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| import gradio as gr | |
| # Placeholder for the prediction function | |
| def predict(mode, text, image_path): | |
| """ | |
| This is a placeholder for the final prediction logic. | |
| It will return a hardcoded dictionary to demonstrate the output format. | |
| """ | |
| if mode == "Multimodal": | |
| result_text = "Result for Multimodal input: a category from a real model. Confidence: 0.95" | |
| elif mode == "Text Only": | |
| result_text = ( | |
| "Result for Text Only input: a category from a real model. Confidence: 0.92" | |
| ) | |
| elif mode == "Image Only": | |
| result_text = "Result for Image Only input: a category from a real model. Confidence: 0.88" | |
| else: | |
| result_text = "Please select a classification mode." | |
| return { | |
| "label": result_text, | |
| "confidences": { | |
| "abcat0100000": 0.05, | |
| "abcat0200000": 0.10, | |
| "abcat0300000": 0.20, | |
| "abcat0400000": 0.45, | |
| "abcat0500000": 0.20, | |
| }, | |
| } | |
| # Function to update input visibility based on mode selection | |
| def update_inputs(mode): | |
| 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: # Default case | |
| return gr.Textbox(visible=True), gr.Image(visible=True) | |
| # Gradio Interface using Blocks | |
| with gr.Blocks(title="Multimodal Product Classification") as demo: | |
| with gr.Tabs(): | |
| with gr.TabItem("App"): | |
| gr.Markdown("# Multimodal Product Classifier") | |
| gr.Markdown("Classify products using either text, images, or both.") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| with gr.Column(variant="panel"): | |
| gr.Markdown("### ⚙️ 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_btn = gr.Button("🚀 Classify Product", variant="primary") | |
| with gr.Column(scale=1): | |
| with gr.Column(variant="panel"): | |
| gr.Markdown("### 📊 Classification Results") | |
| output_label = gr.Label( | |
| label="Predicted Category", num_top_classes=5 | |
| ) | |
| with gr.Accordion("How to use this demo", open=False): | |
| gr.Markdown( | |
| """ | |
| This demo 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. | |
| """ | |
| ) | |
| with gr.TabItem("About"): | |
| gr.Markdown( | |
| """ | |
| ### About the Project | |
| This project demonstrates a multimodal classification system trained on data from Best Buy. It uses a Multilayer Perceptron (MLP) model trained on pre-generated embeddings from a Text-based model (MiniLM-L6) and an Image-based model (ConvNeXtV2). | |
| """ | |
| ) | |
| with gr.TabItem("Architecture"): | |
| gr.Markdown( | |
| """ | |
| ### Model Architecture | |
| This section would contain details about the MLP architecture, the embedding models used, and a diagram explaining the data flow. | |
| """ | |
| ) | |
| # Event listeners for conditional rendering | |
| mode_radio.change( | |
| fn=update_inputs, inputs=mode_radio, outputs=[text_input, image_input] | |
| ) | |
| # Event listener for the classify button | |
| classify_btn.click( | |
| fn=predict, inputs=[mode_radio, text_input, image_input], outputs=output_label | |
| ) | |
| demo.launch() | |