+        # Initialize the TimesFM model
+        tfm = timesfm.TimesFm(
+            hparams=timesfm.TimesFmHparams(
+                backend="pytorch",
+                per_core_batch_size=32,
+                horizon_len=30,  # Predicting the next 30 days
+                input_patch_len=32,
+                output_patch_len=128,
+                num_layers=50,
+                model_dims=1280,
+                use_positional_embedding=False,
+            ),
+            checkpoint=timesfm.TimesFmCheckpoint(
+                huggingface_repo_id="google/timesfm-2.0-500m-pytorch"
+            ),
+        )
+
+        # Forecast using the prepared DataFrame
+        forecast_df = tfm.forecast_on_df(
+            inputs=df,
+            freq="D",  # Daily frequency
+            value_name="y",
+            num_jobs=-1,
+        )
+
+        # Combine actual and forecasted data for plotting
+        combined_df = pd.concat([df, forecast_df], axis=0)
+
+        # Create an interactive plot with Plotly
+        fig = px.line(combined_df, x='ds', y=['y', 'timesfm'], labels={'value': 'Price', 'ds': 'Date'}, 
+                      title=f'{ticker} Stock Price Forecast')
+        
+        # Enhance interactivity
+        fig.update_layout(
+            legend_title_text='Type',
+            hovermode='x unified',  # Show hover info for all series at the same x-value
+            xaxis=dict(
+                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")
+                    ])
+                ),
+                rangeslider=dict(visible=True),  # Add a range slider
+                type="date"
+            ),
+            yaxis=dict(title="Price (USD)")
+        )
+
+        # Add custom hover data
+        fig.update_traces(
+            hovertemplate="<b>Date:</b> %{x}<br><b>Price:</b> %{y:.2f}<extra></extra>"
+        )
+
+        return fig
+
+    except Exception as e:
+        # Return an empty Plotly figure with an error message
+        error_fig = go.Figure()
+        error_fig.update_layout(
+            title=f"Error: {str(e)}",
+            xaxis_title="Date",
+            yaxis_title="Price",
+            annotations=[dict(text="No data available", x=0.5, y=0.5, showarrow=False)]
+        )
+        return error_fig
+
+# Create Gradio interface with an "Enter" button
+with gr.Blocks() as demo:
+    gr.Markdown("# Stock Price Forecast App")
+    gr.Markdown("Enter a stock ticker, start date, and end date to visualize historical and forecasted stock prices.")
+
+    with gr.Row():
+        ticker_input = gr.Textbox(label="Enter Stock Ticker", value="AAPL")
+        start_date_input = gr.Textbox(label="Enter Start Date (YYYY-MM-DD)", value="2022-01-01")
+        end_date_input = gr.Textbox(label="Enter End Date (YYYY-MM-DD)", value="2025-01-01")
+    
+    submit_button = gr.Button("Enter")
+    plot_output = gr.Plot()
+
+    # Link the button to the function
+    submit_button.click(
+        stock_forecast,
+        inputs=[ticker_input, start_date_input, end_date_input],
+        outputs=plot_output
+    )
+
+# Launch the Gradio app
+demo.launch()