Update app.py

app.py

@@ -23,21 +23,18 @@ def predict_image(image):
     image = tf.keras.preprocessing.image.img_to_array(image)
     image = np.expand_dims(image, axis=0)
 
-    # Make a prediction
+   # Make a prediction
     prediction = model.predict(image)
 
-    # Get the probability of being 'Clean' or 'Carries'
-    probabilities = tf.nn.softmax(prediction, axis=-1)
-    predicted_class_index = np.argmax(probabilities)
-    if predicted_class_index == 0:
-        predicted_label = "Clean"
-        predicted_probability = probabilities[0][0] * 100  # Convert to percentage
-    else:
-        predicted_label = "Carries"
-        predicted_probability = probabilities[0][1] * 100  # Convert to percentage
+    # Get the probability of being 'Good'
+    probability_good = prediction[0][0]  # Assuming it's a binary classification
 
-    # Return the prediction result as a dictionary
-    return {"Predicted Label": predicted_label, "Evaluate the teeth": f"{predicted_probability:.2f}%"}
+    # Define the prediction result
+    result = {
+        "prediction": "Your Teeth are Good & You Don't Need To Visit Doctor" if probability_good > 0.5 else "Your Teeth are Bad & You Need To Visit Doctor"
+    }
+
+    return result
 
 
 # Create the interface
@@ -48,7 +45,7 @@ iface = gr.Interface(
     fn=predict_image,
     inputs=input_interface,
     outputs=output_interface,
-    title="<h1 style='color: lightgreen; text-align: center;'>Dentella</h1><p style='text-align: center; color: skyblue; font-size: 30px;'>Please Enter Your Teeth Here...</p>",)
+    title="<h1 style='color: lightgreen; text-align: center;'>Dentella</h1><p style='text-align: left; color: skyblue; font-size: 25px;'>Please Enter Your Teeth Here...</p>",)
 
 
 # Launch the interface