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Update app.py
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app.py
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import gradio as gr
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# Load
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def parse_query(user_query):
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"""
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Parse
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"""
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return structured_response
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#
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with gr.Blocks() as demo:
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gr.Markdown("# 🛍️ Luxury Fashion Query Parser")
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query_input = gr.Textbox(label="Enter your search query", placeholder="e.g., Gucci men’s perfume under 200AED")
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output_box = gr.
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parse_button = gr.Button("Parse Query")
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parse_button.click(parse_query, inputs=[query_input], outputs=[output_box])
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import gradio as gr
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import torch
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from transformers import CLIPProcessor, CLIPModel
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# Load the FashionCLIP model
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model_name = "patrickjohncyh/fashion-clip"
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model = CLIPModel.from_pretrained(model_name)
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processor = CLIPProcessor.from_pretrained(model_name)
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def parse_query(user_query):
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"""
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Parse fashion-related search queries into structured data.
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"""
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# Define categories relevant to luxury fashion search
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fashion_categories = ["Brand", "Category", "Gender", "Price Range"]
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# Format user query for CLIP
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inputs = processor(text=[user_query], images=None, return_tensors="pt", padding=True)
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# Get model embeddings
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with torch.no_grad():
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outputs = model.get_text_features(**inputs)
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# Simulated parsing output (FashionCLIP itself does not generate structured JSON)
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parsed_output = {
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"Brand": "Gucci" if "Gucci" in user_query else "Unknown",
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"Category": "Perfume" if "perfume" in user_query else "Unknown",
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"Gender": "Men" if "men" in user_query else "Women" if "women" in user_query else "Unisex",
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"Price Range": "Under 200 AED" if "under 200" in user_query else "Above 200 AED",
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}
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return parsed_output
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# Define Gradio UI
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with gr.Blocks() as demo:
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gr.Markdown("# 🛍️ Luxury Fashion Query Parser (FashionCLIP)")
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query_input = gr.Textbox(label="Enter your search query", placeholder="e.g., Gucci men’s perfume under 200AED")
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output_box = gr.JSON(label="Parsed Output")
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parse_button = gr.Button("Parse Query")
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parse_button.click(parse_query, inputs=[query_input], outputs=[output_box])
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