update my_app.py

my_app.py

@@ -4,17 +4,12 @@ from huggingface_hub import from_pretrained_keras
 from tensorflow.keras import mixed_precision
 
 # Load your trained models
-#model1 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101")
+model1 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101")
 #model2 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101")
 
-# Try different keras model
-model1 = from_pretrained_keras("NikiTricky/resnet50-food101")
-
 with open('classes.txt', 'r') as f:
     classes = [line.strip() for line in f]
 
-print("Class names: ", classes)
-
 # Add information about the models
 model1_info = """
 ### Model 1 Information
@@ -31,9 +26,8 @@ This model is based on the EfficientNetB0 architecture and was trained on the Fo
 def preprocess(image):
     print("before resize", image.shape)
     image = tf.image.resize(image, [224, 224])
- 
+    
     image = tf.expand_dims(image, axis=0)
-
     print("After expanddims", image.shape)
     return image
 
@@ -48,17 +42,16 @@ def predict(image):
 
     image = preprocess(image)
     print(mixed_precision.global_policy())
-    prediction = model1.predict(image)
+    prediction = model1.predict(image)[0]
     print("model prediction", prediction)
-    predicted_class = classes[int(tf.argmax(prediction, axis=1))]
-    confidence = tf.reduce_max(prediction).numpy()
-    return predicted_class, confidence
+    confidences = {model1.config['id2label'][str(i)]: float(prediction[i]) for i in range(101)}
+    return confidences
 
 iface = gr.Interface(
     fn=predict,
     inputs=[gr.Image()],
-    outputs=[gr.Textbox(label="Predicted Class"), gr.Textbox(label="Confidence")],
-    title="Transfer Learning Mini Project",
+    outputs=[gr.Label(num_top_classes=5)],
+    title="Food Vision Mini Project",
     description=f"{model1_info}\n",
 )