- prints fix

app.py

@@ -60,29 +60,29 @@ if generate_button:
             )
         )
 
+        # Displaying the input and model's output
+        st.write(f"Generated Tweet: {generated_tweet[0]['generated_text']}")
+
     with st.spinner('Generating the Stance-Aware ABSA output...'):
         # Call the model with the aspects inputs
         absa_output = absa(absa_prompt.format(generated_tweet=generated_tweet[0]['generated_text']))
         stances = [x.strip() for x in absa_output[0]['generated_text'].split(',')]
 
+        st.write("Stance-Aware ABSA Output:")
+        st.write(f"{stances}")
+
     with st.spinner('Classifying the toxicity...'):
         # Call the model with the input text
         model_output = classifier(generated_tweet[0]['generated_text'])
         output = model_output[0]
 
-# Displaying the input and model's output
-st.write(f"Generated Tweet: {generated_tweet[0]['generated_text']}")
-
-st.write("Stance-Aware ABSA Output:")
-st.write(f"{stances}")
-
-st.write("Toxicity Classifier Output:")
-for i in range(3):
-    if output[i]['label'] == 'LABEL_0':
-        # st.write(f"Non-Toxic Content: {output[i]['score']*100:.1f}%")
-        print(f"Non-Toxic Content: {output[i]['score']*100:.1f}%")
-    elif output[i]['label'] == 'LABEL_1':
-        # st.write(f"Toxic Content: {output[i]['score']*100:.1f}%")
-        print(f"Toxic Content: {output[i]['score']*100:.1f}%")
-    else:
-        continue
+        st.write("Toxicity Classifier Output:")
+        for i in range(3):
+            if output[i]['label'] == 'LABEL_0':
+                # st.write(f"Non-Toxic Content: {output[i]['score']*100:.1f}%")
+                print(f"Non-Toxic Content: {output[i]['score']*100:.1f}%")
+            elif output[i]['label'] == 'LABEL_1':
+                # st.write(f"Toxic Content: {output[i]['score']*100:.1f}%")
+                print(f"Toxic Content: {output[i]['score']*100:.1f}%")
+            else:
+                continue