Update app.py

app.py

@@ -8,14 +8,14 @@ model_name = "patrickjohncyh/fashion-clip"
 model = CLIPModel.from_pretrained(model_name)
 processor = CLIPProcessor.from_pretrained(model_name)
 
-# Price extraction regex
+# Regex for price extraction
 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):
+def get_text_embedding(text_list):
     """
-    Converts input text into an embedding using FashionCLIP.
+    Converts a list of input texts into embeddings using FashionCLIP.
     """
-    inputs = processor(text=[text], images=None, return_tensors="pt", padding=True)
+    inputs = processor(text=text_list, return_tensors="pt", padding=True)  # Corrected input format
     with torch.no_grad():
         text_embedding = model.get_text_features(**inputs)
     return text_embedding
@@ -27,18 +27,19 @@ def extract_attributes(query):
     structured_output = {"Brand": "Unknown", "Category": "Unknown", "Gender": "Unknown", "Price": "Unknown"}
 
     # Get embedding for the query
-    query_embedding = get_text_embedding(query)
+    query_embedding = get_text_embedding([query])
 
-    # Compare with embeddings of common fashion attribute words (using FashionCLIP)
+    # Reference labels for classification
     reference_labels = ["Brand", "Category", "Gender", "Price"]
     reference_embeddings = get_text_embedding(reference_labels)
 
-    # Compute cosine similarity to classify the type of query
+    # Compute cosine similarity
     similarities = torch.nn.functional.cosine_similarity(query_embedding, reference_embeddings)
     best_match_index = similarities.argmax().item()
 
-    # Assign type dynamically
+    # Assign attribute dynamically
     attribute_type = reference_labels[best_match_index]
+    structured_output[attribute_type] = query  # Assigns the query text to the detected attribute
 
     # Extract price dynamically
     price_match = price_pattern.search(query)
@@ -46,9 +47,6 @@ def extract_attributes(query):
         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