chore: Add base code for Gradio app

app.py

@@ -1,9 +1,127 @@
 import gradio as gr
 
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Updated placeholder for the prediction function
+def predict(mode, text, image_path):
+    """
+    This placeholder function now returns a dictionary
+    in the format expected by the gr.Label component.
+    """
+    # Hardcoded, sample output. In the future, this will come from your model.
+    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 {}  # Return an empty dictionary for no selection
+
+
+# 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 = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()

base.py

@@ -0,0 +1,117 @@
+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()