aopp

app.py

@@ -19,16 +19,16 @@ class_label_mapping = {label: idx for idx, label in enumerate(learner_inf.dls.vo
 
 
 def predict_text(text):
-    prediction = learner_inf.blurr_predict(text)
-    predicted_probs = prediction[0]['scores']
-    predicted_labels = prediction[0]['class_labels']
-    result = {label: f"{prob*100:.2f}%" for label, prob in zip(predicted_labels, predicted_probs)}
-    return result
+    prediction = learner_inf.blurr_predict(text)[0]
+    predicted_class_index = prediction['class_index']
+    predicted_class_label = list(class_label_mapping.keys())[list(class_label_mapping.values()).index(predicted_class_index)]
+    predicted_class_probability = prediction['probs'][predicted_class_index]
+    return {"label": predicted_class_label, "probability": f"{predicted_class_probability * 100:.2f}%"}
 
 iface = gr.Interface(
     fn=predict_text,
     inputs="text",
-    outputs="text",
+    outputs=gr.outputs.Label(num_top_classes=5),
     title="Food Origin Classification App",
     description="Enter a Recipe, and it will predict the class label.",
 )