Update app.py

app.py

@@ -10,7 +10,7 @@ def food_not_food_classifier(text: str) -> Dict[str, float]:
     # Set up text classification pipeline
     food_not_food_classifier = pipeline(task="text-classification", 
                                         # Because our model is on Hugging Face already, we can pass in the model name directly
-                                        model="mrdbourke/learn_hf_food_not_food_text_classifier-distilbert-base-uncased", # link to model on HF Hub
+                                        model="devagonal/bert-f1-durga-muhammad", # link to model on HF Hub
                                         device="cuda" if torch.cuda.is_available() else "cpu",
                                         top_k=None) # return all possible scores (not just top-1)
     
@@ -36,10 +36,10 @@ See [source code](https://github.com/mrdbourke/learn-huggingface/blob/main/noteb
 demo = gr.Interface(fn=food_not_food_classifier, 
              inputs="text", 
              outputs=gr.Label(num_top_classes=2), # show top 2 classes (that's all we have)
-             title="🍗🚫🥑 Food or Not Food Text Classifier",
+             title="Bert F1 Durga Muhammad",
              description=description,
-             examples=[["I whipped up a fresh batch of code, but it seems to have a syntax error."],
-                       ["A delicious photo of a plate of scrambled eggs, bacon and toast."]])
+             examples=[["siapakah durga"],
+                       ["siapakah muhammad"]])
 
 # 4. Launch the interface
 if __name__ == "__main__":