Update app.py

app.py

@@ -7,12 +7,14 @@ MODEL_PATH = "activity_model.keras"
 CLASS_NAMES = ["Stationary", "Walking", "Running"]
 EXPECTED_STEPS = 100
 
-model = tf.keras.models.load_model("activity_model.keras")
+model = tf.keras.models.load_model(MODEL_PATH)
 
 def read_signal(file_path):
     if file_path is None:
         raise gr.Error("Please upload a CSV file.")
+    
     df = pd.read_csv(file_path)
+    
     # Accept either a column named sensor_value or the first numeric column.
     if "sensor_value" in df.columns:
         values = df["sensor_value"].to_numpy(dtype=np.float32)
@@ -21,8 +23,10 @@ def read_signal(file_path):
         if numeric.shape[1] == 0:
             raise gr.Error("CSV must contain a numeric sensor column.")
         values = numeric.iloc[:, 0].to_numpy(dtype=np.float32)
+        
     if len(values) != EXPECTED_STEPS:
         raise gr.Error(f"Expected exactly {EXPECTED_STEPS} sensor readings, found {len(values)}.")
+        
     return values
 
 def predict_activity(file_path):
@@ -38,6 +42,7 @@ with gr.Blocks() as demo:
     output = gr.Label(label="Prediction", num_top_classes=3)
     button = gr.Button("Predict activity", variant="primary")
     button.click(predict_activity, inputs=file_input, outputs=output)
+    
     gr.Examples(
         examples=[["sample_stationary.csv"], ["sample_walking.csv"], ["sample_running.csv"]],
         inputs=file_input,