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import gradio as gr |
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def predict(mode, text, image_path): |
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multimodal_output = { |
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"abcat0100000": 0.05, |
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"abcat0200000": 0.10, |
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"abcat0300000": 0.20, |
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"abcat0400000": 0.45, |
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"abcat0500000": 0.20, |
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} |
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text_only_output = { |
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"abcat0100000": 0.08, |
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"abcat0200000": 0.15, |
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"abcat0300000": 0.25, |
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"abcat0400000": 0.35, |
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"abcat0500000": 0.17, |
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} |
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image_only_output = { |
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"abcat0100000": 0.10, |
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"abcat0200000": 0.20, |
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"abcat0300000": 0.30, |
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"abcat0400000": 0.25, |
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"abcat0500000": 0.15, |
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} |
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if mode == "Multimodal": |
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return multimodal_output |
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elif mode == "Text Only": |
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return text_only_output |
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elif mode == "Image Only": |
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return image_only_output |
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else: |
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return {} |
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def update_inputs(mode: str): |
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if mode == "Multimodal": |
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return gr.Textbox(visible=True), gr.Image(visible=True) |
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elif mode == "Text Only": |
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return gr.Textbox(visible=True), gr.Image(visible=False) |
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elif mode == "Image Only": |
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return gr.Textbox(visible=False), gr.Image(visible=True) |
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else: |
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return gr.Textbox(visible=True), gr.Image(visible=True) |
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css_code = """ |
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/* Target the footer container by its ID and apply fixed positioning */ |
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#footer-container { |
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position: fixed; |
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bottom: 0; |
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left: 0; |
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right: 0; |
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z-index: 1000; /* Ensure it stays on top of other content */ |
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background-color: var(--background-fill-primary); /* Use a Gradio theme variable */ |
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padding: var(--spacing-md); |
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border-top: 1px solid var(--border-color-primary); |
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} |
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/* Add padding to the body to prevent content from being hidden by the footer */ |
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.gradio-container { |
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padding-bottom: 70px !important; |
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} |
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""" |
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with gr.Blocks( |
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title="Multimodal Product Classification", |
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theme=gr.themes.Ocean(), |
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css=css_code, |
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) as demo: |
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with gr.Tabs(): |
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with gr.TabItem("App"): |
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gr.Markdown("# ποΈ Multimodal Product Classification") |
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with gr.Row(equal_height=True): |
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with gr.Column(scale=1): |
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with gr.Column(): |
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gr.Markdown("## βοΈ Classification Inputs") |
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mode_radio = gr.Radio( |
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choices=["Multimodal", "Text Only", "Image Only"], |
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value="Multimodal", |
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label="Choose Classification Mode:", |
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) |
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text_input = gr.Textbox( |
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label="Product Description:", |
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placeholder="e.g., Apple iPhone 15 Pro Max 256GB", |
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) |
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image_input = gr.Image( |
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label="Product Image", type="filepath", visible=True |
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) |
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classify_button = gr.Button( |
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"β¨ Classify Product", variant="primary" |
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) |
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with gr.Column(scale=2): |
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with gr.Column(): |
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gr.Markdown("## π Results") |
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gr.Markdown( |
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"""**π‘ How to use this app** |
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This app classifies a product based on its description and image. |
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- **Multimodal:** Uses both text and image for the most accurate prediction. |
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- **Text Only:** Uses only the product description. |
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- **Image Only:** Uses only the product image. |
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""" |
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) |
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output_label = gr.Label( |
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label="Predict category", num_top_classes=5 |
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) |
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with gr.TabItem("About"): |
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gr.Markdown("""...""") |
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with gr.TabItem("Model"): |
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gr.Markdown("""...""") |
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with gr.Row(elem_id="footer-container"): |
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gr.HTML(""" |
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<div style="text-align: center;"> |
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<b>Connect with me:</b> πΌ <a href="https://www.linkedin.com/in/alex-turpo/" target="_blank">LinkedIn</a> β’ |
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π± <a href="https://github.com/iBrokeTheCode" target="_blank">GitHub</a> β’ |
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π€ <a href="https://huggingface.co/iBrokeTheCode" target="_blank">Hugging Face</a> |
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</div> |
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""") |
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mode_radio.change( |
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fn=update_inputs, |
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inputs=mode_radio, |
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outputs=[text_input, image_input], |
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) |
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classify_button.click( |
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fn=predict, inputs=[mode_radio, text_input, image_input], outputs=output_label |
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) |
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demo.launch() |
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