Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+import numpy as np
+
+# Load the saved model
+model = load_model('time_prediction_model.h5')
+
+def predict_time(features):
+    # Assuming features is a list of input values
+    input_data = np.array(features).reshape(1, -1)
+    hour_prediction, minute_prediction = model.predict(input_data)
+    
+    # Convert predictions to readable format
+    predicted_hour = np.argmax(hour_prediction)
+    predicted_minute = np.argmax(minute_prediction)
+    
+    return f"{predicted_hour:02}:{predicted_minute:02}"
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict_time,
+    inputs=[gr.inputs.Textbox(lines=2, placeholder="Enter input features")],
+    outputs="text",
+    title="Time Prediction AI"
+)
+
+# Launch the interface
+interface.launch()