update

pages/1_Demo_1.py

@@ -42,6 +42,7 @@ else:
     data_size = st.slider('Select number of samples per category:', min_value=1, max_value=50,
                           value=st.session_state['data_size'])
     st.session_state['data_size'] = data_size
+
     if st.button('Show Data'):
         st.session_state['female_bold'] = sample(
             [p for p in st.session_state['bold'] if p['category'] == 'American_actresses'], data_size)
@@ -49,31 +50,35 @@ else:
             [p for p in st.session_state['bold'] if p['category'] == 'American_actors'], data_size)
 
         st.write(f'Sampled {data_size} female and male American actors.')
+        st.write('**Female Samples:**', pd.DataFrame(st.session_state['female_bold']))
+        st.write('**Male Samples:**', pd.DataFrame(st.session_state['male_bold']))
 
     if st.session_state['female_bold'] and st.session_state['male_bold']:
-        st.subheader('Step 2: Generated Text')
+        st.subheader('Step 2: Generate Text')
+
         if st.button('Generate Text'):
             GPT2 = gpt2()
             st.session_state['male_prompts'] = [p['prompts'][0] for p in st.session_state['male_bold']]
             st.session_state['female_prompts'] = [p['prompts'][0] for p in st.session_state['female_bold']]
 
+            progress_bar = st.progress(0)
+
             st.write('Generating text for male prompts...')
             male_generation = GPT2.text_generation(st.session_state['male_prompts'], pad_token_id=50256, max_length=50,
                                                    do_sample=False, truncation=True)
-
-            print(male_generation)
             st.session_state['male_continuations'] = [gen[0]['generated_text'].replace(prompt, '') for gen, prompt in
                                                       zip(male_generation, st.session_state['male_prompts'])]
 
+            progress_bar.progress(50)
+
             st.write('Generating text for female prompts...')
             female_generation = GPT2.text_generation(st.session_state['female_prompts'], pad_token_id=50256,
                                                      max_length=50, do_sample=False, truncation=True)
             st.session_state['female_continuations'] = [gen[0]['generated_text'].replace(prompt, '') for gen, prompt in
                                                         zip(female_generation, st.session_state['female_prompts'])]
 
-            st.write('Generated {} male continuations'.format(len(st.session_state['male_continuations'])))
-            st.write('Generated {} female continuations'.format(len(st.session_state['female_continuations'])))
-
+            progress_bar.progress(100)
+            st.write('Text generation completed.')
 
     if st.session_state.get('male_continuations') and st.session_state.get('female_continuations'):
         st.subheader('Step 3: Sample Generated Texts')
@@ -86,14 +91,15 @@ else:
             st.subheader('Step 4: Regard Results')
             regard = Regard("compare")
             st.write('Computing regard results to compare male and female continuations...')
-            regard_results = regard.compute(data=st.session_state['male_continuations'],
-                                            references=st.session_state['female_continuations'])
-            st.write('**Raw Regard Results:**')
-            st.json(regard_results)
-
-            st.write('Computing average regard results for comparative analysis...')
-            regard_results_avg = regard.compute(data=st.session_state['male_continuations'],
-                                                references=st.session_state['female_continuations'],
-                                                aggregation='average')
-            st.write('**Average Regard Results:**')
-            st.json(regard_results_avg)
+
+            with st.spinner('Computing regard results...'):
+                regard_results = regard.compute(data=st.session_state['male_continuations'],
+                                                references=st.session_state['female_continuations'])
+                st.write('**Raw Regard Results:**')
+                st.json(regard_results)
+
+                regard_results_avg = regard.compute(data=st.session_state['male_continuations'],
+                                                    references=st.session_state['female_continuations'],
+                                                    aggregation='average')
+                st.write('**Average Regard Results:**')
+                st.json(regard_results_avg)