update

pages/1_Demo_1.py

@@ -9,6 +9,7 @@ import os
 # Set up the Streamlit interface
 st.title('Gender Bias Analysis in Text Generation')
 
+
 def check_password():
     def password_entered():
         if password_input == os.getenv('PASSWORD'):
@@ -22,55 +23,74 @@ def check_password():
     if submit_button and not st.session_state.get('password_correct', False):
         st.error("Please enter a valid password to access the demo.")
 
+
 if not st.session_state.get('password_correct', False):
     check_password()
 else:
     st.sidebar.success("Password Verified. Proceed with the demo.")
 
-    st.subheader('Loading and Processing Data')
-    st.write('Loading the BOLD dataset...')
-    bold = load_dataset("AlexaAI/bold", split="train")
-
-    # Allow user to set the sample size
-    data_size = st.sidebar.slider('Select number of samples per category:', min_value=1, max_value=50, value=10)
-
-    st.write(f'Sampling {data_size} female and male American actors...')
-    female_bold = sample([p for p in bold if p['category'] == 'American_actresses'], data_size)
-    male_bold = sample([p for p in bold if p['category'] == 'American_actors'], data_size)
-
-    male_prompts = [p['prompts'][0] for p in male_bold]
-    female_prompts = [p['prompts'][0] for p in female_bold]
-
-    GPT2 = gpt2()
-
-    st.write('Generating text for male prompts...')
-    male_generation = GPT2.text_generation(male_prompts, pad_token_id=50256, max_length=50, do_sample=False,truncation=True)
-    print(male_generation)
-    male_continuations = [gen[0]['generated_text'].replace(prompt, '') for gen, prompt in zip(male_generation, male_prompts)]
-
-    st.write('Generating text for female prompts...')
-
-    female_generation = GPT2.text_generation(female_prompts, pad_token_id=50256, max_length=50, do_sample=False,truncation=True)
-    print(male_generation)
-    female_continuations = [gen[0]['generated_text'].replace(prompt, '') for gen, prompt in zip(male_generation, male_prompts)]
-
-    st.write('Generated {} male continuations'.format(len(male_continuations)))
-    st.write('Generated {} female continuations'.format(len(female_continuations)))
-
-    st.subheader('Sample Generated Texts')
-    st.write('**Male Prompt:**', male_prompts[0])
-    st.write('**Male Continuation:**', male_continuations[0])
-    st.write('**Female Prompt:**', female_prompts[0])
-    st.write('**Female Continuation:**', female_continuations[0])
-
-    regard = Regard("compare")
-    st.write('Computing regard results to compare male and female continuations...')
-    regard_results = regard.compute(data=male_continuations, references=female_continuations)
-    st.subheader('Regard Results')
-    st.write('**Raw Regard Results:**')
-    st.json(regard_results)
-
-    st.write('Computing average regard results for comparative analysis...')
-    regard_results_avg = regard.compute(data=male_continuations, references=female_continuations, aggregation='average')
-    st.write('**Average Regard Results:**')
-    st.json(regard_results_avg)
+    if 'data_size' not in st.session_state:
+        st.session_state['data_size'] = 10
+    if 'bold' not in st.session_state:
+        st.session_state['bold'] = load_dataset("AlexaAI/bold", split="train")
+    if 'female_bold' not in st.session_state:
+        st.session_state['female_bold'] = []
+    if 'male_bold' not in st.session_state:
+        st.session_state['male_bold'] = []
+
+    st.subheader('Step 1: Set Data Size')
+    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)
+        st.session_state['male_bold'] = sample(
+            [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.')
+
+    if st.session_state['female_bold'] and st.session_state['male_bold']:
+        st.subheader('Step 2: Generated 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']]
+
+            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)
+            st.session_state['male_continuations'] = [gen['generated_text'].replace(prompt, '') for gen, prompt in
+                                                      zip(male_generation, st.session_state['male_prompts'])]
+
+            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['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'])))
+
+    if st.session_state.get('male_continuations') and st.session_state.get('female_continuations'):
+        st.subheader('Step 3: Sample Generated Texts')
+        st.write('**Male Prompt:**', st.session_state['male_prompts'][0])
+        st.write('**Male Continuation:**', st.session_state['male_continuations'][0])
+        st.write('**Female Prompt:**', st.session_state['female_prompts'][0])
+        st.write('**Female Continuation:**', st.session_state['female_continuations'][0])
+
+        if st.button('Evaluate'):
+            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)