Update app.py

app.py

@@ -19,22 +19,25 @@ def predict_image(image):
     image.save(image_bytes, format="JPEG")
 
     # Load the image from the file-like object
-    image = tf.keras.preprocessing.image.load_img(image_bytes, target_size=(256, 256))
-    image = tf.keras.preprocessing.image.img_to_array(image)
+    image = tf.keras.preprocessing.image.load_img(image_bytes, target_size=(256, 256,3))
+    image = np.array(image)/255
     image = np.expand_dims(image, axis=0)
 
-   # Make a prediction
+    # Make a prediction
     prediction = model.predict(image)
 
-    # Get the probability of being 'Good'
-    probability_good = prediction[0][0]  # Assuming it's a binary classification
+    # 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 == 1:
+        predicted_label = "Clean"
+        predicted_probability = probabilities[0][1] * 100  # Convert to percentage
+    elif predicted_class_index == 0:
+        predicted_label = "Carries"
+        predicted_probability = probabilities[0][0] * 100  # Convert to percentage
 
-    # 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
+    # Return the prediction result as a dictionary
+    return {"Predicted Label": predicted_label}
 
 
 # Create the interface
@@ -44,9 +47,7 @@ output_interface = "json"
 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: left; color: skyblue; font-size: 25px;'>Please Enter Your Teeth Here...</p>",)
-
+    outputs=output_interface)
 
 # Launch the interface
 iface.launch(share=True)