Update app.py

app.py

@@ -1,128 +1,3 @@
-# import streamlit as st
-# from ultralytics import YOLO
-# from PIL import Image
-# from transformers import AutoModelForImageClassification, AutoImageProcessor
-# import requests
-# from io import BytesIO
-# import numpy as np
-# import cv2
-# import concurrent.futures
-# from classification import hii,pipes
-# # Categories dictionary
-# categories_dict = {
-#     "UpperBody": ["top", "t-shirt", "sweatshirt", "blouse", "sweater", "cardigan", "jacket", "vest"],
-#     "Lowerbody": ["pants", "shorts", "skirt"],
-#     "Wholebody": ["coat", "dress", "jumpsuit", "cape"],
-#     "Head": ["glasses", "hat", "headband", "head covering", "hair accessory"],
-#     "Neck": ["tie", "neckline", "collar"],
-#     "Arms and Hands": ["glove", "watch","sleeve"],
-#     "Waist": ["belt"],
-#     "Legs and Feet": ["leg warmer", "tights", "stockings", "sock", "shoe"],
-#     "Others": ["bag", "wallet", "scarf", "umbrella"],
-#     "Garment parts": ["hood", "lapel", "epaulette","pocket"],
-#     "Closures": ["buckle", "zipper"],
-#     "Decorations": ["applique", "bead", "bow", "flower", "fringe", "ribbon", "rivet", "ruffle", "sequin", "tassel"]
-# }
-
-# def find_category(subcategory):
-#     for category, subcategories in categories_dict.items():
-#         if subcategory in subcategories:
-#             return category
-#     return "Subcategory not found."
-
-# # 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")
-#     #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
-# st.title("Clothing Classification Pipeline")
-# url = st.sidebar.text_input("Paste image URL here...")
-
-# if url:
-#     try:
-#         response = requests.get(url)
-#         if response.status_code == 200:
-#             image = Image.open(BytesIO(response.content))
-#             st.sidebar.image(image.resize((200, 200)), caption="Uploaded Image", use_column_width=False)
-
-#             # Convert image to numpy array for YOLO model
-#             image_np = np.array(image)
-            
-#             # Perform inference
-#             results = st.session_state.segment_model(image_np)
-            
-#             # Create a copy of the original image to draw bounding boxes and labels
-#             output_image = image_np.copy()
-            
-#             cropped_images = []  # List to hold cropped images and their titles
-
-#             # Visualize the segmentation results
-#             for result in results:
-#                 boxes = result.boxes  # Bounding boxes
-#                 classes = result.names  # Class names of the detected objects
-                
-#                 for i, box in enumerate(boxes):
-#                     box_coords = box.xyxy[0].cpu().numpy().astype(int)
-#                     x1, y1, x2, y2 = box_coords
-                    
-#                     # Draw the bounding box on the original image
-#                     cv2.rectangle(output_image, (x1, y1), (x2, y2), color=(0, 255, 0), thickness=2)
-                    
-#                     # Get the class label and confidence score for the object
-#                     class_label = classes[box.cls[0].int().item()]
-#                     confidence = box.conf[0].item()
-                    
-#                     # Prepare the label text with class and confidence
-#                     label_text = f'{class_label}: {confidence:.2f}'
-                    
-#                     # Put text label on the original image
-#                     cv2.putText(output_image, label_text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-                    
-#                     # Crop the image based on the bounding box
-#                     cropped_images = image_np[y1:y2, x1:x2].copy()
-#                     cropped_image = cv2.resize(cropped_images, (200, 200))  # Resize cropped image
-#                     category_name = find_category(class_label) #categories name is here...................................
-#                     st.write(">>>>>>>>>",category_name)
-#                     st.write(pipes(cropped_images,category_name))
-#                     # Add cropped image and its title to the list
-#                     cropped_images.append((cropped_image, f'Class: {category_name}, Confidence: {confidence:.2f}'))
-            
-#             # Display the original image with bounding boxes and labels
-#             st.sidebar.image(output_image, caption="Segmented Image", channels="RGB", use_column_width=True)
-
-#             # Display cropped images row-wise
-#             num_columns = 3  # Number of columns per row
-#             num_rows = (len(cropped_images) + num_columns - 1) // num_columns  # Calculate the number of rows
-            
-#             for i in range(num_rows):
-#                 cols = st.columns(num_columns)
-#                 for j in range(num_columns):
-#                     idx = i * num_columns + j
-#                     if idx < len(cropped_images):
-#                         cropped_image, title = cropped_images[idx]
-#                         with cols[j]:
-#                             st.image(cropped_image, caption=title, use_column_width=True)
-#             st.write(hii())
-            
-#         else:
-#             st.write("URL Invalid...!")
-#     except Exception as e:
-#         st.write(f"An error occurred: {e}")
-
 import streamlit as st
 from ultralytics import YOLO
 from PIL import Image
@@ -187,7 +62,7 @@ if url:
 
             # Convert image to numpy array for YOLO model
             image_np = np.array(image)
-
+            outputs={}
             # Perform inference
             results = st.session_state.segment_model(image_np)
 
@@ -223,8 +98,9 @@ if url:
                     cropped_image_resized = cv2.resize(cropped_image, (200, 200))  # Resize cropped image
                     category_name = find_category(class_label)
 
-                    st.write(f"Detected category: {category_name}")
-                    st.write(pipes(cropped_image, category_name))
+                    # st.write(f"Detected category: {category_name}")
+                    outputs[category_name]=pipes(cropped_image, category_name)
+                    # st.write(pipes(cropped_image, category_name))
 
                     # Add cropped image and its title to the list
                     cropped_images_list.append((cropped_image_resized, f'Class: {category_name}, Confidence: {confidence:.2f}'))
@@ -246,6 +122,7 @@ if url:
                             st.image(cropped_image, caption=title, use_column_width=True)
 
             st.write(hii())
+            st.json(outputs)
 
         else:
             st.write("URL Invalid...!")