Update app.py

app.py

@@ -22,9 +22,9 @@ def predict(input_image):
        
         class_idx = np.argmax(predictions)
         class_label = labels[class_idx]
-        confidence = predictions[0][class_idx]
+        confidence = np.round(predictions[0][class_idx], 3)
 
-        return f"Predicted Class: {class_label} <br><br> Confidence Score: {confidence}"
+        return f"Predicted Class: {class_label}.  Confidence Score: {confidence}"
 
     except Exception as e:
         return f"An error occurred: {e}"
@@ -36,9 +36,9 @@ iface = gr.Interface(
     inputs=gr.inputs.Image(shape=(256, 256)),
     outputs="text",
     title="🌿 Plant Disease Detection",
-    description='<br> This specialized Image Classification model identifies the health status of plants, pinpointing conditions like Powdery Mildew or Rust. <br>\
-    The model is engineered on a Convolutional Neural Network and has been rigorously trained, evaluated, and validated. Kaggle Notebook: <a href="https://www.kaggle.com/code/lusfernandotorres/convolutional-neural-network-from-scratch">🧠 Convolutional Neural Network From Scratch</a>. <br> \
-    <br> Upload a photo of a plant for classification!',
+    description='<br> This is a specialized Image Classification model engineered to identify the health status of plants, specifically detecting conditions of Powdery Mildew or Rust. <br> \
+    This model is based on a Convolutional Neural Network that I have trained, evaluated, and validated on my Kaggle Notebook: <a href="https://www.kaggle.com/code/lusfernandotorres/convolutional-neural-network-from-scratch">🧠 Convolutional Neural Network From Scratch</a>. <br> \
+    <br> Upload a photo of a plant to see how the model classifies its status!',
     examples=examples
 )