app change new

app.py

@@ -9,19 +9,21 @@ tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
 with open("label_types_encoded.json", "r") as fp:
     encode_genre_types = json.load(fp)
 
+genres = list(encode_genre_types.keys())
+
 inf_session = rt.InferenceSession('food-classifier-quantized.onnx')
 input_name = inf_session.get_inputs()[0].name
 output_name = inf_session.get_outputs()[0].name
 
 
-def classify_news_label(article):
+def classify_food_Ingredient(article):
     input_ids = tokenizer(article)['input_ids'][:512]
     logits = inf_session.run([output_name], {input_name: [input_ids]})[0]
     logits = torch.FloatTensor(logits)
     probs = torch.sigmoid(logits)[0]
-    return dict(zip(label, map(float, probs)))
+    return dict(zip(genres, map(float, probs)))
 
 
 label = gr.outputs.Label(num_top_classes=6)
-iface = gr.Interface(fn=classify_news_label, inputs="text", outputs=label)
+iface = gr.Interface(fn=classify_food_Ingredient, inputs="text", outputs=label)
 iface.launch(inline=False)