Update app.py

app.py

@@ -46,6 +46,11 @@ activations = sorted(activations)
 # Step 3: Prediction function
 def predict(r, g, b, activation, seed, neurons):
     try:
+        # Normalise R G B 
+        r = r/255
+        g = g/255
+        b = b/255
+        
         X = np.array([[r, g, b]])
         
         # Linear prediction (you can replace this with your actual linear model)
@@ -59,7 +64,8 @@ def predict(r, g, b, activation, seed, neurons):
         model = tf.keras.models.load_model(keras_path)
         ann_pred = model.predict(X)[0][0]
 
-        return ann_pred, lin_pred_rgb
+        # Rescale cholestrol concentration prediction in mM
+        return ann_pred*50, lin_pred_rgb*50
 
     except Exception as e:
         return f"Error: {str(e)}", ""
@@ -78,14 +84,14 @@ with gr.Blocks() as demo:
     gr.Markdown("Dynamically select models and predict cholesterol concentration.")
     
     with gr.Row():
-        r = gr.Number(label="R")
-        g = gr.Number(label="G")
-        b = gr.Number(label="B")
+        r = gr.Number(label="R (0 -255)")
+        g = gr.Number(label="G (0 -255)")
+        b = gr.Number(label="B (0 -255)")
 
     with gr.Row():
         activation = gr.Dropdown(choices=activations, label="Activation Function", interactive=True)
-        seed = gr.Dropdown(label="Seed", interactive=True)
-        neurons = gr.Dropdown(label="Neurons", interactive=True)
+        seed = gr.Dropdown(choices=seed, label="Seed", interactive=True)
+        neurons = gr.Dropdown(choices=neurons, label="Neurons", interactive=True)
 
     activation.change(update_seeds, inputs=[activation], outputs=[seed])
     seed.change(update_neurons, inputs=[activation, seed], outputs=[neurons])
@@ -94,8 +100,8 @@ with gr.Blocks() as demo:
         btn = gr.Button("Predict")
     
     with gr.Row():
-        ann_output = gr.Text(label="ANN Model Prediction")
-        lin_rgb_output = gr.Text(label="Linear RGB Prediction")
+        ann_output = gr.Text(label="Cholestrol Conentration (mM) - ANN Model Prediction ")
+        lin_rgb_output = gr.Text(label="Cholestrol Conentration (mM) - Linear Model Prediction")
 
     btn.click(
         fn=predict,