create app.py
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app.py
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import pandas as pd
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import gradio as gr
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df = pd.read_csv("liked_images.csv")
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df['url'] = df['url'].apply(lambda x: '<a href= "' + str(x) + '" target="_blank"> <img src= "' + str(x) + '"/> </a>') #'<img src= "' + str(x) + '"/> </a>')
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df['seed'] = df['seed'].apply(lambda x: str(x))
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df['width'] = df['width'].apply(lambda x: str(x))
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df['height'] = df['height'].apply(lambda x: str(x))
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df['steps'] = df['steps'].apply(lambda x: str(x))
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df['source'] = df['source'].apply(lambda x: str(x))
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df = df[[ 'url', 'prompt', 'seed', 'width', 'height', 'steps', 'source']]
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def display_df():
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df_images = df.head()
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return df_images
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def display_next10(dataframe, end):
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start = (end or dataframe.index[-1]) + 1
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end = start + 9
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df_images = df.loc[start:end]
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return df_images, end
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#Gradio Blocks
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with gr.Blocks() as demo:
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gr.Markdown("<h1><center>Utility Gradio Space for viewing PlaygroundAI Images</center></h1>")
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#gr.Markdown("""<img src='https://xxxxxxxx.jpg' class='center'> <br> """)
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gr.Markdown(
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"""<div align="center">This Tool helps you to analyze and inspect the images and corresponding prompts from <a href = "https://playgroundai.com/">Playground AI</a> Images.<br><a href="https://twitter.com/Suhail">Suhail</a> has recently shared an open dataset of all the liked images and their prompts from PlaygroundAI on <a href="https://github.com/playgroundai/liked_images">Github here</a>. This is an attempt to explore this dataset beautifully using the power and flexibility of Gradio!<br><b>To use the tool:<br>First, click on the 'Initial' button, and then iteratively on the 'Next 10' button.<br><b>Bonus:</b>Click on images to get the original PlaygroundAI image displayed on next tab</div>""")
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with gr.Row():
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num_end = gr.Number(visible=False)
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b1 = gr.Button("Get Initial dataframe")
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b2 = gr.Button("Next 10 Rows")
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with gr.Row():
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out_dataframe = gr.Dataframe(wrap=True, max_rows=10, overflow_row_behaviour= "paginate", datatype = ["markdown", "markdown", "str", "str", "str", "str", "str", "str"])
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b1.click(fn=display_df, outputs=out_dataframe)
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b2.click(fn=display_next10, inputs= [out_dataframe, num_end ], outputs=[out_dataframe, num_end])
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demo.launch(debug=True, show_error=True)
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