Bug fixes in the app, with both the evaluate function and the download button

app.py

@@ -1,11 +1,11 @@
 import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
 import gradio as gr
+import tempfile
 
 from statsforecast import StatsForecast
 from statsforecast.models import (
-    HistoricAverage,
+    HistoricalAverage,
     Naive,
     SeasonalNaive,
     WindowAverage,
@@ -15,36 +15,46 @@ from statsforecast.models import (
 )
 
 from utilsforecast.evaluation import evaluate
-import tempfile
 
-# Function to load and process the CSV file
+# Function to load and process uploaded CSV
 def load_data(file):
     if file is None:
         return None, "Please upload a CSV file"
-    
     try:
-        # Safe read using file-like object
         df = pd.read_csv(file)
-        
-        # Check for required columns
         required_cols = ['unique_id', 'ds', 'y']
         missing_cols = [col for col in required_cols if col not in df.columns]
-        
         if missing_cols:
             return None, f"Missing required columns: {', '.join(missing_cols)}"
-        
-        # Convert 'ds' to datetime
         df['ds'] = pd.to_datetime(df['ds'])
-        
-        # Sort by date
         df = df.sort_values(['unique_id', 'ds'])
-        
         return df, "Data loaded successfully!"
-    
     except Exception as e:
         return None, f"Error loading data: {str(e)}"
 
-# Forecasting logic
+# Function to generate and return a plot
+def create_forecast_plot(forecast_df, original_df):
+    plt.figure(figsize=(10, 6))
+    unique_ids = forecast_df['unique_id'].unique()
+    forecast_cols = [col for col in forecast_df.columns if col not in ['unique_id', 'ds']]
+
+    for unique_id in unique_ids:
+        original_data = original_df[original_df['unique_id'] == unique_id]
+        plt.plot(original_data['ds'], original_data['y'], 'k-', label='Actual')
+        forecast_data = forecast_df[forecast_df['unique_id'] == unique_id]
+        for col in forecast_cols:
+            if col in forecast_data.columns:
+                plt.plot(forecast_data['ds'], forecast_data[col], label=col)
+
+    plt.title('Forecasting Results')
+    plt.xlabel('Date')
+    plt.ylabel('Value')
+    plt.legend()
+    plt.grid(True)
+    fig = plt.gcf()
+    return fig
+
+# Main forecasting logic
 def run_forecast(
     file,
     frequency,
@@ -68,99 +78,99 @@ def run_forecast(
         return None, None, None, message
 
     models = []
-    
+    model_aliases = []
+
     if use_historical_avg:
-        models.append(HistoricAverage(alias='historical_average'))
+        models.append(HistoricalAverage(alias='historical_average'))
+        model_aliases.append('historical_average')
     if use_naive:
         models.append(Naive(alias='naive'))
+        model_aliases.append('naive')
     if use_seasonal_naive:
         models.append(SeasonalNaive(m=seasonality, alias='seasonal_naive'))
+        model_aliases.append('seasonal_naive')
     if use_window_avg:
         models.append(WindowAverage(window_size=window_size, alias='window_average'))
+        model_aliases.append('window_average')
     if use_seasonal_window_avg:
         models.append(SeasonalWindowAverage(m=seasonality, window_size=seasonal_window_size, alias='seasonal_window_average'))
+        model_aliases.append('seasonal_window_average')
     if use_autoets:
         models.append(AutoETS(alias='autoets'))
+        model_aliases.append('autoets')
     if use_autoarima:
         models.append(AutoARIMA(alias='autoarima'))
+        model_aliases.append('autoarima')
 
     if not models:
         return None, None, None, "Please select at least one forecasting model"
 
     sf = StatsForecast(models=models, freq=frequency, n_jobs=-1)
-    
+
     try:
         if eval_strategy == "Cross Validation":
             cv_results = sf.cross_validation(df=df, h=horizon, step_size=step_size, n_windows=num_windows)
-            evaluation = evaluate(cv_results, df, metrics=['me', 'mae', 'rmse', 'mape'])
+            evaluation = evaluate(df=cv_results, metrics=['me', 'mae', 'rmse', 'mape'], models=model_aliases)
             eval_df = pd.DataFrame(evaluation).reset_index()
             fig_forecast = create_forecast_plot(cv_results, df)
             return eval_df, cv_results, fig_forecast, "Cross validation completed successfully!"
-        else:
+
+        else:  # Fixed window
             train_size = len(df) - horizon
             if train_size <= 0:
                 return None, None, None, f"Not enough data for horizon={horizon}"
-            
+
             train_df = df.iloc[:train_size]
             test_df = df.iloc[train_size:]
             sf.fit(train_df)
             forecast = sf.predict(h=horizon)
-            evaluation = evaluate(forecast, test_df, metrics=['me', 'mae', 'rmse', 'mape'])
+            evaluation = evaluate(df=forecast, metrics=['me', 'mae', 'rmse', 'mape'], models=model_aliases)
             eval_df = pd.DataFrame(evaluation).reset_index()
             fig_forecast = create_forecast_plot(forecast, df)
             return eval_df, forecast, fig_forecast, "Fixed window evaluation completed successfully!"
-    
+
     except Exception as e:
         return None, None, None, f"Error during forecasting: {str(e)}"
 
-# Forecast plot
-def create_forecast_plot(forecast_df, original_df):
-    plt.figure(figsize=(10, 6))
-    unique_ids = forecast_df['unique_id'].unique()
-    forecast_cols = [col for col in forecast_df.columns if col not in ['unique_id', 'ds']]
-    
-    for unique_id in unique_ids:
-        original_data = original_df[original_df['unique_id'] == unique_id]
-        plt.plot(original_data['ds'], original_data['y'], 'k-', label='Actual')
-        forecast_data = forecast_df[forecast_df['unique_id'] == unique_id]
-        for col in forecast_cols:
-            if col in forecast_data.columns:
-                plt.plot(forecast_data['ds'], forecast_data[col], label=col)
-    
-    plt.title('Forecasting Results')
-    plt.xlabel('Date')
-    plt.ylabel('Value')
-    plt.legend()
-    plt.grid(True)
-    fig = plt.gcf()
-    return fig
-
-# Download sample file (placeholder path)
+# Sample CSV file generation
 def download_sample():
-    return "sample_data.csv"
-
-# Gradio UI
+    sample_data = """unique_id,ds,y
+series1,2023-01-01,100
+series1,2023-01-02,105
+series1,2023-01-03,102
+series1,2023-01-04,107
+series1,2023-01-05,104
+series1,2023-01-06,110
+series1,2023-01-07,108
+series1,2023-01-08,112
+series1,2023-01-09,115
+series1,2023-01-10,118
+series1,2023-01-11,120
+series1,2023-01-12,123
+series1,2023-01-13,126
+series1,2023-01-14,129
+series1,2023-01-15,131
+"""
+    temp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w', newline='')
+    temp.write(sample_data)
+    temp.close()
+    return temp.name
+
+# Gradio interface
 with gr.Blocks(title="StatsForecast Demo") as app:
     gr.Markdown("# 📈 StatsForecast Demo App")
-    gr.Markdown("Upload a CSV with `unique_id`, `ds`, `y` columns and configure forecasting models.")
+    gr.Markdown("Upload a CSV with `unique_id`, `ds`, and `y` columns to apply forecasting models.")
 
     with gr.Row():
         with gr.Column(scale=2):
             file_input = gr.File(label="Upload CSV file", file_types=[".csv"])
+
             download_btn = gr.Button("Download Sample Data")
-            download_output = gr.File(interactive=False, label="Sample Data", visible=False)
+            download_output = gr.File(label="Click to download", visible=True)
             download_btn.click(fn=download_sample, outputs=download_output)
 
-            frequency = gr.Dropdown(
-                choices=["H", "D", "WS", "MS", "QS", "YS"],
-                label="Frequency",
-                value="D"
-            )
-            eval_strategy = gr.Radio(
-                choices=["Fixed Window", "Cross Validation"],
-                label="Evaluation Strategy",
-                value="Cross Validation"
-            )
+            frequency = gr.Dropdown(choices=["H", "D", "WS", "MS", "QS", "YS"], label="Frequency", value="D")
+            eval_strategy = gr.Radio(choices=["Fixed Window", "Cross Validation"], label="Evaluation Strategy", value="Cross Validation")
             horizon = gr.Slider(1, 100, value=14, label="Horizon")
             step_size = gr.Slider(1, 50, value=5, label="Step Size")
             num_windows = gr.Slider(1, 20, value=3, label="Number of Windows")
@@ -197,4 +207,4 @@ with gr.Blocks(title="StatsForecast Demo") as app:
     )
 
 if __name__ == "__main__":
-    app.launch()
+    app.launch(share=True)