Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from autots import AutoTS
+import plotly.graph_objects as go
+
+# Function to handle forecasting
+def forecast_queues(data):
+    # Convert the input string to a pandas DataFrame
+    data = pd.read_csv(data)
+    
+    # Ensure the 'date' column is in the correct datetime format
+    data['date'] = pd.to_datetime(data['date'], errors='coerce')
+    
+    # Check for missing values and drop them if necessary
+    data = data.dropna(subset=['date', 'queue_length'])
+    
+    # Setup AutoTS for time-series forecasting
+    model = AutoTS(forecast_length=12, frequency='D', ensemble=True, model_list="all")
+    model = model.fit(data, date_col='date', value_col='queue_length')  # Adjust column names as per your CSV
+    
+    # Generate forecast for the next 12 time periods
+    forecast = model.predict()
+    
+    # Get the forecasted values
+    forecast_df = forecast.forecast
+    forecast_values = forecast_df['queue_length'].values.tolist()
+    
+    # Create a Plotly figure to visualize the forecast
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=forecast_df.index, y=forecast_values, mode='lines+markers', name='Forecast'))
+    fig.update_layout(title="Queue Length Forecast", xaxis_title="Date", yaxis_title="Queue Length")
+    
+    return fig
+
+# Gradio Interface function
+def gradio_interface(file):
+    return forecast_queues(file.name)
+
+# Define Gradio interface with file input and plot output
+iface = gr.Interface(fn=gradio_interface, 
+                     inputs=gr.File(label="Upload your historical queue data (CSV with date and queue_length columns)"), 
+                     outputs=gr.Plot(), 
+                     live=True, 
+                     title="Queue Length Forecasting with AutoTS",
+                     description="Upload a CSV file containing historical queue data with a date and queue_length columns, and get a forecast for the next 12 periods.")
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()