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app.py

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+
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import json
+
+# Load the model and tokenizer
+model_id = "selvaonline/shopping-assistant"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+
+# Load the categories
+try:
+    from huggingface_hub import hf_hub_download
+    categories_path = hf_hub_download(repo_id=model_id, filename="categories.json")
+    with open(categories_path, "r") as f:
+        categories = json.load(f)
+except Exception as e:
+    print(f"Error loading categories: {str(e)}")
+    categories = ["electronics", "clothing", "home", "kitchen", "toys", "other"]
+
+def classify_text(text):
+    """
+    Classify the text using the model
+    """
+    # Prepare the input
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    
+    # Get the model prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predictions = torch.sigmoid(outputs.logits)
+    
+    # Get the top categories
+    top_categories = []
+    for i, score in enumerate(predictions[0]):
+        if score > 0.5:  # Threshold for multi-label classification
+            top_categories.append((categories[i], score.item()))
+    
+    # Sort by score
+    top_categories.sort(key=lambda x: x[1], reverse=True)
+    
+    # Format the results
+    if top_categories:
+        result = f"Top categories for '{text}':\n\n"
+        for category, score in top_categories:
+            result += f"- {category}: {score:.4f}\n"
+        
+        result += f"\nBased on your query, I would recommend looking for deals in the **{top_categories[0][0]}** category."
+    else:
+        result = f"No categories found for '{text}'. Please try a different query."
+    
+    return result
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(
+        lines=2, 
+        placeholder="Enter your shopping query here...",
+        label="Shopping Query"
+    ),
+    outputs=gr.Markdown(label="Results"),
+    title="Shopping Assistant",
+    description="""
+    This demo shows how to use the Shopping Assistant model to classify shopping queries into categories.
+    Enter a shopping query below to see which categories it belongs to.
+    
+    Examples:
+    - "I'm looking for headphones"
+    - "Do you have any kitchen appliance deals?"
+    - "Show me the best laptop deals"
+    - "I need a new smart TV"
+    """,
+    examples=[
+        ["I'm looking for headphones"],
+        ["Do you have any kitchen appliance deals?"],
+        ["Show me the best laptop deals"],
+        ["I need a new smart TV"]
+    ],
+    theme=gr.themes.Soft()
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()