preference images update
Browse files- __pycache__/live_preview_helpers.cpython-310.pyc +0 -0
- __pycache__/optim_utils.cpython-310.pyc +0 -0
- __pycache__/utils.cpython-310.pyc +0 -0
- app.py +65 -10
- utils.py +1 -1
__pycache__/live_preview_helpers.cpython-310.pyc
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__pycache__/optim_utils.cpython-310.pyc
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__pycache__/utils.cpython-310.pyc
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app.py
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@@ -8,7 +8,7 @@ import re
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import open_clip
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from optim_utils import optimize_prompt
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from utils import clean_response_gpt, setup_model, init_gpt_api, call_gpt_api, get_refine_msg, clean_cache
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from utils import SCENARIOS, PROMPTS, IMAGES, OPTIONS, T2I_MODELS, INSTRUCTION
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import spaces #[uncomment to use ZeroGPU]
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import transformers
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import gspread
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@@ -274,6 +274,18 @@ def check_evaluation(sim_radio, response):
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return True
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def redesign(participant, scenario, prompt, sim_radio, response, images_method, active_tab):
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global counter1, counter2, responses_memory, current_task1, current_task2
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method = current_task1 if active_tab == "Task A" else current_task2
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@@ -367,6 +379,11 @@ css="""
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max-width: 1000px;
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}
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#button-container {
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display: flex;
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justify-content: center; /* Centers the buttons horizontally */
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@@ -418,17 +435,34 @@ with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Inconsolata"), "
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)
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next_btn1 = gr.Button("Generate", variant="primary", scale=1, interactive=False, visible=False)
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with gr.
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-
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-
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with gr.Column(elem_id="col-container2"):
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gr.Markdown("### π Evaluation")
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sim_radio1 = gr.Radio(
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OPTIONS,
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label="How would you
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type="value",
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elem_classes=["gradio-radio"]
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)
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response1 = gr.Textbox(
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label="Verbally describe key differences found in the image pair.",
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max_lines=1,
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@@ -455,21 +489,37 @@ with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Inconsolata"), "
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scale=4,
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visible=False
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)
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-
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next_btn2 = gr.Button("Generate", variant="primary", scale=1, interactive=False, visible=False)
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with gr.
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with gr.Column(elem_id="col-container2"):
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gr.Markdown("### π Evaluation")
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sim_radio2 = gr.Radio(
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OPTIONS,
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label="How would you
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type="value",
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elem_classes=["gradio-radio"]
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)
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response2 = gr.Textbox(
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label="Verbally describe key differences found in the image pair.",
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max_lines=1,
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@@ -494,6 +544,11 @@ with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Inconsolata"), "
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next_btn2.click(fn=generate_image, inputs=[participant, scenario, prompt2, gallery_state2, active_tab], outputs=[images_method2])
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sim_radio1.change(fn=check_satisfaction, inputs=[sim_radio1, active_tab], outputs=[submit_btn1, redesign_btn1])
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sim_radio2.change(fn=check_satisfaction, inputs=[sim_radio2, active_tab], outputs=[submit_btn2, redesign_btn2])
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redesign_btn1.click(
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fn=redesign,
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inputs=[participant, scenario, prompt1, sim_radio1, response1, images_method1, active_tab],
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import open_clip
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from optim_utils import optimize_prompt
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from utils import clean_response_gpt, setup_model, init_gpt_api, call_gpt_api, get_refine_msg, clean_cache
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from utils import SCENARIOS, PROMPTS, IMAGES, OPTIONS, T2I_MODELS, INSTRUCTION, IMAGE_OPTIONS
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import spaces #[uncomment to use ZeroGPU]
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import transformers
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import gspread
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return True
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def select_dislike(like_radio, images_method):
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if like_radio == IMAGE_OPTIONS[0]:
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return images_method[0]
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elif like_radio == IMAGE_OPTIONS[1]:
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return images_method[1]
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elif like_radio == IMAGE_OPTIONS[2]:
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return images_method[2]
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elif like_radio == IMAGE_OPTIONS[3]:
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return images_method[3]
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else:
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return None
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def redesign(participant, scenario, prompt, sim_radio, response, images_method, active_tab):
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global counter1, counter2, responses_memory, current_task1, current_task2
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method = current_task1 if active_tab == "Task A" else current_task2
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max-width: 1000px;
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}
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#col-container3 {
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margin: 0 auto;
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max-width: 300px;
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}
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#button-container {
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display: flex;
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justify-content: center; /* Centers the buttons horizontally */
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)
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next_btn1 = gr.Button("Generate", variant="primary", scale=1, interactive=False, visible=False)
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with gr.Row():
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with gr.Column(elem_id="col-container"):
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gallery_state1 = gr.State([])
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images_method1 = gr.Gallery(show_label=False, columns=[4], rows=[1], height=420, elem_id="gallery")
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with gr.Column(elem_id="col-container3"):
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like_image1 = gr.Image(label="Satisfied Image", width=200, height=200, sources='upload')
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dislike_image1 = gr.Image(label="Unsatisfied Image", width=200, height=200, sources='upload')
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with gr.Column(elem_id="col-container2"):
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gr.Markdown("### π Evaluation")
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sim_radio1 = gr.Radio(
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OPTIONS,
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label="How would you rate your satisfaction with the generated images, based on your expectations for the specified scenario?",
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type="value",
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elem_classes=["gradio-radio"]
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)
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like_radio1 = gr.Radio(
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IMAGE_OPTIONS,
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label="Select the image you are most satisfied.",
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type="value",
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elem_classes=["gradio-radio"]
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)
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dislike_radio1 = gr.Radio(
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IMAGE_OPTIONS,
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label="Select the image you are most unsatisfied.",
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type="value",
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elem_classes=["gradio-radio"]
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)
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response1 = gr.Textbox(
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label="Verbally describe key differences found in the image pair.",
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max_lines=1,
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scale=4,
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visible=False
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)
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next_btn2 = gr.Button("Generate", variant="primary", scale=1, interactive=False, visible=False)
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with gr.Row():
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with gr.Column(elem_id="col-container"):
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gallery_state2 = gr.State(IMAGES[DEFAULT_SCENARIO]["ours"])
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images_method2 = gr.Gallery(height=420, show_label=False, columns=[4], rows=[1], elem_id="gallery")
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with gr.Column(elem_id="col-container3"):
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like_image2 = gr.Image(label="Satisfied Image", width=200, height=200, sources='upload')
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dislike_image2 = gr.Image(label="Unsatisfied Image", width=200, height=200, sources='upload')
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with gr.Column(elem_id="col-container2"):
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gr.Markdown("### π Evaluation")
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sim_radio2 = gr.Radio(
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OPTIONS,
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label="How would you rate your satisfaction with the generated images, based on your expectations for the specified scenario?",
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type="value",
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elem_classes=["gradio-radio"]
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)
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like_radio2 = gr.Radio(
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IMAGE_OPTIONS,
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label="Select the image you are most satisfied.",
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type="value",
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elem_classes=["gradio-radio"]
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)
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dislike_radio2 = gr.Radio(
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IMAGE_OPTIONS,
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label="Select the image you are most unsatisfied.",
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type="value",
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elem_classes=["gradio-radio"]
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)
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response2 = gr.Textbox(
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label="Verbally describe key differences found in the image pair.",
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max_lines=1,
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next_btn2.click(fn=generate_image, inputs=[participant, scenario, prompt2, gallery_state2, active_tab], outputs=[images_method2])
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sim_radio1.change(fn=check_satisfaction, inputs=[sim_radio1, active_tab], outputs=[submit_btn1, redesign_btn1])
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sim_radio2.change(fn=check_satisfaction, inputs=[sim_radio2, active_tab], outputs=[submit_btn2, redesign_btn2])
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dislike_radio1.select(fn=select_dislike, inputs=[dislike_radio1, gallery_state1], outputs=[dislike_image1])
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like_radio1.select(fn=select_dislike, inputs=[like_radio1, gallery_state1], outputs=[like_image1])
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dislike_radio2.select(fn=select_dislike, inputs=[dislike_radio2, gallery_state2], outputs=[dislike_image2])
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like_radio2.select(fn=select_dislike, inputs=[like_radio2, gallery_state2], outputs=[like_image2])
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redesign_btn1.click(
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fn=redesign,
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inputs=[participant, scenario, prompt1, sim_radio1, response1, images_method1, active_tab],
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utils.py
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@@ -43,7 +43,7 @@ IMAGES = {
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}
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OPTIONS = ["Very Unsatisfied", "Unsatisfied", "Slightly Unsatisfied", "Neutral", "Slightly Satisfied", "Satisfied", "Very Satisfied"]
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INSTRUCTION = "π **Instruction**: Now, we want to understand your satisfaction with the images generated. <br /> π Step 1: You will start from evaluating the following images based on the given prompt. <br /> π 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**."
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def clean_cache():
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subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
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}
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OPTIONS = ["Very Unsatisfied", "Unsatisfied", "Slightly Unsatisfied", "Neutral", "Slightly Satisfied", "Satisfied", "Very Satisfied"]
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IMAGE_OPTIONS = ["First Image", "Second Image", "Third Image", "Fourth Image", "None of them"]
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INSTRUCTION = "π **Instruction**: Now, we want to understand your satisfaction with the images generated. <br /> π Step 1: You will start from evaluating the following images based on the given prompt. <br /> π 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**."
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def clean_cache():
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subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
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