app.py

import streamlit as st
from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image
import requests
from io import BytesIO
import threading
import time

# Load models and processor only once using Streamlit session state
if 'models_loaded' not in st.session_state:
    st.session_state.image_processor = AutoImageProcessor.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-topwear")
    st.session_state.top_wear_model = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-topwear")
    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

# Define image processing and classification functions
def imageprocessing(image):
    encoding = st.session_state.image_processor(image.convert("RGB"), return_tensors="pt")
    return encoding

def topwear(encoding):
    outputs = st.session_state.top_wear_model(**encoding)
    predicted_class_idx = outputs.logits.argmax(-1).item()
    st.write(st.session_state.top_wear_model.config.id2label[predicted_class_idx])
    return st.session_state.top_wear_model.config.id2label[predicted_class_idx]

def patterns(encoding):
    outputs = st.session_state.pattern_model(**encoding)
    predicted_class_idx = outputs.logits.argmax(-1).item()
    return st.session_state.pattern_model.config.id2label[predicted_class_idx]

def prints(encoding):
    outputs = st.session_state.print_model(**encoding)
    predicted_class_idx = outputs.logits.argmax(-1).item()
    return st.session_state.print_model.config.id2label[predicted_class_idx]

def sleevelengths(encoding):
    outputs = st.session_state.sleeve_length_model(**encoding)
    predicted_class_idx = outputs.logits.argmax(-1).item()
    return st.session_state.sleeve_length_model.config.id2label[predicted_class_idx]

# Run all models in parallel
def pipes(image):
    encoding = imageprocessing(image)

    results = [None] * 4

    def update_results(index, func):
        results[index] = func(encoding)

    threads = [
        threading.Thread(target=update_results, args=(0, topwear)),
        threading.Thread(target=update_results, args=(1, patterns)),
        threading.Thread(target=update_results, args=(2, prints)),
        threading.Thread(target=update_results, args=(3, sleevelengths)),
    ]

    for thread in threads:
        thread.start()
    for thread in threads:
        thread.join()

    result_dict = {
        "topwear": results[0],
        "pattern": results[1],
        "print": results[2],
        "sleeve_length": results[3]
    }

    return result_dict

# Streamlit app UI
st.title("Clothing Classification Pipeline")

url = st.text_input("Paste image URL here...")
if url:
    response = requests.get(url)
    if response.status_code == 200:
        image = Image.open(BytesIO(response.content))
        st.image(image.resize((200, 200)), caption="Uploaded Image", use_column_width=False)
        
        start_time = time.time()

        try:
            result = pipes(image)
            st.write("Classification Results (JSON):")
            st.json(result)  # Display results in JSON format
            st.write(f"Time taken: {time.time() - start_time:.2f} seconds")
        except Exception as e:
            st.error(f"Error processing the image: {str(e)}")
    else:
        st.error("Failed to load image from URL. Please check the URL.")