Commit
Β·
114611c
1
Parent(s):
a1406a7
Update app.py
Browse files
app.py
CHANGED
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@@ -27,29 +27,33 @@ LINKS = {
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"karlo": "https://huggingface.co/kakaobrain/karlo-v1-alpha",
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}
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SD_1_5_RESULT = """
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-
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\n Stable Diffusion 1-5 is the most used open-source text-to-image model offering an amazing speed-to-image-quality trade-off!
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Check out your soulmate [here](https://huggingface.co/runwayml/stable-diffusion-v1-5).
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"""
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SD_2_1_RESULT = """
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\n Stable Diffusion 2-1 is the latest open-source release of Stable Diffusion and allows to render stunning images of much larger sizes than Stable Diffusion v1.
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Try it out [here](https://huggingface.co/stabilityai/stable-diffusion-2-1).
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"""
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IF_V1_0 = """
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\n IF v1-0 is by far the largest of the open-sourced text-to-image models and is a very powerful image generator.
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Besides being able to generate multiple complex concepts in the same image, IF v1-0 is also extremely good at generating text in images.
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Check out your new best friend [here](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0)
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"""
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KARLO = """
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\n Karlo is based on the same architecture as DALLE-2 and has been trained on the [well curated COYO dataset](https://huggingface.co/datasets/kakaobrain/coyo-700m).
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Play around with it [here]("https://huggingface.co/kakaobrain/karlo-v1-alpha").
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"""
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RESULT = {
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@@ -199,9 +203,10 @@ def change_view(row_number, dataframe):
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dataset.push_to_hub(repo_id, token=os.getenv("HF_TOKEN"))
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return {
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intro_view: gr.update(visible=
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result_view: gr.update(visible=True),
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gallery_view: gr.update(visible=False),
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result: RESULT[favorite_model],
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}
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else:
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@@ -209,6 +214,7 @@ def change_view(row_number, dataframe):
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intro_view: gr.update(visible=False),
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result_view: gr.update(visible=False),
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gallery_view: gr.update(visible=True),
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result: "",
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}
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@@ -216,9 +222,10 @@ def change_view(row_number, dataframe):
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TITLE = "# What's Your AI Gen Personality �"
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DESCRIPTION = """
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*In this interactive game you are shown image descriptions along side 4 AI generated images.
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"""
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EXPLANATION = """\n\n
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## What to do π \n\n
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@@ -227,23 +234,23 @@ EXPLANATION = """\n\n
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2. A prompt and 4 different images are displayed
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3. Select your favorite image
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4. After 10 rounds your favorite diffusion model is displayed \n\n
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Parti Prompts is designed to test text-to-image AI models on 1600+ prompts of varying difficulty and categories.
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The images you are shown have been pre-generated with 4 state-of-the-art open-sourced text-to-image models. \n\n
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You answers will be used to contribute to the official [**Open Parti Prompts Leaderboard**](https://huggingface.co/spaces/OpenGenAI/parti-prompts-leaderboard). \n
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By playing this game, you are greatly helping the community to have a better, **human-preference-aligned** metric to compare text-to-image models β€οΈ. \n\n
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Every month, the generated images will be updated with possibly improved models
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"""
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GALLERY_COLUMN_NUM = len(SUBMISSIONS)
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with gr.Blocks() as demo:
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with gr.Column(visible=True) as intro_view:
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gr.Markdown(TITLE)
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gr.Markdown(DESCRIPTION)
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gr.Markdown(EXPLANATION)
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start_button = gr.Button("Start").style(full_width=False)
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headers = ["prompt", "result", "id", "Challenge", "Category", "Note"] + [
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f"choice_{i}" for i in range(len(SUBMISSIONS))
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@@ -272,6 +279,10 @@ with gr.Blocks() as demo:
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)
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gr.Markdown("Click on start to play again!")
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with gr.Column(visible=False):
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selected_image = gr.Number(label="Selected index", value=-1, precision=0)
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@@ -307,7 +318,7 @@ with gr.Blocks() as demo:
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).then(
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fn=change_view,
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inputs=[row_number, dataframe],
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outputs=[intro_view, result_view, gallery_view, result]
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).then(
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fn=process, inputs=[dataframe], outputs=[image_1, image_2, image_3, image_4, prompt, counter]
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)
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@@ -325,7 +336,7 @@ with gr.Blocks() as demo:
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).then(
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fn=change_view,
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inputs=[row_number, dataframe],
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outputs=[intro_view, result_view, gallery_view, result]
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).then(
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fn=process,
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inputs=[dataframe, row_number],
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"karlo": "https://huggingface.co/kakaobrain/karlo-v1-alpha",
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}
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SD_1_5_RESULT = """
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"## The traditional one π₯!
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\n You mostly resonate with **Stable Diffusion 1-5** released by RunwayML.
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\n Stable Diffusion 1-5 is the most used open-source text-to-image model offering an amazing speed-to-image-quality trade-off!
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\n Check out your soulmate [here](https://huggingface.co/runwayml/stable-diffusion-v1-5).
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"""
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SD_2_1_RESULT = """
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## The creative one π¨!
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\n You mostly resonate with **Stable Diffusion 2-1** released by Stability AI.
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\n Stable Diffusion 2-1 is the latest open-source release of Stable Diffusion and allows to render stunning images of much larger sizes than Stable Diffusion v1.
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Try it out [here](https://huggingface.co/stabilityai/stable-diffusion-2-1).
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"""
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IF_V1_0 = """
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## The powerful one β‘!
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\n You mostly resonate with **IF v1-0** released by DeepFloyd.
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\n IF v1-0 is by far the largest of the open-sourced text-to-image models and is a very powerful image generator.
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\n Besides being able to generate multiple complex concepts in the same image, IF v1-0 is also extremely good at generating text in images.
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\n Check out your new best friend [here](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0)
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"""
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KARLO = """
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## The precise one π―!
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\n You mostly resonate with **Karlo** released by KakaoBrain.
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\n Karlo is based on the same architecture as DALLE-2 and has been trained on the [well curated COYO dataset](https://huggingface.co/datasets/kakaobrain/coyo-700m).
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\n Play around with it [here]("https://huggingface.co/kakaobrain/karlo-v1-alpha").
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"""
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RESULT = {
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dataset.push_to_hub(repo_id, token=os.getenv("HF_TOKEN"))
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return {
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intro_view: gr.update(visible=False),
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result_view: gr.update(visible=True),
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gallery_view: gr.update(visible=False),
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start_view: gr.update(visible=True)
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result: RESULT[favorite_model],
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}
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else:
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intro_view: gr.update(visible=False),
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result_view: gr.update(visible=False),
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gallery_view: gr.update(visible=True),
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start_view: gr.update(visible=False)
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result: "",
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}
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TITLE = "# What's Your AI Gen Personality �"
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DESCRIPTION = """
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*In this interactive game you are shown image descriptions along side 4 AI generated images.
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\n Select the image that best fits the image description.
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\n Answer **10** questions to find out what AI generator most resonates with you.
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\n Your submissions contribute to [**Open Parti Prompts Leaderboard**](https://huggingface.co/spaces/OpenGenAI/parti-prompts-leaderboard) β€οΈ
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"""
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EXPLANATION = """\n\n
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## What to do π \n\n
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2. A prompt and 4 different images are displayed
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3. Select your favorite image
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4. After 10 rounds your favorite diffusion model is displayed \n\n
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"""
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NOTE = """
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The prompts you are shown originate from the [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts) dataset.
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Parti Prompts is designed to test text-to-image AI models on 1600+ prompts of varying difficulty and categories.
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The images you are shown have been pre-generated with 4 state-of-the-art open-sourced text-to-image models. \n\n
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You answers will be used to contribute to the official [**Open Parti Prompts Leaderboard**](https://huggingface.co/spaces/OpenGenAI/parti-prompts-leaderboard). \n
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By playing this game, you are greatly helping the community to have a better, **human-preference-aligned** metric to compare text-to-image models β€οΈ. \n\n
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Every month, the generated images will be updated with possibly improved models.
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"""
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GALLERY_COLUMN_NUM = len(SUBMISSIONS)
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with gr.Blocks() as demo:
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gr.Markdown(TITLE)
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with gr.Column(visible=True) as intro_view:
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gr.Markdown(DESCRIPTION)
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gr.Markdown(EXPLANATION)
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headers = ["prompt", "result", "id", "Challenge", "Category", "Note"] + [
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f"choice_{i}" for i in range(len(SUBMISSIONS))
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)
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gr.Markdown("Click on start to play again!")
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with gr.Column(visible=True) as start_view:
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start_button = gr.Button("Start").style(full_width=True)
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gr.Markdown(NOTE)
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with gr.Column(visible=False):
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selected_image = gr.Number(label="Selected index", value=-1, precision=0)
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).then(
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fn=change_view,
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inputs=[row_number, dataframe],
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outputs=[intro_view, result_view, gallery_view, start_view, result]
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).then(
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fn=process, inputs=[dataframe], outputs=[image_1, image_2, image_3, image_4, prompt, counter]
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)
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).then(
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fn=change_view,
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inputs=[row_number, dataframe],
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outputs=[intro_view, result_view, gallery_view, start_view, result]
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).then(
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fn=process,
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inputs=[dataframe, row_number],
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