Update app.py

app.py

@@ -31,8 +31,20 @@ def find_category(subcategory):
 
 # Load models and processor only once using Streamlit session state
 if 'models_loaded' not in st.session_state:
+    #localization model
     st.session_state.segment_model = YOLO("best.pt")
-    st.write("Model loaded!")
+    #image preprocessor 
+    st.session_state.image_processor = AutoImageProcessor.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-topwear")
+    #top wear
+    st.session_state.top_wear_model = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-topwear")
+    #bootomwear
+    st.session_state.bottomwear_model = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-bottomwear")
+    #fullwear
+    st.session_state.fullwear = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-fullwear")
+    #for fullwear and top wear
+    st.session_state.pattern_model = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-pattern-rgb")
+    st.session_state.print_model = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-print")
+    st.session_state.sleeve_length_model = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-sleeve-length")
     st.session_state.models_loaded = True
 
 # Streamlit app UI
@@ -82,7 +94,7 @@ if url:
                     # Crop the image based on the bounding box
                     cropped_image = image_np[y1:y2, x1:x2].copy()
                     cropped_image = cv2.resize(cropped_image, (200, 200))  # Resize cropped image
-                    category_name = find_category(class_label)
+                    category_name = find_category(class_label) #categories name is here...................................
                     
                     # Add cropped image and its title to the list
                     cropped_images.append((cropped_image, f'Class: {category_name}, Confidence: {confidence:.2f}'))