init

app.py

@@ -74,6 +74,16 @@ input_text = st.text_input("Enter your text here")
 
 if st.button("Predict"):
 
+    inputs = tokenizer_emotion(input_text, return_tensors="pt")
+    # Get the model's predictions
+    outputs = model_emotion(**inputs)
+    # Get the predicted class
+    predicted_class_idx = torch.argmax(outputs.logits).item()
+    # Decode the predicted class
+    predicted_class = model_emotion.config.id2label[predicted_class_idx]
+    # Return the prediction
+    st.write({"predicted_emotion": predicted_class})
+
     # Tokenize the input text
     inputs = tokenizer_t_class.encode(input_text, return_tensors="pt")
     # Get the model's predictions
@@ -88,15 +98,4 @@ if st.button("Predict"):
     # Filter out subwords
     filtered_token_labels = [(token, label) for token, label in token_labels if not token.startswith("##")]
     # Return the tokens and their predicted labels
-    st.write({"token_labels": filtered_token_labels})
-
-
-    inputs = tokenizer_emotion(input.text, return_tensors="pt")
-    # Get the model's predictions
-    outputs = model_emotion(**inputs)
-    # Get the predicted class
-    predicted_class_idx = torch.argmax(outputs.logits).item()
-    # Decode the predicted class
-    predicted_class = model_emotion.config.id2label[predicted_class_idx]
-    # Return the prediction
-    st.write({"predicted_emotion": predicted_class})
+    st.write({"token_labels": filtered_token_labels})