Update app.py

app.py

@@ -20,16 +20,17 @@ except FileNotFoundError:
     model = None
 
 # ===================================================================
-# 2. CORE PREDICTION LOGIC
+# 2. CORE PREDICTION LOGIC (THE "ENGINE")
 # ===================================================================
 
-def predict_glucose(current_glucose, hba1c, gender, hr_mean, hrv_rmssd, temp_mean):
+def predict_glucose_engine(current_glucose, hba1c, gender, hr_mean, hrv_rmssd, temp_mean):
     """
-    This is the core prediction engine. It takes the primary inputs from the UI,
-    simulates the rest, engineers all features, and returns a prediction.
+    This is the core prediction engine. It takes all inputs, engineers
+    the features, and returns a raw numerical prediction.
     """
     if model is None:
-        return "Model not loaded. Check file path."
+        # Return a specific value or raise an error to be handled by callers
+        return -1 
 
     # --- a) Simulate a realistic 1-hour glucose history ---
     history_size = 12
@@ -74,12 +75,24 @@ def predict_glucose(current_glucose, hba1c, gender, hr_mean, hrv_rmssd, temp_mea
 
     # --- e) Make Prediction ---
     prediction = model.predict(input_df)[0]
-    return f"{prediction:.0f} mg/dL"
+    return round(prediction) # FIX 1: Return the raw, rounded number
 
 # ===================================================================
-# 3. RANDOM SCENARIO GENERATOR
+# 3. WRAPPER FUNCTIONS FOR THE UI
 # ===================================================================
 
+def predict_and_format_for_ui(current_glucose, hba1c, gender, hr_mean, hrv_rmssd, temp_mean):
+    """
+    A wrapper function for the 'Predict' button. It calls the engine
+    and then formats the output for display in the textbox.
+    """
+    prediction_value = predict_glucose_engine(current_glucose, hba1c, gender, hr_mean, hrv_rmssd, temp_mean)
+    
+    if prediction_value == -1:
+        return "Model not loaded. Check file path."
+        
+    return f"{prediction_value} mg/dL" # Format the output string here
+
 def generate_random_scenario_and_predict():
     """Generates random values for all inputs and triggers a prediction."""
     # Generate random values
@@ -90,11 +103,16 @@ def generate_random_scenario_and_predict():
     random_hrv = np.random.randint(20, 70)
     random_temp = np.round(np.random.uniform(32.0, 36.0), 1)
 
-    # Get a prediction based on these random values
-    prediction = predict_glucose(random_glucose, random_hba1c, random_gender, random_hr, random_hrv, random_temp)
+    # FIX 2: Call the engine to get the number, then format it separately.
+    prediction_value = predict_glucose_engine(random_glucose, random_hba1c, random_gender, random_hr, random_hrv, random_temp)
+    
+    if prediction_value == -1:
+        prediction_str = "Model not loaded."
+    else:
+        prediction_str = f"{prediction_value} mg/dL"
     
     # Return all the generated values to update the UI
-    return random_glucose, random_hba1c, random_gender, random_hr, random_hrv, random_temp, prediction
+    return random_glucose, random_hba1c, random_gender, random_hr, random_hrv, random_temp, prediction_str
 
 # ===================================================================
 # 4. CREATE THE GRADIO INTERFACE
@@ -141,7 +159,7 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
 
     # --- Link UI components to functions ---
     predict_btn.click(
-        fn=predict_glucose,
+        fn=predict_and_format_for_ui, # FIX 3: Use the new UI wrapper function
         inputs=[glucose_input, hba1c_input, gender_input, hr_input, hrv_input, temp_input],
         outputs=[prediction_output]
     )