Create app.py

app.py

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+import joblib
+import numpy as np
+import pandas as pd
+import gradio as gr
+
+# Load the saved models
+encoder_file = "ordinal_encoder.joblib"
+scaler_file = "min_max_scaler.joblib"
+joblib_file = "best_random_forest_model_s.joblib"
+
+loaded_encoder = joblib.load(encoder_file)
+loaded_scaler = joblib.load(scaler_file)
+loaded_model = joblib.load(joblib_file)
+
+# Define the prediction function
+def predict_from_csv(file_path):
+    df = pd.read_csv(file_path)
+
+    # Convert date columns to datetime
+    df['appt_slot_date'] = pd.to_datetime(df['appt_slot_date'])
+    df['appt_date'] = pd.to_datetime(df['appt_date'])
+
+    # Calculate the days_difference
+    df['days_difference'] = (df['appt_slot_date'] - df['appt_date']).dt.days
+
+    input_columns = ['facilitytype', 'appt_type', 'service_name', 'facility_name', 'directorate_name', 'year', 'region', 'days_difference']
+    test_data = df[input_columns]
+    test_data = test_data.astype(str)
+
+    test_data_encoded = loaded_encoder.transform(test_data)
+    test_data_scaled = loaded_scaler.transform(test_data_encoded)
+
+    predictions = loaded_model.predict(test_data_scaled)
+
+    df['Prediction'] = predictions
+
+    df['Prediction'] = np.where(predictions == 0, "No Show", "Show")
+    print(df['Prediction'].value_counts())
+
+    output_file = "predictions.csv"
+    df.to_csv(output_file, index=False)
+
+    return output_file
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=predict_from_csv,
+    inputs=gr.File(label='Upload CSV file', type='filepath', file_count='single'),
+    outputs=gr.File(label='Download CSV file with predictions'),
+    title="Appointment Status Prediction in Saudi Arabian Health Care System",
+    description="Upload a CSV file with the required columns to get predictions."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch(debug=True)