Update app.py

app.py

@@ -17,26 +17,34 @@ def predict_image(image):
     # Save the image to a file-like object
     image_bytes = io.BytesIO()
     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)
-    img_array = np.expand_dims(image, axis=0)
-    outputs = model.predict(img_array)
-    predictions = tf.nn.softmax(outputs.logits, axis=-1)
-    predicted_class = np.argmax(predictions)
-    if predicted_class == 0:
-        predict_label = "Clean"
-    else:
-        predict_label = "Carries"
-    confidence = float(np.max(predictions))
-    prediction_dict = {"prediction": predict_label, "confidence": confidence}
-    #return prediction_dict
-    probability_good = outputs[0][0]
+    image = np.expand_dims(image, axis=0)
+
+    # Make a prediction
+    prediction = model.predict(image)
+
+    # Get the probability of being 'Good'
+    probability_good = prediction[0][0]  # Assuming it's a binary classification
+
+    # 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
+    #predictions = tf.nn.softmax(outputs.logits, axis=-1)
+    #predicted_class = np.argmax(predictions)
+    #if predicted_class == 0:
+        #predict_label = "Clean"
+    #else:
+        #predict_label = "Carries"
+    #confidence = float(np.max(predictions))
+    #prediction_dict = {"prediction": predict_label, "confidence": confidence}
+    #return prediction_dict
+   
 
 
 # Create the interface