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