Update app.py

app.py

@@ -1,45 +1,71 @@
 import gradio as gr
 import torch
 from transformers import CLIPProcessor, CLIPModel
+import re
 
-# Load the FashionCLIP model
+# Load FashionCLIP model
 model_name = "patrickjohncyh/fashion-clip"
 model = CLIPModel.from_pretrained(model_name)
 processor = CLIPProcessor.from_pretrained(model_name)
 
-def parse_query(user_query):
+# Price extraction regex
+price_pattern = re.compile(r'(\bunder\b|\babove\b|\bbelow\b|\bbetween\b)?\s?(\d{1,5})\s?(AED|USD|EUR)?', re.IGNORECASE)
+
+def get_text_embedding(text):
+    """
+    Converts input text into an embedding using FashionCLIP.
+    """
+    inputs = processor(text=[text], images=None, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        text_embedding = model.get_text_features(**inputs)
+    return text_embedding
+
+def extract_attributes(query):
     """
-    Parse fashion-related search queries into structured data.
+    Extract structured fashion attributes dynamically using FashionCLIP.
     """
-    # Define categories relevant to luxury fashion search
-    fashion_categories = ["Brand", "Category", "Gender", "Price Range"]
+    structured_output = {"Brand": "Unknown", "Category": "Unknown", "Gender": "Unknown", "Price": "Unknown"}
 
-    # Format user query for CLIP
-    inputs = processor(text=[user_query], images=None, return_tensors="pt", padding=True)
+    # Get embedding for the query
+    query_embedding = get_text_embedding(query)
 
-    # Get model embeddings
-    with torch.no_grad():
-        outputs = model.get_text_features(**inputs)
+    # Compare with embeddings of common fashion attribute words (using FashionCLIP)
+    reference_labels = ["Brand", "Category", "Gender", "Price"]
+    reference_embeddings = get_text_embedding(reference_labels)
+
+    # Compute cosine similarity to classify the type of query
+    similarities = torch.nn.functional.cosine_similarity(query_embedding, reference_embeddings)
+    best_match_index = similarities.argmax().item()
 
-    # Simulated parsing output (FashionCLIP itself does not generate structured JSON)
-    parsed_output = {
-        "Brand": "Gucci" if "Gucci" in user_query else "Unknown",
-        "Category": "Perfume" if "perfume" in user_query else "Unknown",
-        "Gender": "Men" if "men" in user_query else "Women" if "women" in user_query else "Unisex",
-        "Price Range": "Under 200 AED" if "under 200" in user_query else "Above 200 AED",
-    }
+    # Assign type dynamically
+    attribute_type = reference_labels[best_match_index]
 
-    return parsed_output
+    # Extract price dynamically
+    price_match = price_pattern.search(query)
+    if price_match:
+        condition, amount, currency = price_match.groups()
+        structured_output["Price"] = f"{condition.capitalize() if condition else ''} {amount} {currency if currency else 'AED'}".strip()
+
+    # Extract brand & category dynamically using FashionCLIP similarity
+    structured_output[attribute_type] = query  # Assigning full query text to matched attribute
+
+    return structured_output
 
 # Define Gradio UI
+def parse_query(user_query):
+    """
+    Takes user query and returns structured attributes dynamically.
+    """
+    parsed_output = extract_attributes(user_query)
+    return parsed_output  # Returns structured JSON
+
 with gr.Blocks() as demo:
-    gr.Markdown("# 🛍️ Luxury Fashion Query Parser (FashionCLIP)")
-    
+    gr.Markdown("# 🛍️ Fashion Query Parser using FashionCLIP")
+
     query_input = gr.Textbox(label="Enter your search query", placeholder="e.g., Gucci men’s perfume under 200AED")
     output_box = gr.JSON(label="Parsed Output")
 
     parse_button = gr.Button("Parse Query")
     parse_button.click(parse_query, inputs=[query_input], outputs=[output_box])
 
-# Launch the app
 demo.launch()