Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from statsmodels.tsa.arima.model import ARIMA
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+import matplotlib.pyplot as plt
+import io
+import base64
+
+# Load and preprocess data
+def load_data():
+    df = pd.read_csv("data (3).csv", skiprows=2, header=None)
+    header_df = pd.read_csv("data (3).csv", nrows=2, header=None)
+    
+    column_names = ['PN', 'Dummy_Project'] + [
+        f"{header_df.iloc[0, i]}_{header_df.iloc[1, i]}" for i in range(2, len(header_df.columns))
+    ]
+    
+    df.columns = column_names
+    id_vars = ['PN', 'Dummy_Project']
+    value_vars = column_names[2:]
+    
+    df_long = df.melt(
+        id_vars=id_vars,
+        value_vars=value_vars,
+        var_name='Time_Period',
+        value_name='y'
+    )
+    
+    df_long[['Year', 'Month']] = df_long['Time_Period'].str.split('_', expand=True)
+    df_long.drop(columns=['Time_Period'], inplace=True)
+    df_long['Year'] = df_long['Year'].astype(int)
+    df_long['Month'] = df_long['Month'].astype(str)
+    
+    df_long['Date'] = pd.to_datetime(
+        df_long['Year'].astype(str) + '-' + df_long['Month'] + '-01',
+        format='%Y-%b-%d',
+        errors='coerce'
+    )
+    
+    df_long.dropna(subset=['y'], inplace=True)
+    df_long.reset_index(drop=True, inplace=True)
+    
+    return df_long
+
+# Get available part numbers
+def get_part_numbers(df):
+    return df['PN'].unique().tolist()
+
+# Train ARIMA model and make predictions
+def train_arima(series, order=(5,1,0)):
+    model = ARIMA(series, order=order)
+    model_fit = model.fit()
+    forecast = model_fit.forecast(steps=10)
+    return model_fit, forecast
+
+# Create plot
+def create_plot(historical, forecast):
+    plt.figure(figsize=(12, 6))
+    plt.plot(historical.index, historical, label='Historical')
+    plt.plot(range(len(historical), len(historical) + len(forecast)), forecast, 
+             label='Forecast', color='orange')
+    plt.legend()
+    plt.title('Time Series Forecast')
+    plt.xlabel('Time Period')
+    plt.ylabel('Value')
+    
+    # Convert plot to base64 for Gradio
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    img_str = base64.b64encode(buf.read()).decode('utf-8')
+    buf.close()
+    
+    return f'<img src="data:image/png;base64,{img_str}" />'
+
+# Main prediction function
+def predict(part_number, model_name):
+    df = load_data()
+    df_part = df[df['PN'] == part_number].copy()
+    
+    # Prepare time series data
+    start_date = '2021-10-09'
+    date_range = pd.date_range(start=start_date, periods=len(df_part), freq='W')
+    df_part['Date'] = date_range
+    df_part.set_index('Date', inplace=True)
+    
+    series = df_part['y'].astype(float)
+    
+    if model_name == 'ARIMA':
+        model, forecast = train_arima(series)
+    
+    # Calculate metrics
+    train_size = int(len(series) * 0.8)
+    train, test = series[:train_size], series[train_size:]
+    
+    model_eval = ARIMA(train, order=(5,1,0))
+    model_fit_eval = model_eval.fit()
+    predictions = model_fit_eval.forecast(steps=len(test))
+    
+    rmse = np.sqrt(mean_squared_error(test, predictions))
+    mae = mean_absolute_error(test, predictions)
+    
+    plot_html = create_plot(series, forecast)
+    
+    metrics = f"""
+    Model Performance Metrics:
+    - RMSE: {rmse:.2f}
+    - MAE: {mae:.2f}
+    
+    Forecast for next 10 periods:
+    {', '.join([f'{x:.2f}' for x in forecast])}
+    """
+    
+    return metrics, plot_html
+
+# Create Gradio interface
+def create_interface():
+    df = load_data()
+    part_numbers = get_part_numbers(df)
+    
+    with gr.Blocks() as demo:
+        gr.Markdown("# Time Series Forecasting Dashboard")
+        
+        with gr.Row():
+            part_dropdown = gr.Dropdown(choices=part_numbers, label="Select Part Number")
+            model_dropdown = gr.Dropdown(choices=['ARIMA'], label="Select Model")
+        
+        predict_btn = gr.Button("Predict")
+        
+        with gr.Row():
+            metrics_output = gr.Textbox(label="Metrics and Forecast")
+            plot_output = gr.HTML(label="Forecast Plot")
+        
+        predict_btn.click(
+            fn=predict,
+            inputs=[part_dropdown, model_dropdown],
+            outputs=[metrics_output, plot_output]
+        )
+    
+    return demo
+
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()