diff --git a/__pycache__/live_preview_helpers.cpython-310.pyc b/__pycache__/live_preview_helpers.cpython-310.pyc
index 422dde1553a0650d56b8ef19f963713b8562d245..297303fbe50dc663031b9e597f37ac9d947766f1 100644
Binary files a/__pycache__/live_preview_helpers.cpython-310.pyc and b/__pycache__/live_preview_helpers.cpython-310.pyc differ
diff --git a/__pycache__/optim_utils.cpython-310.pyc b/__pycache__/optim_utils.cpython-310.pyc
index c101d5ead6cc1076a582b7061217fc6fb2a05c53..98049bf4f0530ff3e4d326734bcc1f45f671cf74 100644
Binary files a/__pycache__/optim_utils.cpython-310.pyc and b/__pycache__/optim_utils.cpython-310.pyc differ
diff --git a/__pycache__/utils.cpython-310.pyc b/__pycache__/utils.cpython-310.pyc
index 3ce89868a99fdf552a12fe7b347ab90f7027ce4b..0c74b8014decef246c54c355c0b85f7eea56afb5 100644
Binary files a/__pycache__/utils.cpython-310.pyc and b/__pycache__/utils.cpython-310.pyc differ
diff --git a/app.py b/app.py
index 7011ce81485d59cc7ff92deff6c3f9939fcfac70..f4a957fe656ce8626213dce5f85baf3ab364f907 100644
--- a/app.py
+++ b/app.py
@@ -7,13 +7,11 @@ import torch
 import re
 import open_clip
 from optim_utils import optimize_prompt
-from utils import clean_response_gpt, setup_model, init_gpt_api, call_gpt_api, get_refine_msg, clean_cache
+from utils import clean_response_gpt, setup_model, init_gpt_api, call_gpt_api, get_refine_msg, clean_cache, get_personalize_message
 from utils import SCENARIOS, PROMPTS, IMAGES, OPTIONS, T2I_MODELS, INSTRUCTION, IMAGE_OPTIONS
 import spaces #[uncomment to use ZeroGPU]
 import transformers
 import gspread
-import asyncio
-from datetime import datetime
 
 CLIP_MODEL = "ViT-H-14"
 PRETRAINED_CLIP = "laion2b_s32b_b79k"
@@ -33,7 +31,7 @@ llm_pipe = None
 torch.cuda.empty_cache()
 inverted_prompt = ""
 
-VERBAL_MSG = "Please verbally describe key differences found in the image pair."
+VERBAL_MSG = "Please verbally describe why you are satisfied or unsatisfied at the generated images."
 DEFAULT_SCENARIO = "Product advertisement"
 METHODS = ["Method 1", "Method 2"]
 MAX_ROUND = 5
@@ -78,37 +76,6 @@ def infer(
 
     return image
 
-async def infer_async(prompt):
-    return infer(prompt)
-# generate a batch of images in parallel
-async def generate_batch(prompts):
-    tasks = [infer_async(p) for p in prompts]
-    images = await asyncio.gather(*tasks)  # Run all in parallel
-    return images
-
-@spaces.GPU 
-def call_llm_refine_prompt(prompt, num_prompts=5, max_tokens=1000, temperature=0.7, top_p=0.9):
-    print(f"loading {default_llm_model}")
-    global llm_pipe
-    if not llm_pipe:
-        llm_pipe = transformers.pipeline("text-generation", model=default_llm_model, model_kwargs={"torch_dtype": torch_dtype}, device_map="auto")
-    
-    messages = get_refine_msg(prmpt, num_prompts)
-    terminators = [
-        llm_pipe.tokenizer.eos_token_id,
-        llm_pipe.tokenizer.convert_tokens_to_ids("<|eot_id|>")
-    ]
-    outputs = llm_pipe(
-        messages,
-        max_new_tokens=max_tokens,
-        eos_token_id=terminators,
-        do_sample=True,
-        temperature=temperature,
-        top_p=top_p,
-    )
-    prompt_list = clean_response_gpt(outputs[0]["generated_text"][-1]["content"])
-    return prompt_list
-
 def call_gpt_refine_prompt(prompt, num_prompts=5, max_tokens=1000, temperature=0.7, top_p=0.9):
     seed = random.randint(0, MAX_SEED)
     client = init_gpt_api()
@@ -117,12 +84,6 @@ def call_gpt_refine_prompt(prompt, num_prompts=5, max_tokens=1000, temperature=0
     prompt_list = clean_response_gpt(outputs)
     return prompt_list
 
-def refine_prompt(gallery_state, prompt):
-    modified_prompts = call_gpt_refine_prompt(prompt)
-    return modified_prompts
-    
-    # eval(prompt, inverted_prompt, gallery_state, clip_model, preprocess)
-
 @spaces.GPU(duration=100)
 def invert_prompt(prompt, images, prompt_len=15, iter=1000, lr=0.1, batch_size=2):
     text_params = {
@@ -142,25 +103,15 @@ def invert_prompt(prompt, images, prompt_len=15, iter=1000, lr=0.1, batch_size=2
     # eval(prompt, learned_prompt, optimized_images, clip_model, preprocess)
     # return learned_prompt
 
-
-def eval(prompt, optimized_prompt, optimized_images, clip_model, preprocess):
-    torch.cuda.empty_cache()
-    tokenizer = open_clip.get_tokenizer(CLIP_MODEL)
-    images = [preprocess(i).unsqueeze(0) for i in optimized_images]
-    images = torch.concatenate(images).to(device)
-    
-    with torch.no_grad():
-        image_feat = clip_model.encode_image(images)
-        text_feat = clip_model.encode_text(tokenizer([prompt]).to(device))
-        optimized_text_feat = clip_model.encode_text(tokenizer([optimized_prompt]).to(device))
-
-    image_feat /= image_feat.norm(dim=-1, keepdim=True)
-    text_feat /= text_feat.norm(dim=-1, keepdim=True)
-    optimized_text_feat /= optimized_text_feat.norm(dim=-1, keepdim=True)
-
-    similarity = text_feat.cpu().numpy() @ image_feat.cpu().numpy().T
-    similarity_optimized = optimized_text_feat.cpu().numpy() @ image_feat.cpu().numpy().T
-
+def personalize_prompt(prompt, history, feedback, like_image, dislike_image):
+    seed = random.randint(0, MAX_SEED)
+    client = init_gpt_api()
+    messages = get_personalize_message(prompt, history, feedback, like_image, dislike_image)
+    outputs = call_gpt_api(messages, client, "gpt-4o", seed, max_tokens=2000, temperature=0.7, top_p=0.9)
+    print(outputs)
+    # prompt_list = clean_response_gpt(outputs)
+    # print(prompt_list)
+    return outputs
 
 ########################################################################################################
 # Button-related functions
@@ -182,8 +133,43 @@ def switch_tab(active_tab):
     else:
         return gr.Tabs(selected="Task A")
 
+def check_satisfaction(sim_radio, active_tab):
+    global counter1, counter2, current_task1, current_task2
+    method = current_task1 if active_tab == "Task A" else current_task2
+    counter = counter1 if method == METHODS[0] else counter2
+
+    fully_satisfied_option = ["Satisfied", "Very Satisfied"]  # The value to trigger submit
+    enable_submit = sim_radio in fully_satisfied_option or counter >= MAX_ROUND
+   
+    return gr.update(interactive=enable_submit), gr.update(interactive=(not enable_submit)) 
+
+def check_participant(participant):
+    if participant == "":
+        display_error_message("Please fill your participant id!")
+        return False
+    return True
+
+def check_evaluation(sim_radio):
+    if not sim_radio :
+        display_error_message("❌ Please fill all evaluations before change image or submit.")
+        return False
+    
+    return True
+
+def select_image(like_radio, images_method):
+    if like_radio == IMAGE_OPTIONS[0]:
+        return images_method[0][0]
+    elif like_radio == IMAGE_OPTIONS[1]:
+        return images_method[1][0]
+    elif like_radio == IMAGE_OPTIONS[2]:
+        return images_method[2][0]
+    elif like_radio == IMAGE_OPTIONS[3]:
+        return images_method[3][0]
+    else:
+        return None
+
 def set_user(participant):
-    global responses_memory
+    global responses_memory, assigned_scenarios
     responses_memory[participant] = {METHODS[0]:{}, METHODS[1]:{}}
 
     id = re.findall(r'\d+', participant)
@@ -213,25 +199,12 @@ def display_scenario(participant, choice):
     
     res = { 
         scenario_content: SCENARIOS.get(choice, ""), 
-        prompt: PROMPTS.get(choice, ""),
-        prompt1: "", 
-        prompt2: "",
+        prompt1: gr.update(value=PROMPTS.get(choice, ""), interactive=False),
+        prompt2: gr.update(value=PROMPTS.get(choice, ""), interactive=False),
         images_method1: initial_images1, 
         images_method2: initial_images2,
-        gallery_state1: initial_images1, 
-        gallery_state2: initial_images2, 
-        sim_radio1: None, 
-        sim_radio2: None, 
-        dislike_radio1: None,
-        like_radio1: None,
-        dislike_radio2: None,
-        like_radio2: None,
-        like_image1: None,
-        dislike_image1: None,
-        like_image2: None,
-        dislike_image2: None,
-        response1: VERBAL_MSG, 
-        response2: VERBAL_MSG, 
+        history_images1: [],
+        history_images2: [],
         next_btn1: gr.update(interactive=False), 
         next_btn2: gr.update(interactive=False), 
         redesign_btn1: gr.update(interactive=True), 
@@ -241,64 +214,34 @@ def display_scenario(participant, choice):
     }
     return res
 
-def generate_image(participant, scenario, prompt, gallery_state, active_tab):
+def generate_image(participant, scenario, prompt, active_tab, like_image, dislike_image):
     if not check_participant(participant): return [], []
     global current_task1, current_task2
-    
     method = current_task1 if active_tab == "Task A" else current_task2
     
+    history_prompts = [v["prompt"] for v in responses_memory[participant][method].values()]
+    feedback = [v["sim_radio"] for v in responses_memory[participant][method].values()]
+    print(history_prompts, feedback)
+    personalized_prompt = personalize_prompt(prompt, history_prompts, feedback, like_image, dislike_image)
+    
+    gallery_images = []
     if method == METHODS[0]:
         for i in range(NUM_IMAGES): 
-            img = infer(prompt)
-            gallery_state.append(img)
-            yield gallery_state
+            img = infer(personalized_prompt)
+            gallery_images.append(img)
+            yield gallery_images
     else:
-        refined_prompts = refine_prompt(gallery_state, prompt)
+        refined_prompts = call_gpt_refine_prompt(personalized_prompt)
         for i in range(NUM_IMAGES): 
             img = infer(refined_prompts[i])
-            gallery_state.append(img)
-            yield gallery_state
-
-def check_satisfaction(sim_radio, active_tab):
-    global counter1, counter2, current_task1, current_task2
-    method = current_task1 if active_tab == "Task A" else current_task2
-    counter = counter1 if method == METHODS[0] else counter2
-
-    fully_satisfied_option = ["Satisfied", "Very Satisfied"]  # The value to trigger submit
-    enable_submit = sim_radio in fully_satisfied_option or counter >= MAX_ROUND
-   
-    return gr.update(interactive=enable_submit), gr.update(interactive=(not enable_submit)) 
-
-def check_participant(participant):
-    if participant == "":
-        display_error_message("Please fill your participant id!")
-        return False
-    return True
-
-def check_evaluation(sim_radio, response):
-    if not sim_radio :
-        display_error_message("❌ Please fill all evaluations before change image or submit.")
-        return False
-    
-    return True
-
-def select_dislike(like_radio, images_method):
-    if like_radio == IMAGE_OPTIONS[0]:
-        return images_method[0]
-    elif like_radio == IMAGE_OPTIONS[1]:
-        return images_method[1]
-    elif like_radio == IMAGE_OPTIONS[2]:
-        return images_method[2]
-    elif like_radio == IMAGE_OPTIONS[3]:
-        return images_method[3]
-    else:
-        return None
+            gallery_images.append(img)
+            yield gallery_images
     
-def redesign(participant, scenario, prompt, sim_radio, response, images_method, active_tab):
+def redesign(participant, scenario, prompt, sim_radio, current_images, history_images, active_tab):
     global counter1, counter2, responses_memory, current_task1, current_task2
     method = current_task1 if active_tab == "Task A" else current_task2
 
-    if check_evaluation(sim_radio, response) and check_participant(participant):
+    if check_evaluation(sim_radio) and check_participant(participant):
         if method == METHODS[0]:
             counter1 += 1
             counter = counter1
@@ -309,62 +252,68 @@ def redesign(participant, scenario, prompt, sim_radio, response, images_method,
         responses_memory[participant][method][counter-1] = {}
         responses_memory[participant][method][counter-1]["prompt"] = prompt
         responses_memory[participant][method][counter-1]["sim_radio"] = sim_radio
-        responses_memory[participant][method][counter-1]["response"] = response
-
-        prompt_state = gr.update(visible=True)
+        # responses_memory[participant][method][counter-1]["response"] = response
+        
+        history_prompts = [[v["prompt"]] for v in responses_memory[participant][method].values()]
+        if not history_images: 
+            history_images = current_images
+        elif current_images:
+            history_images.extend(current_images)
+        current_images = []
+        examples_state = gr.update(samples=history_prompts, visible=True)
+        prompt_state = gr.update(interactive=True)
         next_state = gr.update(interactive=False) if counter >= MAX_ROUND else gr.update(visible=True, interactive=True)
         redesign_state = gr.update(interactive=False) if counter >= MAX_ROUND else gr.update(interactive=True)
         submit_state = gr.update(interactive=True) if counter >= MAX_ROUND else gr.update(interactive=False)
 
-        return [], None, VERBAL_MSG, prompt_state, next_state, redesign_state, submit_state
+        return None, None, None, current_images, history_images, examples_state, prompt_state, next_state, redesign_state, submit_state
     else:
         return {submit_btn1: gr.skip()} if active_tab == "Task A" else {submit_btn2: gr.skip()}
 
-def show_message(selected_option):
-    if selected_option:
-        return "Click \"Redesign\" and revise your prompt to create images that may more closely match your expectations."
-    return ""  
-  
-def save_response(participant, scenario, prompt, sim_radio, response, images_method, active_tab):
-    global current_task1, current_task2, counter1, counter2, responses_memory, task1_success, task2_success, assigned_scenarios
+
+def save_response(participant, scenario, prompt, sim_radio, active_tab):
+    global current_task1, current_task2 # not change
+    global task1_success, task2_success, counter1, counter2, responses_memory, assigned_scenarios # will change
+
     method = current_task1 if active_tab == "Task A" else current_task2
-    
-    if check_evaluation(sim_radio, response) and check_participant(participant):
+    if check_evaluation(sim_radio) and check_participant(participant):
         counter = counter1 if method == METHODS[0] else counter2
-        # image_paths = [save_image(img, "method", i) for i, img in enumerate(images_method)]
 
         responses_memory[participant][method][counter] = {}
         responses_memory[participant][method][counter]["prompt"] = prompt
         responses_memory[participant][method][counter]["sim_radio"] = sim_radio
-        responses_memory[participant][method][counter]["response"] = response
-        prompt_state = gr.update(visible=False)
-        next_state = gr.update(visible=False, interactive=False)
-        submit_state = gr.update(interactive=False) 
-        redesign_state = gr.update(interactive=False) 
-
+        # responses_memory[participant][method][counter]["response"] = response
+        
         try:
             gc = gspread.service_account(filename='credentials.json')
             sheet = gc.open("DiverseGen-phase3").sheet1 
 
             for i, entry in responses_memory[participant][method].items():
-                sheet.append_row([participant, scenario, method, i, entry["prompt"], entry["sim_radio"],  entry["response"]])
+                sheet.append_row([participant, scenario, method, i, entry["prompt"], entry["sim_radio"]])
 
             display_info_message("✅ Your answer is saved!")
 
-            # reset counter and update success indicator
+            # reset global variables
             if method == METHODS[0]:
                 counter1 = 1
             else:
                 counter2 = 1
-
             if active_tab == "Task A":
                 task1_success = True
             else:
                 task2_success = True
-
-            tabs = switch_tab(active_tab)
+            # decide if change scenario
             next_scenario = assigned_scenarios[1] if task1_success and task2_success else assigned_scenarios[0]
-            return [], [], None, None, None, None, None, VERBAL_MSG, prompt_state, next_state, redesign_state, submit_state, tabs, next_scenario
+            # update buttons
+            example_state = gr.update(samples=[], visible=False)
+            prompt_state = gr.update(interactive=False)
+            next_state = gr.update(visible=False, interactive=False)
+            submit_state = gr.update(interactive=False) 
+            redesign_state = gr.update(interactive=False) 
+            tabs = switch_tab(active_tab)
+
+            return None, None, None, None, None, [], [], example_state, prompt_state, next_state, redesign_state, submit_state, next_scenario, tabs
+        
         except Exception as e:
             display_error_message(f"❌ Error saving response: {str(e)}")
             return {submit_btn1: gr.skip()} if active_tab == "Task A" else {submit_btn2: gr.skip()}
@@ -388,7 +337,7 @@ css="""
 }
 
 #col-container3 {
-    margin: 0 auto;
+    margin: 0 0 auto auto;
     max-width: 300px;
 }
 
@@ -413,7 +362,6 @@ with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Inconsolata"), "
             )
             scenario = gr.Dropdown(
                 choices=list(SCENARIOS.keys()),
-                # value=DEFAULT_SCENARIO,
                 value=None,
                 label="📌 Scenario",
                 interactive=False,
@@ -421,13 +369,6 @@ with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Inconsolata"), "
         scenario_content = gr.Textbox(
             label="📖 Background", 
             interactive=False, 
-            # value=SCENARIOS[DEFAULT_SCENARIO]
-        )
-        prompt = gr.Textbox(
-                label="🎨 Prompt",
-                max_lines=1,
-                # value=PROMPTS[DEFAULT_SCENARIO],
-                interactive=False
         )
         active_tab = gr.State("Task A")
         instruction = gr.Markdown(INSTRUCTION)
@@ -435,26 +376,25 @@ with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Inconsolata"), "
     with gr.Tabs() as tabs:
         with gr.TabItem("Task A", id="Task A") as task1_tab:
             task1_tab.select(lambda: "Task A", outputs=[active_tab])
-            with gr.Column(elem_id="col-container"):        
-                # gr.Markdown("### Step 2: This is the prompt to generate images, you may modify the prompt after first round evaluation")
-                with gr.Row():
-                    prompt1 = gr.Textbox(
-                            label="🎨 Revise Prompt",
-                            max_lines=1,
-                            placeholder="Enter your prompt",
-                            # value=PROMPTS[DEFAULT_SCENARIO],
-                            scale=4, 
-                            visible=False
-                    )
-                    next_btn1 = gr.Button("Generate", variant="primary", scale=1, interactive=False, visible=False)
-
+            with gr.Row(elem_id="compact-row"):
+                prompt1 = gr.Textbox(
+                        label="🎨 Revise Prompt",
+                        max_lines=5,
+                        placeholder="Enter your prompt",
+                        scale=4, 
+                        visible=True,
+                )
+                next_btn1 = gr.Button("Generate", variant="primary", scale=1, interactive=False, visible=False)
+            with gr.Row(elem_id="compact-row"):
+                example1 = gr.Examples([['']], prompt1, label="Revised Prompt History", visible=False)
+        
             with gr.Row(elem_id="compact-row"):
                 with gr.Column(elem_id="col-container"):
-                    gallery_state1 = gr.State([])
-                    images_method1 = gr.Gallery(show_label=False, columns=[4], rows=[1], height=420, elem_id="gallery")
+                    images_method1 = gr.Gallery(label="Images", columns=[4], rows=[1], height=200, elem_id="gallery")
+                    history_images1 = gr.Gallery(label="History Images", columns=[4], rows=[1], elem_id="gallery")
                 with gr.Column(elem_id="col-container3"):
-                    like_image1 = gr.Image(label="Satisfied Image", width=200, height=200, sources='upload')
-                    dislike_image1 = gr.Image(label="Unsatisfied Image", width=200, height=200, sources='upload')
+                    like_image1 = gr.Image(label="Satisfied Image", width=200, height=200, sources='upload', type="pil")
+                    dislike_image1 = gr.Image(label="Unsatisfied Image", width=200, height=200, sources='upload', type="pil")
             with gr.Column(elem_id="col-container2"):
                 gr.Markdown("### 📝 Evaluation")               
                 sim_radio1 = gr.Radio(
@@ -465,13 +405,13 @@ with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Inconsolata"), "
                 )
                 like_radio1 = gr.Radio(
                     IMAGE_OPTIONS, 
-                    label="Select the image you are most satisfied.",
+                    label="Select the image that you find MOST satisfactory. You may leave this section blank if you prefer the previous images.",
                     type="value",
                     elem_classes=["gradio-radio"]
                 )
                 dislike_radio1 = gr.Radio(
                     IMAGE_OPTIONS, 
-                    label="Select the image you are most unsatisfied.",
+                    label="Please choose the image that you find LEAST satisfactory. You may leave this section blank if you are more dislike previous images.",
                     type="value",
                     elem_classes=["gradio-radio"]
                 )
@@ -491,26 +431,25 @@ with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Inconsolata"), "
 
         with gr.TabItem("Task B", id="Task B") as task2_tab:
             task2_tab.select(lambda: "Task B", outputs=[active_tab])
-            with gr.Column(elem_id="col-container"):        
-                # gr.Markdown("### Step 2: This is the prompt to generate images, you may modify the prompt after first round evaluation")
-                with gr.Row():
-                    prompt2 = gr.Textbox(
-                            label="🎨 Revise Prompt",
-                            max_lines=1,
-                            placeholder="Enter your prompt",
-                            # value=PROMPTS[DEFAULT_SCENARIO],
-                            scale=4,
-                            visible=False
-                    )
-                    next_btn2 = gr.Button("Generate", variant="primary", scale=1, interactive=False, visible=False)
+            with gr.Row(elem_id="compact-row"):
+                prompt2 = gr.Textbox(
+                        label="🎨 Revise Prompt",
+                        max_lines=5,
+                        placeholder="Enter your prompt",
+                        scale=4,
+                        visible=True,
+                )
+                next_btn2 = gr.Button("Generate", variant="primary", scale=1, interactive=False, visible=False)
+            with gr.Row(elem_id="compact-row"):
+                example2 = gr.Examples([['']], prompt2, label="Revised Prompt History", visible=False)
 
             with gr.Row(elem_id="compact-row"):
                 with gr.Column(elem_id="col-container"):
-                    gallery_state2 = gr.State(IMAGES[DEFAULT_SCENARIO]["ours"])
-                    images_method2 = gr.Gallery(height=420, show_label=False, columns=[4], rows=[1], elem_id="gallery")
+                    images_method2 = gr.Gallery(label="Images", columns=[4], rows=[1], height=200, elem_id="gallery")
+                    history_images2 = gr.Gallery(label="History Images", columns=[4], rows=[1], elem_id="gallery")
                 with gr.Column(elem_id="col-container3"):
-                    like_image2 = gr.Image(label="Satisfied Image", width=200, height=200, sources='upload')
-                    dislike_image2 = gr.Image(label="Unsatisfied Image", width=200, height=200, sources='upload')
+                    like_image2 = gr.Image(label="Satisfied Image", width=200, height=200, sources='upload', type="pil")
+                    dislike_image2 = gr.Image(label="Unsatisfied Image", width=200, height=200, sources='upload', type="pil")
 
             with gr.Column(elem_id="col-container2"):
                 gr.Markdown("### 📝 Evaluation")
@@ -522,13 +461,13 @@ with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Inconsolata"), "
                 )
                 like_radio2 = gr.Radio(
                     IMAGE_OPTIONS, 
-                    label="Select the image you are most satisfied.",
+                    label="Select the image that you find MOST satisfactory. You may leave this section blank if you prefer the previous images.",
                     type="value",
                     elem_classes=["gradio-radio"]
                 )
                 dislike_radio2 = gr.Radio(
                     IMAGE_OPTIONS, 
-                    label="Select the image you are most unsatisfied.",
+                    label="Please choose the image that you find LEAST satisfactory. You may leave this section blank if you are more dislike previous images.",
                     type="value",
                     elem_classes=["gradio-radio"]
                 )
@@ -550,35 +489,37 @@ with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Inconsolata"), "
 ########################################################################################################
 
     participant.change(fn=set_user, inputs=[participant], outputs=[scenario])
-    scenario.change(display_scenario, inputs=[participant, scenario], outputs=[scenario_content, prompt, prompt1, prompt2, images_method1, images_method2, gallery_state1, gallery_state2, sim_radio1, sim_radio2, dislike_radio1, like_radio1, dislike_radio2, like_radio2, like_image1, dislike_image1, like_image2, dislike_image2, response1, response2, next_btn1, next_btn2, redesign_btn1, redesign_btn2, submit_btn1, submit_btn2])
-    prompt1.change(fn=reset_gallery, inputs=[], outputs=[gallery_state1])
-    prompt2.change(fn=reset_gallery, inputs=[], outputs=[gallery_state2])
-    next_btn1.click(fn=generate_image, inputs=[participant, scenario, prompt1, gallery_state1, active_tab], outputs=[images_method1])
-    next_btn2.click(fn=generate_image, inputs=[participant, scenario, prompt2, gallery_state2, active_tab], outputs=[images_method2])
+    scenario.change(display_scenario, 
+        inputs=[participant, scenario], 
+        outputs=[scenario_content, prompt1, prompt2, images_method1, images_method2, history_images1, history_images2, next_btn1, next_btn2, redesign_btn1, redesign_btn2, submit_btn1, submit_btn2])
+    # prompt1.change(fn=reset_gallery, inputs=[], outputs=[gallery_state1])
+    # prompt2.change(fn=reset_gallery, inputs=[], outputs=[gallery_state2])
+    next_btn1.click(fn=generate_image, inputs=[participant, scenario, prompt1, active_tab, like_image1, dislike_image1], outputs=[images_method1])
+    next_btn2.click(fn=generate_image, inputs=[participant, scenario, prompt2, active_tab, like_image2, dislike_image2], outputs=[images_method2])
     sim_radio1.change(fn=check_satisfaction, inputs=[sim_radio1, active_tab], outputs=[submit_btn1, redesign_btn1])
     sim_radio2.change(fn=check_satisfaction, inputs=[sim_radio2, active_tab], outputs=[submit_btn2, redesign_btn2])
-    dislike_radio1.select(fn=select_dislike, inputs=[dislike_radio1, gallery_state1], outputs=[dislike_image1])
-    like_radio1.select(fn=select_dislike, inputs=[like_radio1, gallery_state1], outputs=[like_image1])
-    dislike_radio2.select(fn=select_dislike, inputs=[dislike_radio2, gallery_state2], outputs=[dislike_image2])
-    like_radio2.select(fn=select_dislike, inputs=[like_radio2, gallery_state2], outputs=[like_image2])
+    dislike_radio1.select(fn=select_image, inputs=[dislike_radio1, images_method1], outputs=[dislike_image1])
+    like_radio1.select(fn=select_image, inputs=[like_radio1, images_method1], outputs=[like_image1])
+    dislike_radio2.select(fn=select_image, inputs=[dislike_radio2, images_method2], outputs=[dislike_image2])
+    like_radio2.select(fn=select_image, inputs=[like_radio2, images_method2], outputs=[like_image2])
 
     redesign_btn1.click(
         fn=redesign, 
-        inputs=[participant, scenario, prompt1, sim_radio1, response1, images_method1, active_tab], 
-        outputs=[gallery_state1, sim_radio1, response1, prompt1, next_btn1, redesign_btn1, submit_btn1]
+        inputs=[participant, scenario, prompt1, sim_radio1, images_method1, history_images1, active_tab], 
+        outputs=[sim_radio1, dislike_radio1, like_radio1, images_method1, history_images1, example1.dataset, prompt1, next_btn1, redesign_btn1, submit_btn1]
     )
     redesign_btn2.click(
         fn=redesign, 
-        inputs=[participant, scenario, prompt2, sim_radio2, response2, images_method2, active_tab], 
-        outputs=[gallery_state2, sim_radio2, response2, prompt2, next_btn2, redesign_btn2, submit_btn2]
+        inputs=[participant, scenario, prompt2, sim_radio2, images_method2, history_images2, active_tab], 
+        outputs=[sim_radio2, dislike_radio2, like_radio2, images_method2, history_images2, example2.dataset, prompt2, next_btn2, redesign_btn2, submit_btn2]
     )
     submit_btn1.click(fn=save_response, 
-        inputs=[participant, scenario, prompt1, sim_radio1, response1, images_method1, active_tab], 
-        outputs=[images_method1, gallery_state1, sim_radio1, dislike_radio1, like_radio1, like_image1, dislike_image1, prompt1, response1, next_btn1, redesign_btn1, submit_btn1, tabs, scenario])
+        inputs=[participant, scenario, prompt1, sim_radio1, active_tab], 
+        outputs=[sim_radio1, dislike_radio1, like_radio1, like_image1, dislike_image1, images_method1, history_images1, example1.dataset, prompt1, next_btn1, redesign_btn1, submit_btn1, scenario, tabs])
     
     submit_btn2.click(fn=save_response, 
-        inputs=[participant, scenario, prompt2, sim_radio2, response2, images_method2, active_tab], 
-        outputs=[images_method2, gallery_state2, sim_radio2,  dislike_radio2, like_radio2, like_image2, dislike_image2, prompt2, response2, next_btn2, redesign_btn2, submit_btn2, tabs, scenario])
+        inputs=[participant, scenario, prompt2, sim_radio2, active_tab], 
+        outputs=[sim_radio2,  dislike_radio2, like_radio2, like_image2, dislike_image2, images_method2, history_images2, example2.dataset, prompt2, next_btn2, redesign_btn2, submit_btn2, scenario, tabs])
 
 
 if __name__ == "__main__":
diff --git a/images/.DS_Store b/images/.DS_Store
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diff --git a/utils.py b/utils.py
index 2ef53a920b059ba1e01d349fb89952682fbdf16f..472b7e81ab150b8bc748a6775ab0406b923a71de 100644
--- a/utils.py
+++ b/utils.py
@@ -5,6 +5,9 @@ import torch
 import os
 from openai import OpenAI
 import subprocess
+import spaces #[uncomment to use ZeroGPU]
+import base64
+from io import BytesIO
 
 T2I_MODELS = {
     "Stable Diffusion v2.1": "stabilityai/stable-diffusion-2-1", 
@@ -15,35 +18,31 @@ T2I_MODELS = {
 
 SCENARIOS = {
   "Product advertisement": "You are designing an advertising campaign for a new line of coffee machines. To ensure the campaign resonates with a wider audience, you use generative models to create marketing images that showcase a variety of users interacting with the product.",
-  "Tourist promotion": "You are creating a travel campaign to attract a diverse range of visitors to a specific destination. To make the promotional materials more engaging and inclusive, you use generative models to design posters that highlight a broader array of experiences.",
-  "Fictional character generation": "You are creating a narrative superhero game where the player often interacts with multiple other non-player characters in the story. To test how different characters would affect the experience of gameplay, you decide to use generative models to help construct characters for (play)testing.",
-  "Interior Design": "You have a one-bedroom apartment and want to arrange your bed, desk, and dresser in the best way possible. You love the color white and want to ensure your space feels bright and open. To make a decision, you need a way to visualize different furniture placements before setting everything up.",
-#   "Education & accessibility": "You are a grade school teacher and the lesson of the day is about teamwork. Some of your students may have a difficult time visualizing what teamwork looks like because they are either (1) too young, (2) English is not their first language, or (3) they may have cognitive impairments that make it difficult for them to visualize concepts (e.g. aphantasia).."
+  "Tourist promotion": "You are creating a travel campaign to attract a variety of visitors to a specific destination. To make the promotional materials more engaging, you use generative models to design posters that highlight a broader array of experiences.",
+  "Fictional character generation": "You are creating a superhero video game that’s fun and relatable to a range of users. You decide to use generative models to help visualize a new character.",
+  "Interior Design": "You are helping design the furniture layout for a model one-bedroom rental apartment. To make the apartment appealing to different potential tenants, you try to visualize different furniture placements before setting everything up.",
 }
 
 PROMPTS = {
-    "Product advertisement": "Design a marketing advertisement image for a coffee machine.",
-    "Tourist promotion": "Design a travel promotional poster to showcase the beauty and attractions of a tourist destination.",
-    "Fictional character generation": "Generate a character of a superhero.",
-    "Interior Design": "Generate an one-bedroom apartment interior design.",
-    # "Education & accessibility": "Generate an image of grade school students buildind a sandcastle together on the beach."
+    "Product advertisement": "Design an advertisement image showcasing a range of users operating coffee machines.",
+    "Tourist promotion": "Design a promotional poster to attract a variety of visitors to a tourist destination.",
+    "Fictional character generation": "Design a video game superhero character that is relatable. ",
+    "Interior Design": "Design an apartment that’s appealing to potential tenants.",
 }
 
 IMAGES = {
     "Product advertisement": {"baseline": ["images/scenario1_base1.png","images/scenario1_base2.png","images/scenario1_base3.png","images/scenario1_base4.png"], 
-                                            "ours": ["images/scenario1_our1.png","images/scenario1_our2.png","images/scenario1_our3.png","images/scenario1_our4.png"]},
-    "Tourist promotion": {"baseline": ["images/scenario5_base1.png","images/scenario5_base2.png","images/scenario5_base3.png","images/scenario5_base4.png"], 
-                                            "ours": ["images/scenario5_our1.png","images/scenario5_our2.png","images/scenario5_our3.png","images/scenario5_our4.png"]},
-    "Fictional character generation": {"baseline": ["images/scenario2_base1.png","images/scenario2_base2.png","images/scenario2_base3.png","images/scenario2_base4.png"], 
-                                            "ours": ["images/scenario2_our1.png","images/scenario2_our2.png","images/scenario2_our3.png","images/scenario2_our4.png"]},
-    "Interior Design": {"baseline": ["images/scenario3_base1.png","images/scenario3_base2.png","images/scenario3_base3.png","images/scenario3_base4.png"], 
-                                            "ours": ["images/scenario3_our1.png","images/scenario3_our2.png","images/scenario3_our3.png","images/scenario3_our4.png"]},
-    # "Education & accessibility": {"baseline": ["images/scenario4_base1.png","images/scenario4_base2.png","images/scenario4_base3.png","images/scenario4_base4.png"], 
-                                            # "ours": ["images/scenario4_our1.png","images/scenario4_our2.png","images/scenario4_our3.png","images/scenario4_our4.png"]},
+                                            "ours": ["images/scenario1_ours1.png","images/scenario1_ours2.png","images/scenario1_ours3.png","images/scenario1_ours4.png"]},
+    "Tourist promotion": {"baseline": ["images/scenario2_base1.png","images/scenario2_base2.png","images/scenario2_base3.png","images/scenario2_base4.png"], 
+                                            "ours": ["images/scenario2_ours1.png","images/scenario2_ours2.png","images/scenario2_ours3.png","images/scenario2_ours4.png"]},
+    "Fictional character generation": {"baseline": ["images/scenario3_base1.png","images/scenario3_base2.png","images/scenario3_base3.png","images/scenario3_base4.png"], 
+                                            "ours": ["images/scenario3_ours1.png","images/scenario3_ours2.png","images/scenario3_ours3.png","images/scenario3_ours4.png"]},
+    "Interior Design": {"baseline": ["images/scenario4_base1.png","images/scenario4_base2.png","images/scenario3_base4.png","images/scenario4_base4.png"], 
+                                            "ours": ["images/scenario4_ours1.png","images/scenario4_ours2.png","images/scenario4_ours3.png","images/scenario4_ours4.png"]},
 }
 
 OPTIONS = ["Very Unsatisfied", "Unsatisfied", "Slightly Unsatisfied", "Neutral", "Slightly Satisfied", "Satisfied", "Very Satisfied"]
-IMAGE_OPTIONS = ["First Image", "Second Image", "Third Image", "Fourth Image", "None of them"]
+IMAGE_OPTIONS = ["First Image", "Second Image", "Third Image", "Fourth Image"]
 INSTRUCTION = "📌 **Instruction**: Now, we want to understand your satisfaction with the images generated. <br /> 📌 Step 1: You will start from evaluating the following images based on the given prompt. <br /> 📌 Step 2: Then please modify the prompt according to your expectations for the given scenario background, and answer the evaluation question **until you are satisfied** with at least one of the images generated below. If you are not satisfied with the generated images, you can repeatedly modify the prompts for at most **5 times**."
 def clean_cache():
     subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
@@ -102,3 +101,90 @@ Can you give me {num_prompts} modified prompts for the prompt '{prompt}' please.
 
     messages.append({"role": "user", "content": f"{message}"})
     return messages
+
+def encode_image(image):
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")  
+    return base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+def get_personalize_message(prompt, history_prompts, history_feedback, like_image, dislike_image):
+    messages = [{"role": "system", "content": f"You will act as a prompt optimization assistant that helps refine an original prompt based on user feedback over multiple rounds of image generation. The goal is to dynamically adjust the prompt to better align with user preferences while preserving the original intent."}]
+
+    message = f"""The process consists of a maximum of 5 rounds. 
+    Users start with an initial prompt and generate 4 images per round. After reviewing the images, users will modify the prompt based on their preferences. Then we will generate new images based on the modified prompt. 
+    Users will rate the generated images on a scale from ["Very Unsatisfied", "Unsatisfied", "Slightly Unsatisfied", "Neutral", "Slightly Satisfied", "Satisfied", "Very Satisfied"], indicating how satisfied they are with the results.
+    
+    Your task is to analyze the sequence of modified prompts and corresponding ratings to refine the prompt dynamically, ensuring improved results in the next rounds. For each new round, you should:
+    Incorporate the user's modifications: Use the latest user-revised prompt as a reference but retain essential details from previous rounds if they contributed positively.
+    
+    Analyze user ratings:
+    If the rating is high ("Satisfied", "Very Satisfied") → Maintain key aspects of the most recent prompt since it aligns well with user preferences.
+    If the rating is medium ("Slightly Unsatisfied", "Neutral", "Slightly Satisfied") → Adjust minor details that could improve alignment with the user’s preferences, considering the changes from previous rounds.
+    If the rating is low ("Very Unsatisfied", "Unsatisfied") → Identify aspects that might be causing dissatisfaction (e.g., unwanted elements, style mismatch) and rework the prompt while keeping the user’s core intent intact.
+
+    Refine the prompt intelligently and ensure the following: 
+    - The updated prompt reflects user feedback without unnecessary repetition.
+    - Unwanted elements (if any) from previous rounds are removed.
+    - Preferred elements are retained and enhanced.
+    - The modifications remain subtle but progressive to ensure smooth refinement over multiple rounds.
+    - Maintain coherence: Avoid drastic changes that might deviate from the original intent unless the user explicitly requests them.
+    
+
+    Now given the following revised prompts and ratings from user\n:
+"""
+    
+    for his_prompt, feedback in zip(history_prompts, history_feedback):
+        message += f"Revised prompt: {his_prompt}; Rating: {feedback}\n"
+    
+    message += f"\nWe also provide the user's preferred image during this process as the first image provided and the disliked image as the second image\n"
+    message += "Now, please optimize current prompt and only output the modified prompt: '{prompt}'"""
+
+    messages.append({
+            "role": "user",
+            "content": [
+                {"type": "text", "text": f"{message}"},
+            ],
+        })
+    if like_image:
+        like_image_base64 = encode_image(like_image)
+        messages[-1]["content"].append({
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/jpeg;base64,{like_image_base64}",
+                    },
+                })
+    if dislike_image:
+        dislike_image_base64 = encode_image(dislike_image)
+        messages[-1]["content"].append({
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/jpeg;base64,{dislike_image_base64}",
+                    },
+                })
+        
+    print(messages)
+    
+    return messages
+
+@spaces.GPU 
+def call_llm_refine_prompt(prompt, num_prompts=5, max_tokens=1000, temperature=0.7, top_p=0.9):
+    print(f"loading {default_llm_model}")
+    global llm_pipe
+    if not llm_pipe:
+        llm_pipe = transformers.pipeline("text-generation", model=default_llm_model, model_kwargs={"torch_dtype": torch_dtype}, device_map="auto")
+    
+    messages = get_refine_msg(prmpt, num_prompts)
+    terminators = [
+        llm_pipe.tokenizer.eos_token_id,
+        llm_pipe.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+    ]
+    outputs = llm_pipe(
+        messages,
+        max_new_tokens=max_tokens,
+        eos_token_id=terminators,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p,
+    )
+    prompt_list = clean_response_gpt(outputs[0]["generated_text"][-1]["content"])
+    return prompt_list
\ No newline at end of file