remove gr.dropdown

my_app.py

@@ -4,7 +4,7 @@ from huggingface_hub import from_pretrained_keras
 
 # Load your trained models
 model1 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101")
-model2 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101")
+#model2 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101")
 
 with open('classes.txt', 'r') as f:
     classes = [line.strip() for line in f]
@@ -16,11 +16,11 @@ model1_info = """
 This model is based on the EfficientNetB0 architecture and was trained on the Food101 dataset.
 """
 
-model2_info = """
-### Model 2 Information
+#model2_info = """
+#### Model 2 Information
 
-This model is based on the EfficientNetB0 architecture and was trained on augmented data, providing improved generalization.
-"""
+#This model is based on the EfficientNetB0 architecture and was trained on augmented data, providing improved generalization.
+#"""
 
 def preprocess(image):
     print("before resize", image.shape)
@@ -30,15 +30,15 @@ def preprocess(image):
     print("After expanddims", image.shape)
     return image
 
-def predict(model_selection, image):
+def predict(image):
     # Choose the model based on the dropdown selection
-    print("---model_selection---", model_selection) #
-    model = model1 if model_selection == "EfficentNetB0 Fine Tune" else model2
+    #print("---model_selection---", model_selection) #
+    #model = model1 if model_selection == "EfficentNetB0 Fine Tune" else model2
 
-    print(model.summary())
+    #print(model.summary())
 
     image = preprocess(image)
-    prediction = model.predict(image)
+    prediction = model1.predict(image)
     print("model prediction", prediction)
     predicted_class = classes[int(tf.argmax(prediction, axis=1))]
     confidence = tf.reduce_max(prediction).numpy()
@@ -46,10 +46,10 @@ def predict(model_selection, image):
 
 iface = gr.Interface(
     fn=predict,
-    inputs=[gr.Dropdown(["EfficentNetB0 Fine Tune", "EfficentNetB0 Fine Tune Augmented"]), gr.Image()],
+    inputs=[gr.Image()],
     outputs=[gr.Textbox(label="Predicted Class"), gr.Textbox(label="Confidence")],
     title="Transfer Learning Mini Project",
-    description=f"{model1_info}\n\n{model2_info}",
+    description=f"{model1_info}\n",
 )
 
 iface.launch()