update

pages/1_Demo_1.py

@@ -7,9 +7,10 @@ from utils.model import gpt2
 import os
 
 # Set up the Streamlit interface
+st.set_page_config(page_title="Gender Bias Analysis", page_icon="🔍", layout="wide")
 st.title('Gender Bias Analysis in Text Generation')
 
-
+# Password protection function
 def check_password():
     def password_entered():
         if password_input == os.getenv('PASSWORD'):
@@ -17,99 +18,106 @@ def check_password():
         else:
             st.error("Incorrect Password, please try again.")
 
-    password_input = st.text_input("Enter Password:", type="password")
-    submit_button = st.button("Submit", on_click=password_entered)
+    if 'password_correct' not in st.session_state:
+        st.session_state['password_correct'] = False
 
-    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['password_correct']:
+        password_input = st.text_input("Enter Password:", type="password")
+        st.button("Submit", on_click=password_entered)
 
+# Data loading function
+def load_data():
+    if 'bold' not in st.session_state:
+        st.session_state['bold'] = load_dataset("AlexaAI/bold", split="train")
 
+# Sampling function
+def sample_data(data_size):
+    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)
+
+# Text generation function
+def 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)
+    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'])]
+
+    progress_bar.progress(100)
+    st.write('Text generation completed.')
+
+# Display data samples function
+def display_samples():
+    st.write("### Male Data Samples")
+    samples_df = pd.DataFrame({
+        'Male Prompt': st.session_state['male_prompts'],
+        'Male Continuation': st.session_state['male_continuations'],
+    })
+    st.dataframe(samples_df)
+
+    st.write("### Female Data Samples")
+    samples_df = pd.DataFrame({
+        'Female Prompt': st.session_state['female_prompts'],
+        'Female Continuation': st.session_state['female_continuations']
+    })
+    st.dataframe(samples_df)
+
+# Evaluate regard function
+def evaluate_regard():
+    regard = Regard("compare")
+    st.write('Computing regard results to compare male and female continuations...')
+
+    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)
+
+# Main app logic
 if not st.session_state.get('password_correct', False):
     check_password()
 else:
     st.sidebar.success("Password Verified. Proceed with the demo.")
-
-    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'] = []
+    load_data()
 
     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'])
+                          value=st.session_state.get('data_size', 10))
     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)
-
+        sample_data(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']))
+        display_samples()
 
-    if st.session_state['female_bold'] and st.session_state['male_bold']:
+    if st.session_state.get('female_bold') and st.session_state.get('male_bold'):
         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)
-            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'])]
-
-            progress_bar.progress(100)
-            st.write('Text generation completed.')
+            generate_text()
 
     if st.session_state.get('male_continuations') and st.session_state.get('female_continuations'):
-        st.subheader('Step 3: Sample Generated Texts')
-
-        st.write("Male Data Samples:")
-        samples_df = pd.DataFrame({
-            'Male Prompt': st.session_state['male_prompts'],
-            'Male Continuation': st.session_state['male_continuations'],
-        })
-        st.write(samples_df)
-
-        st.write("Female Data Samples:")
-        samples_df = pd.DataFrame({
-            'Female Prompt': st.session_state['female_prompts'],
-            'Female Continuation': st.session_state['female_continuations']
-        })
-        st.write(samples_df)
-
+        st.subheader('Step 3: Evaluate')
+        display_samples()
         if st.button('Evaluate'):
-            st.subheader('Step 4: Regard Results')
-            regard = Regard("compare")
-            st.write('Computing regard results to compare male and female continuations...')
-
-            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)
+            evaluate_regard()