Update app.py

app.py

@@ -64,7 +64,11 @@ def predict(r, g, b, activation, seed, neurons):
         model = tf.keras.models.load_model(keras_path)
         ann_pred = model.predict(X)[0][0]
 
-        # Rescale cholestrol concentration prediction in mM
+        # Rescale cholestrol concentration prediction in mM and adjust to zero if negative
+        if ann_pred < 0:
+            ann_pred = 0;
+        if lin_pred_rgb < 0:
+            lin_pred_rgb = 0;
         return ann_pred*50, lin_pred_rgb*50
 
     except Exception as e:
@@ -80,7 +84,7 @@ def update_neurons(activation, seed):
 
 # Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Cholestrol Concentration Prediction - ANN and Linear Model")
+    # gr.Markdown("# Cholestrol Concentration Prediction - ANN and Linear Model")
     gr.Markdown("# **Cholestrol Concentration Prediction - ANN and Linear Model**")
     gr.Markdown("Dynamically select models and predict cholesterol concentration.")
     
@@ -124,6 +128,8 @@ Professor, Centre for Nanoscience and Technology, Pondicherry University, Puduch
         ann_output = gr.Text(label="Cholestrol Conentration (mM) - ANN Model Prediction ")
         lin_rgb_output = gr.Text(label="Cholestrol Conentration (mM) - Linear Model Prediction")
 
+    gr.Markdown("* Predicted negative concentration adjusted to zero.")
+
     btn.click(
         fn=predict, 
         inputs=[r, g, b, activation, seed, neurons],