Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+import base64
+from io import BytesIO
+import pandas as pd 
+import numpy as np
+import random as rd
+import math
+
+from diffusers import StableDiffusionPipeline
+from transformers import CLIPProcessor, CLIPModel, Pix2StructProcessor, Pix2StructForConditionalGeneration, ViltProcessor, ViltForQuestionAnswering, BlipProcessor, BlipForQuestionAnswering, AutoProcessor, AutoModelForCausalLM
+import openai
+
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+
+vilt_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+vilt_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+
+
+import ds_manager as ds_mgr
+
+MISSING_C = None
+C1_B64s = []
+C2_B64s = []
+C1_PILs = []
+C2_PILs = []
+
+def updateErrorMsg(isError, text):
+    return gr.Markdown.update(visible=isError, value=text)
+
+def moveStep1():
+    variants = ["primary","secondary","secondary"]
+    #inter = [True, False, False]
+    tabs = [True, False, False]
+
+    return (gr.update(variant=variants[0]),
+            gr.update(variant=variants[1]),
+            gr.update(variant=variants[2]),
+            gr.update(visible=tabs[0]),
+            gr.update(visible=tabs[1]),
+            gr.update(visible=tabs[2]))
+
+# Interaction with top tabs
+def moveStep1_clear():
+    variants = ["primary","secondary","secondary"]
+    #inter = [True, False, False]
+    tabs = [True, False, False]
+
+    return (gr.update(variant=variants[0]),
+            gr.update(variant=variants[1]),
+            gr.update(variant=variants[2]),
+            gr.update(visible=tabs[0]),
+            gr.update(visible=tabs[1]),
+            gr.update(visible=tabs[2]),
+            gr.Textbox.update(value=""),
+            gr.Textbox.update(value=""),
+            gr.Textbox.update(value=""),
+            gr.Textbox.update(value=""))
+
+def moveStep2():
+    variants = ["secondary","primary","secondary"]
+    #inter = [True, True, False]
+    tabs = [False, True, False]
+
+    return (gr.update(variant=variants[0]),
+            gr.update(variant=variants[1]),
+            gr.update(variant=variants[2]),
+            gr.update(visible=tabs[0]),
+            gr.update(visible=tabs[1]),
+            gr.update(visible=tabs[2]))
+
+def moveStep3():
+    variants = ["secondary","secondary","primary"]
+    #inter = [True, True, False]
+    tabs = [False, False, True]
+
+    return (gr.update(variant=variants[0]),
+            gr.update(variant=variants[1]),
+            gr.update(variant=variants[2]),
+            gr.update(visible=tabs[0]),
+            gr.update(visible=tabs[1]),
+            gr.update(visible=tabs[2]))
+
+def decode_b64(b64s):
+    decoded = []
+    for b64 in b64s:
+        decoded.append(Image.open(BytesIO(base64.b64decode(b64))))
+    return decoded
+
+def generate(prompt, openai_key):
+    prompt = prompt.lower().strip()
+    _, retrieved, _ = ds_mgr.getSavedSentences(prompt)
+    print(f"retrieved: {retrieved}")
+    if len(retrieved.index) > 0:
+        update_value = decode_b64(list(retrieved['b64']))
+        print(f"update_value: {update_value}")
+        return update_value, list(retrieved['b64'])
+    openai.api_key = openai_key
+    response = openai.Image.create(
+        prompt=prompt,
+        n=4,
+        size="256x256",
+        response_format='b64_json'
+    )
+    image_b64s = []
+    save_b64s = []
+    for image in response['data']:
+        image_b64s.append(image['b64_json'])
+        save_b64s.append([prompt, image['b64_json']])
+    save_df = pd.DataFrame(save_b64s, columns=["prompt", "b64"])
+    print(f"save_df: {save_b64s}")
+    # save (save_df)
+    ds_mgr.saveSentences(save_df)
+    images = decode_b64(image_b64s)
+    # images = pipe(prompt, height=256, width=256, num_images_per_prompt=2).images
+    #print(images)
+    # return (
+    #     gr.update(value=images)
+    # )
+    return images, image_b64s
+
+
+def clip(imgs1, imgs2, g1, g2):
+    """
+    imgs1: list of PIL Images 
+    imgs1: list of PIL Images 
+    g1: list of str (test-concepts 1)
+    g2: list of str (test-concepts 2)
+
+    returns avg_probs_imgs1, avg_probs_imgs2 - dicts for imgs1, imgs2 
+    ({img index: {'g1': probability, 'g2': probability}})
+    """
+    # One call of CLIP processor + model - may need to batch later
+    
+    inputs = clip_processor(text = g1 + g2, images = imgs1 + imgs2, 
+                            return_tensors="pt", padding=True) 
+    outputs = clip_model(**inputs)
+    
+    logits_imgs1 = outputs.logits_per_image[:len(imgs1)]
+    logits_imgs2 = outputs.logits_per_image[len(imgs1):]
+    probs_imgs1 = torch.softmax(logits_imgs1, dim=1)
+    probs_imgs2 = torch.softmax(logits_imgs2, dim=1)
+
+    avg_probs_imgs1 = {}
+    avg_probs_imgs2 = {}
+
+    # Calculate the probabilities of prompts in g1 and g2 for each image in imgs1
+    for idx, img_probs in enumerate(probs_imgs1):
+        prob_g1 = img_probs[:len(g1)].sum().item()
+        prob_g2 = img_probs[len(g1):].sum().item()
+        avg_probs_imgs1[idx] = {'g1': prob_g1, 'g2': prob_g2}
+
+    # Calculate the probabilities of prompts in g1 and g2 for each image in imgs2
+    for idx, img_probs in enumerate(probs_imgs2):
+        prob_g1 = img_probs[:len(g1)].sum().item()
+        prob_g2 = img_probs[len(g1):].sum().item()
+        avg_probs_imgs2[idx] = {'g1': prob_g1, 'g2': prob_g2}
+
+    print(f"avg_probs_imgs1:\n{avg_probs_imgs1}")
+    print(f"avg_probs_imgs2:\n{avg_probs_imgs2}")
+    # Can do an average probability over all images - need to decide how we are using this 
+    return avg_probs_imgs1, avg_probs_imgs2
+
+def vilt_test(imgs1, imgs2, g1, g2, model, processor):
+
+    avg_probs_imgs1 = {}
+    avg_probs_imgs2 = {}
+    
+    for i, img in enumerate(imgs1):
+        g1c = rd.choice(g1)
+        g2c = rd.choice(g2)
+        encoding = processor(img, f'Is the image of a {g1c}?', return_tensors="pt")
+        outputs = model(**encoding)
+        logits = outputs.logits
+        idx = logits.argmax(-1).item()
+        ans = model.config.id2label[idx]
+        print("Predicted answer:", model.config.id2label[idx])
+
+        logitsList = torch.softmax(logits, dim=1).flatten().tolist()
+        m = max(logitsList)
+        s = -math.inf 
+        for logit in logitsList:
+            if s <= logit < m:
+                s = logit
+        t = sum(logitsList)
+        pm, ps = m/t, s/t
+        
+        if 'yes' in ans:
+            avg_probs_imgs1[i] = {'g1': pm, 'g2': ps}
+        else:
+            avg_probs_imgs1[i] = {'g1': ps, 'g2': pm}
+
+    for i, img in enumerate(imgs2):
+        g2c = rd.choice(g2)
+        g1c = rd.choice(g1)
+        encoding = processor(img, f'Is the image of a {g2c}?', return_tensors="pt")
+        outputs = model(**encoding)
+        logits = outputs.logits
+        idx = logits.argmax(-1).item()
+        ans = model.config.id2label[idx]
+        print("Predicted answer:", model.config.id2label[idx])
+
+        logitsList = torch.softmax(logits, dim=1).flatten().tolist()
+        m = max(logitsList)
+        s = -math.inf 
+        for logit in logitsList:
+            if s <= logit < m:
+                s = logit
+        t = sum(logitsList)
+        pm, ps = m/t, s/t
+        
+        if 'yes' in ans:
+            avg_probs_imgs2[i] = {'g1': ps, 'g2': pm}
+        else:
+            avg_probs_imgs2[i] = {'g1': pm, 'g2': ps}
+
+        
+    print(f"avg_probs_imgs1:\n{avg_probs_imgs1}")
+    print(f"avg_probs_imgs2:\n{avg_probs_imgs2}")
+    return avg_probs_imgs1, avg_probs_imgs2
+
+
+def bloombergViz(att, numblocks, score, concept_images, concept_b64s, onRight=False):
+
+    leftColor = "#065b41" #"#555"
+    rightColor = "#35d4ac" #"#999"
+    # if flip:
+    #     leftColor = "#35d4ac" #"#999"
+    #     rightColor = "#065b41" #"#555"
+    
+    spanClass = "tooltiptext_left"
+    if onRight:
+        spanClass = "tooltiptext_right"
+
+    # g1p is indices of score where g1 >= g2
+    # g2p is indices of score where g2 < g1
+    g1p = []
+    g2p = []
+    print(f"score: {score}")
+    for i in score:
+        if score[i]['g1'] >= score[i]['g2']:
+            g1p.append(i)
+        else:
+            g2p.append(i)
+
+    res = ""
+
+    for i in g1p:
+        disp = concept_b64s[i]
+        res += f"<div style='height:20px;width:20px;background-color:{leftColor};display:inline-block;position:relative' id='filled'><span class='{spanClass}' style='color:#FFF'><center><img src='data:image/jpeg;base64,{disp}'></center><br>This image was identified as more likely to depict a group 1 term.</span></div> "
+    for i in g2p: 
+        disp = concept_b64s[i]
+        res += f"<div style='height:20px;width:20px;background-color:{rightColor};display:inline-block;position:relative' id='empty'><span class='{spanClass}' style='color:#FFF'><center><img src='data:image/jpeg;base64,{disp}'></center><br>This image was identified as more likely to depict a group 2 term.</span></div> "
+    return res
+
+def att_bloombergViz(att, numblocks, scores, concept_images, concept_b64s, onRight=False):
+    viz = bloombergViz(att, numblocks, scores, concept_images, concept_b64s, onRight)
+    attHTML = f"<div style='border-style:solid;border-color:#999;border-radius:12px'>{att}: %<br>{viz}</div><br>"
+    return attHTML
+
+def retrieveImgs(concept1, concept2, group1, group2, progress=gr.Progress()):
+    global MISSING_C, C1_B64s, C2_B64s, C1_PILs, C2_PILs
+    print(f"concept1: {concept1}. concept2: {concept2}. group1: {group1}. group2: {group2}")
+    print("RETRIEVE IMAGES CLICKED!")
+    G_MISSING_SPEC = []
+    variants = ["secondary","primary","secondary"]
+    inter = [True, True, False]
+    tabs = [True, False]
+    bias_gen_states = [True, False]
+    bias_gen_label = "Generate New Images"
+    bias_test_label = "Test Model for Social Bias"
+    num2gen_update = gr.update(visible=True) #update the number of new sentences to generate
+    prog_vis = [True]
+    err_update = updateErrorMsg(False, "") 
+    info_msg_update = gr.Markdown.update(visible=False, value="")
+    openai_gen_row_update = gr.Row.update(visible=True)
+    tested_model_dropdown_update = gr.Dropdown.update(visible=False)
+    tested_model_row_update = gr.Row.update(visible=False)
+
+    c1s = concept1.split(',')
+    c2s = concept2.split(',')
+    c1s = [c1.strip() for c1 in c1s]
+    c2s = [c2.strip() for c2 in c2s]
+    C1_PILs = []
+    C2_PILs = []
+    C1_B64s = []
+    C2_B64s = []
+    
+    if not c1s or not c2s:
+        print("No terms entered!")
+        err_update = updateErrorMsg(True, "Please enter terms!") 
+        variants = ["primary","secondary","secondary"]
+        inter = [True, False, False]
+        tabs = [True, False]
+        prog_vis = [False]
+    
+    else:
+        tabs = [False, True]
+        progress(0, desc="Fetching saved images...")
+        
+        for c1 in c1s:
+            _, retrieved, _ = ds_mgr.getSavedSentences(c1)
+            print(f"retrieved: {retrieved}")
+            if len(retrieved.index) > 0:
+                C1_B64s += list(retrieved['b64'])
+                C1_PILs += decode_b64(list(retrieved['b64']))
+                print(f"c1_retrieved: {C1_B64s}")
+        
+        for c2 in c2s:
+            _, retrieved, _ = ds_mgr.getSavedSentences(c2)
+            print(f"retrieved: {retrieved}")
+            if len(retrieved.index) > 0:
+                C2_B64s += list(retrieved['b64'])
+                C2_PILs += decode_b64(list(retrieved['b64']))
+                print(f"c2_retrieved: {C2_B64s}")
+    
+        if not C1_PILs or not C2_PILs:
+            err_update = updateErrorMsg(True, "No images were found for one or both concepts. Please enter OpenAI key and use Dall-E to generate new test images or change bias specification!") 
+            if not C1_PILs and not C2_PILs:
+                MISSING_C = 0
+            elif not C1_PILs:
+                MISSING_C = 1
+            elif not C2_PILs:
+                MISSING_C = 2
+        else:
+            print('there exist images for both!')
+            bias_gen_states = [False, True]
+            openai_gen_row_update = gr.Row.update(visible=False)
+            tested_model_dropdown_update = gr.Dropdown.update(visible=True)
+            tested_model_row_update = gr.Row.update(visible=True)
+        print(len(C1_PILs), len(C2_PILs), len(C1_B64s), len(C2_B64s))
+    print(f"Will these show up?: {concept1}, {concept2}, {group1}, {group2}")
+    print(f"C1_B64s, C1_PILs: {C1_B64s} || {C1_PILs}")
+    print(f"C2_B64s, C2_PILs: {C2_B64s} || {C2_PILs}")
+    return (
+        err_update, # error message
+        openai_gen_row_update, # OpenAI generation
+        num2gen_update, # Number of images to genrate 
+        tested_model_row_update, #Tested Model Row
+        tested_model_dropdown_update, # Tested Model Dropdown
+        info_msg_update, # sentences retrieved info update
+        gr.update(visible=prog_vis), # progress bar top
+        gr.update(variant=variants[0], interactive=inter[0]), # breadcrumb btn1
+        gr.update(variant=variants[1], interactive=inter[1]), # breadcrumb btn2
+        gr.update(variant=variants[2], interactive=inter[2]), # breadcrumb btn3
+        gr.update(visible=tabs[0]), # tab 1
+        gr.update(visible=tabs[1]), # tab 2
+        gr.Accordion.update(visible=bias_gen_states[1], label=f"Test images ({len(C1_PILs) + len(C2_PILs)})"), # accordion
+        gr.update(visible=True), # Row images
+        gr.update(value=C1_PILs+C2_PILs), #test images
+        gr.Button.update(visible=bias_gen_states[0], value=bias_gen_label), # gen btn
+        gr.Button.update(visible=bias_gen_states[1], value=bias_test_label), # bias test btn
+        gr.update(value=concept1), # concept1_fixed
+        gr.update(value=concept2), # concept2_fixed
+        gr.update(value=group1), # group1_fixed
+        gr.update(value=group2)  # group2_fixed
+        )
+            
+
+def generateImgs(concept1, concept2, openai_key, num_imgs2gen, progress=gr.Progress()):
+    global MISSING_C, C1_B64s, C2_B64s, C1_PILs, C2_PILs
+    err_update = updateErrorMsg(False, "")
+    bias_test_label = "Test Model Using Imbalanced Images"
+    
+    if MISSING_C == 0:
+        bias_gen_states = [True, False]
+        online_gen_visible = True
+        test_model_visible = False
+    elif MISSING_C == 1 or MISSING_C == 2:
+        bias_gen_states = [True, True]
+        online_gen_visible = True
+        test_model_visible = True
+    info_msg_update = gr.Markdown.update(visible=False, value="")
+
+    c1s = concept1.split(',')
+    c2s = concept2.split(',')
+    C1_PILs = []
+    C2_PILs = []
+    if not c1s or not c2s:
+        print("No terms entered!")
+        err_update = updateErrorMsg(True, "Please enter terms!") 
+        variants = ["primary","secondary","secondary"]
+        inter = [True, False, False]
+        tabs = [True, False]
+        prog_vis = [False]
+    else:
+        if len(openai_key) == 0:
+            print("Empty OpenAI key!!!")
+            err_update = updateErrorMsg(True, "Please enter an OpenAI key!") 
+        elif len(openai_key) < 10:
+            print("Wrong length OpenAI key!!!")
+            err_update = updateErrorMsg(True, "Please enter a correct OpenAI key!") 
+        else:
+            progress(0, desc="Dall-E generation...")
+            C1_PILs = []
+            C1_B64s = []
+            for c1 in c1s:
+                prompt = c1
+                PILs, c1_b64s = generate(prompt, openai_key)
+                C1_PILs += PILs
+                C1_B64s += c1_b64s
+            C2_PILs = []
+            C2_B64s = []
+            for c2 in c2s:
+                prompt = c2
+                PILs, c2_b64s = generate(prompt, openai_key)
+                C2_PILs += PILs
+                C2_B64s += c2_b64s
+            bias_gen_states = [False, True]
+            online_gen_visible = False
+            test_model_visible = True
+        bias_test_label = "Test Model for Social Bias"
+            
+    return (err_update, # err message if any
+        info_msg_update, # infor message about the number of imgs and coverage
+        gr.Row.update(visible=online_gen_visible),    # online gen row
+        gr.Row.update(visible=test_model_visible), # tested model row 
+        gr.Dropdown.update(visible=test_model_visible), # tested model selection dropdown
+        gr.Accordion.update(visible=test_model_visible, label=f"Test images ({len(C1_PILs)+len(C2_PILs)})"), # accordion
+        gr.update(visible=True), # Row images
+        gr.update(value=C1_PILs+C2_PILs), # test images
+        gr.update(visible=bias_gen_states[0]), # gen btn
+        gr.update(visible=bias_gen_states[1], value=bias_test_label)  # bias btn
+        )
+        
+
+def startBiasTest(test_imgs, concept1, concept2, group1, group2, model_name, progress=gr.Progress()):
+    global C1_B64s, C2_B64s, C1_PILs, C2_PILs
+    variants = ["secondary","secondary","primary"]
+    inter = [True, True, True]
+    tabs = [False, False, True]
+    err_update = updateErrorMsg(False, "") 
+
+    if len(test_imgs) == 0:
+      err_update = updateErrorMsg(True, "There are no images! (How'd you get here?)") 
+    
+    progress(0, desc="Starting social bias testing...")
+    g1 = group1.split(', ')
+    g2 = group2.split(', ')
+    avg_probs_imgs1, avg_probs_imgs2 = None, None
+    if model_name.lower() == 'clip':
+        avg_probs_imgs1, avg_probs_imgs2 = clip(C1_PILs, C2_PILs, g1, g2)
+    elif 'vilt' in model_name.lower():
+        avg_probs_imgs1, avg_probs_imgs2 = vilt_test(C1_PILs, C2_PILs, g1, g2, vilt_model, vilt_processor)
+    else:
+        print("that's not right")
+
+    c1_html = att_bloombergViz(concept1, len(avg_probs_imgs1), avg_probs_imgs1, C1_PILs, C1_B64s, False)
+    c2_html = att_bloombergViz(concept2, len(avg_probs_imgs2), avg_probs_imgs2, C2_PILs, C2_B64s, True)
+
+    model_bias_dict_n = 0.0
+    for key in avg_probs_imgs1:
+        model_bias_dict_n += avg_probs_imgs1[key]['g1']
+    for key in avg_probs_imgs2:
+        model_bias_dict_n += avg_probs_imgs2[key]['g2']
+    model_bias_dict_d = len(avg_probs_imgs1) + len(avg_probs_imgs2)
+    model_bias_dict = {f'bias score for {model_name} on {len(C1_PILs) + len(C2_PILs)} images': round(model_bias_dict_n/model_bias_dict_d, 2)}
+
+    group_labels_html_update = gr.HTML.update(
+        value=f"<div style='height:20px;width:20px;background-color:#065b41;display:inline-block;vertical-align:top'></div><div style='display:inline-block;vertical-align:top'> &nbsp; Image more likely classified as a Group 1 ({group1}) term </div>&nbsp;&nbsp;<div style='height:20px;width:20px;background-color:#35d4ac;display:inline-block;vertical-align:top'></div><div style='display:inline-block;vertical-align:top'> &nbsp; Image more likely classified as a Group 2 ({group2}) term </div>") 
+    
+    return (err_update, # error message
+            gr.Markdown.update(visible=True), # bar progress
+            gr.Button.update(variant=variants[0], interactive=inter[0]), # top breadcrumb button 1
+            gr.Button.update(variant=variants[1], interactive=inter[1]), # top breadcrumb button 2
+            gr.Button.update(variant=variants[2], interactive=inter[2]), # top breadcrumb button 3
+            gr.update(visible=tabs[0]), # content tab/column 1
+            gr.update(visible=tabs[1]), # content tab/column 2
+            gr.update(visible=tabs[2]), # content tab/column 3
+            model_bias_dict, # per model bias score 
+            gr.update(value=c1_html), # c1 bloomberg viz
+            gr.update(value=c2_html), # c2 bloomberg viz
+            gr.update(value=concept1), # c1_fixed
+            gr.update(value=concept2), # c2_fixed
+            gr.update(value=group1), # g1_fixed
+            gr.update(value=group2),  # g2_fixed
+            group_labels_html_update# group_labels_html 
+            )
+
+theme = gr.themes.Soft().set(
+    button_small_radius='*radius_xxs',
+    background_fill_primary='*neutral_50',
+    border_color_primary='*primary_50'
+)
+
+soft = gr.themes.Soft(
+    primary_hue="slate",
+    spacing_size="sm",
+    radius_size="md"
+).set(
+    # body_background_fill="white",
+    button_primary_background_fill='*primary_400'
+)
+css_adds = "#group_row {background: white; border-color: white;} \
+               #attribute_row {background: white; border-color: white;} \
+               #tested_model_row {background: white; border-color: white;} \
+               #button_row {background: white; border-color: white} \
+               #examples_elem .label {display: none}\
+               #con1_words {border-color: #E5E7EB;} \
+               #con2_words {border-color: #E5E7EB;} \
+               #grp1_words {border-color: #E5E7EB;} \
+               #grp2_words {border-color: #E5E7EB;} \
+               #con1_words_fixed {border-color: #E5E7EB;} \
+               #con2_words_fixed {border-color: #E5E7EB;} \
+               #grp1_words_fixed {border-color: #E5E7EB;} \
+               #grp2_words_fixed {border-color: #E5E7EB;} \
+               #con1_words_fixed input {box-shadow:None; border-width:0} \
+               #con1_words_fixed .scroll-hide {box-shadow:None; border-width:0} \
+               #con2_words_fixed input {box-shadow:None; border-width:0} \
+               #con2_words_fixed .scroll-hide {box-shadow:None; border-width:0} \
+               #grp1_words_fixed input {box-shadow:None; border-width:0} \
+               #grp1_words_fixed .scroll-hide {box-shadow:None; border-width:0} \
+               #grp2_words_fixed input {box-shadow:None; border-width:0} \
+               #grp2_words_fixed .scroll-hide {box-shadow:None; border-width:0} \
+               #tested_model_drop {border-color: #E5E7EB;} \
+               #gen_model_check {border-color: white;} \
+               #gen_model_check .wrap {border-color: white;} \
+               #gen_model_check .form {border-color: white;} \
+               #open_ai_key_box {border-color: #E5E7EB;} \
+               #gen_col {border-color: white;} \
+               #gen_col .form {border-color: white;} \
+               #res_label {background-color: #F8FAFC;} \
+               #per_attrib_label_elem {background-color: #F8FAFC;} \
+               #accordion {border-color: #E5E7EB} \
+               #err_msg_elem p {color: #FF0000; cursor: pointer} \
+               #res_label .bar {background-color: #35d4ac; } \
+               #bloomberg_legend {background: white; border-color: white} \
+               #bloomberg_att1 {background: white; border-color: white} \
+               #bloomberg_att2 {background: white; border-color: white} \
+               .tooltiptext_left {visibility: hidden;max-width:50ch;min-width:25ch;top: 100%;left: 0%;background-color: #222;text-align: center;border-radius: 6px;padding: 5px 0;position: absolute;z-index: 1;} \
+               .tooltiptext_right {visibility: hidden;max-width:50ch;min-width:25ch;top: 100%;right: 0%;background-color: #222;text-align: center;border-radius: 6px;padding: 5px 0;position: absolute;z-index: 1;} \
+               #filled:hover .tooltiptext_left {visibility: visible;} \
+               #empty:hover .tooltiptext_left {visibility: visible;} \
+               #filled:hover .tooltiptext_right {visibility: visible;} \
+               #empty:hover .tooltiptext_right {visibility: visible;}"
+
+
+with gr.Blocks(theme=soft, title="Social Bias Testing in Image-To-Text Models",
+               css=css_adds) as iface:
+    with gr.Row():
+        s1_btn = gr.Button(value="Step 1: Bias Specification", variant="primary", visible=True, interactive=True, size='sm')#.style(size='sm')
+        s2_btn = gr.Button(value="Step 2: Test Images", variant="secondary", visible=True, interactive=False, size='sm')#.style(size='sm')
+        s3_btn = gr.Button(value="Step 3: Bias Testing", variant="secondary", visible=True, interactive=False, size='sm')#.style(size='sm')
+    err_message = gr.Markdown("", visible=False, elem_id="err_msg_elem")
+    bar_progress = gr.Markdown("     ")
+
+    # Page 1
+    with gr.Column(visible=True) as tab1:
+        with gr.Column():
+            gr.Markdown("#### Enter concepts to generate") # #group_row
+            with gr.Row(elem_id ="generation_row"):
+                concept1 = gr.Textbox(label="Image Generation Concept 1", max_lines=1, elem_id="con1_words", elem_classes="input_words", placeholder="ceo, executive")
+                concept2 = gr.Textbox(label="Image Generation Concept 2", max_lines=1, elem_id="con2_words", elem_classes="input_words", placeholder="nurse, janitor")
+            gr.Markdown("#### Enter concepts to test") # #attribute_row
+            with gr.Row(elem_id="group_row"):
+                group1 = gr.Textbox(label="Text Caption Concept 1", max_lines=1, elem_id="grp1_words", elem_classes="input_words", placeholder="brother, father")
+                group2 = gr.Textbox(label="Text Caption Concept 2", max_lines=1, elem_id="grp2_words", elem_classes="input_words", placeholder="sister, mother")
+            with gr.Row():
+                gr.Markdown("    ")
+                get_sent_btn = gr.Button(value="Get Images", variant="primary", visible=True)
+                gr.Markdown("    ")
+
+    # Page 2
+    with gr.Column(visible=False) as tab2:
+        info_imgs_found = gr.Markdown(value="", visible=False) # info_sentences_found
+        
+        gr.Markdown("### Tested Social Bias Specification", visible=True)
+        with gr.Row():
+            concept1_fixed = gr.Textbox(label="Image Generation Concept 1", max_lines=1, elem_id="con1_words_fixed", elem_classes="input_words", interactive=False, visible=True) # group1_words_fixed
+            concept2_fixed = gr.Textbox(label='Image Generation Concept 2', max_lines=1, elem_id="con2_words_fixed", elem_classes="input_words", interactive=False, visible=True) # group2_fixed
+        with gr.Row():
+            group1_fixed = gr.Textbox(label='Text Caption Concept 1', max_lines=1, elem_id="grp1_words_fixed", elem_classes="input_words", interactive=False, visible=True) # att1_words_fixed
+            group2_fixed = gr.Textbox(label='Text Caption Concept 2', max_lines=1, elem_id="grp2_words_fixed", elem_classes="input_words", interactive=False, visible=True) # att2_fixed
+
+        with gr.Row():
+            with gr.Column():
+                with gr.Row(visible=False) as online_gen_row:
+                    with gr.Column():
+                        gen_title = gr.Markdown("### Generate Additional Images", visible=True)
+
+                        # OpenAI Key for generator
+                        openai_key = gr.Textbox(lines=1, label="OpenAI API Key", value=None,
+                                                placeholder="starts with sk-", 
+                                info="Please provide the key for an Open AI account to generate new test images",
+                                visible=True,
+                                interactive=True,
+                                elem_id="open_ai_key_box")
+                        num_imgs2gen = gr.Slider(2, 20, value=2, step=1, 
+                                                interactive=True,
+                                                visible=True,
+                                                container=True)
+                    
+                with gr.Row(visible=False) as tested_model_row:
+                    with gr.Column():
+                        gen_title = gr.Markdown("### Select Tested Model", visible=True)
+
+                        tested_model_name = gr.Dropdown(["CLIP", "ViLT"], value="CLIP", 
+                            multiselect=None,
+                            interactive=True, 
+                            label="Tested model", 
+                            elem_id="tested_model_drop",
+                            visible=True
+                        )
+            
+        with gr.Row():
+            gr.Markdown("    ")
+            gen_btn = gr.Button(value="Generate New Images", variant="primary", visible=True)
+            bias_btn = gr.Button(value="Test Model for Social Bias", variant="primary", visible=False)
+            gr.Markdown("    ")
+        
+        with gr.Row(visible=False) as row_imgs: # row_sentences
+            with gr.Accordion(label="Test Images", open=False, visible=False) as acc_test_imgs: # acc_test_sentences
+                test_imgs = gr.Gallery(show_label=False) # test_sentences, output
+            
+    # Page 3
+    with gr.Column(visible=False) as tab3:
+        gr.Markdown("### Tested Social Bias Specification", visible=True)
+        with gr.Row():
+            concept1_fixed2 = gr.Textbox(label="Image Generation Concept 1", max_lines=1, elem_id="con1_words_fixed", elem_classes="input_words", interactive=False) # group1_words_fixed
+            concept2_fixed2 = gr.Textbox(label='Image Generation Concept 2', max_lines=1, elem_id="con2_words_fixed", elem_classes="input_words", interactive=False) # group2_fixed
+        with gr.Row():
+            group1_fixed2 = gr.Textbox(label='Text Caption Concept 1', max_lines=1, elem_id="grp1_words_fixed", elem_classes="input_words", interactive=False) # att1_words_fixed
+            group2_fixed2 = gr.Textbox(label='Text Caption Concept 2', max_lines=1, elem_id="grp2_words_fixed", elem_classes="input_words", interactive=False) # att2_fixed
+
+        with gr.Row():
+            with gr.Column(scale=2):
+                gr.Markdown("### Bias Test Results")
+        with gr.Row():
+            with gr.Column(scale=2):
+                lbl_model_bias = gr.Markdown("**Model Bias** - % stereotyped choices (↑ more bias)")
+                model_bias_label = gr.Label(num_top_classes=1, label="% stereotyped choices (↑ more bias)",
+                                            elem_id="res_label",
+                                            show_label=False)
+                                
+                with gr.Row():
+                    with gr.Column(variant="compact", elem_id="bloomberg_legend"): 
+                        group_labels_html = gr.HTML(value="<div style='height:20px;width:20px;background-color:#065b41;display:inline-block;vertical-align:top'></div><div style='display:inline-block;vertical-align:top'> &nbsp; Social group 1 more probable in the image </div>&nbsp;&nbsp;<div style='height:20px;width:20px;background-color:#35d4ac;display:inline-block;vertical-align:top'></div><div style='display:inline-block;vertical-align:top'> &nbsp; Social group 2 more probable in the image </div>") 
+            
+                with gr.Row():
+                    with gr.Column(variant="compact", elem_id="bloomberg_att1"): 
+                        gr.Markdown("#### Text Caption Concept Probability for Image Generation Concept 1")
+                        c1_results = gr.HTML()
+                    with gr.Column(variant="compact", elem_id="bloomberg_att2"):
+                        gr.Markdown("#### Text Caption Concept Probability for Image Generation Concept 2")
+                        c2_results = gr.HTML()
+            
+                gr.HTML(value="Visualization inspired by <a href='https://www.bloomberg.com/graphics/2023-generative-ai-bias/' target='_blank'>Bloomberg article on bias in text-to-image models</a>.")
+                save_msg = gr.HTML(value="<span style=\"color:black\">Bias test result saved! </span>", visible=False)
+                
+                
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    gr.Markdown("    ")
+                    with gr.Column():
+                        new_bias_button = gr.Button("Try New Bias Test", variant="primary")
+                    gr.Markdown("    ")
+    
+    # Get sentences
+    get_sent_btn.click(fn=retrieveImgs, #retrieveSentences
+                  inputs=[concept1, concept2, group1, group2], 
+                  outputs=[err_message, online_gen_row, num_imgs2gen, tested_model_row, tested_model_name, info_imgs_found, bar_progress, s1_btn, s2_btn, s3_btn, tab1, tab2, acc_test_imgs, row_imgs, test_imgs, gen_btn, bias_btn,
+                           concept1_fixed, concept2_fixed, group1_fixed, group2_fixed ]
+                      )
+
+    # request getting sentences
+    gen_btn.click(fn=generateImgs, #generateSentences  
+                  inputs=[concept1, concept2, openai_key, num_imgs2gen], 
+                  outputs=[err_message, info_imgs_found, online_gen_row, 
+                           tested_model_row, tested_model_name, acc_test_imgs, row_imgs, test_imgs, gen_btn, bias_btn ]
+                 )
+    
+    # Test bias
+    bias_btn.click(fn=startBiasTest,
+                   inputs=[test_imgs, concept1, concept2, group1, group2, tested_model_name],
+                   outputs=[err_message, bar_progress, s1_btn, s2_btn, s3_btn, tab1, tab2, tab3, model_bias_label, 
+                            c1_results, c2_results, concept1_fixed2, concept2_fixed2, group1_fixed2, group2_fixed2, 
+                            group_labels_html]
+                   )
+    
+    # top breadcrumbs
+    s1_btn.click(fn=moveStep1,
+                 inputs=[],
+                 outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3])
+    
+    # top breadcrumbs
+    s2_btn.click(fn=moveStep2,
+                 inputs=[],
+                 outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3])
+    
+    # top breadcrumbs
+    s3_btn.click(fn=moveStep3,
+                 inputs=[],
+                 outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3])
+    
+    new_bias_button.click(fn=moveStep1_clear,
+                          inputs=[],
+                          outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3, concept1, concept2, group1, group2])
+
+iface.queue(concurrency_count=2).launch()