app.py

import streamlit as st
from ultralytics import YOLO
from PIL import Image
import requests
from io import BytesIO
import numpy as np
import cv2
import concurrent.futures
from classification import hii
# 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"],
    "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", "collar", "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:
    st.session_state.segment_model = YOLO("best.pt")
    st.write("Model loaded!")
    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_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)
                    
                    # 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}")