Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import os
+
+# 1. Load the Model
+# We expect the .keras file to be in the same directory
+model = tf.keras.models.load_model("hk_transit_flow_net.keras")
+
+# Helper to map day names to integers
+DAY_MAP = {
+    "Sunday": 0, "Monday": 1, "Tuesday": 2, "Wednesday": 3,
+    "Thursday": 4, "Friday": 5, "Saturday": 6
+}
+
+def predict_eta(distance_meters, num_stops, hour, day_name, route_id):
+    try:
+        # 1. Prepare Inputs
+        # We must match the exact shape and types used in training
+        
+        # Handle empty route
+        if not route_id or route_id.strip() == "":
+            route_id = "UNKNOWN"
+            
+        inputs = {
+            'distance': np.array([[float(distance_meters)]]),
+            'num_stops': np.array([[float(num_stops)]]),
+            'hour': np.array([[int(hour)]]),
+            'day_of_week': np.array([[int(DAY_MAP[day_name])]]),
+            'route_id': tf.constant([[str(route_id)]], dtype=tf.string)
+        }
+        
+        # 2. Run Prediction
+        prediction = model.predict(inputs, verbose=0)
+        seconds = float(prediction[0][0])
+        
+        # 3. Format Output
+        minutes = int(seconds // 60)
+        rem_seconds = int(seconds % 60)
+        
+        return f"{minutes} min {rem_seconds} sec ({seconds:.1f}s)"
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# 3. Build the Interface
+iface = gr.Interface(
+    fn=predict_eta,
+    inputs=[
+        gr.Number(label="Distance (meters)", value=5000),
+        gr.Number(label="Number of Stops", value=10),
+        gr.Slider(minimum=0, maximum=23, step=1, label="Hour of Day (0-23)", value=9),
+        gr.Dropdown(choices=list(DAY_MAP.keys()), label="Day of Week", value="Monday"),
+        gr.Textbox(label="Route ID (Optional)", placeholder="e.g. 968+1+Yuen Long+Tin Hau", value="UNKNOWN")
+    ],
+    outputs="text",
+    title="HK-TransitFlow-Net 🚌",
+    description="""
+    **Hong Kong Bus ETA Predictor**
+    
+    This model uses Deep Learning to predict bus travel time based on distance, stops, and time context.
+    
+    *   **Distance:** Physical distance of the path in meters.
+    *   **Route ID:** Internal ID (e.g., `968+1+...`). If unknown, leave as UNKNOWN.
+    *   **Note:** Trained on KMB & CTB data.
+    """,
+    theme="soft"
+)
+
+# 4. Launch
+if __name__ == "__main__":
+    iface.launch()