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

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

def imageprocessing(image):
    encoding = st.session_state.image_processor(image.convert("RGB"), return_tensors="pt")
    return encoding

# Define the function that will be used in each thread
def call_model(func, encoding, results, index):
    results[index] = func(encoding)

# Run all models in parallel
def pipes(image):
    # Process the image once and reuse the encoding
    encoding = imageprocessing(image)

    # Prepare a list to store the results from each thread
    results = [None] * 4

    # Create threads for each function call
    threads = [
        threading.Thread(target=call_model, args=(topwear, encoding, results, 0)),
        threading.Thread(target=call_model, args=(patterns, encoding, results, 1)),
        threading.Thread(target=call_model, args=(prints, encoding, results, 2)),
        threading.Thread(target=call_model, args=(sleevelengths, encoding, results, 3)),
    ]

    # Start all threads
    for thread in threads:
        thread.start()

    # Wait for all threads to finish
    for thread in threads:
        thread.join()

    # Combine the results into a dictionary
    dicts = {
        "top": results[0],
        "pattern": results[1],
        "print": results[2],
        "sleeve_length": results[3]
    }

    return dicts

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

url = st.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.image(image.resize((200, 200)), caption="Uploaded Image", use_column_width=False)
            
            start_time = time.time()

            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")
        else:
            st.error("Failed to load image from URL. Please check the URL.")
    except Exception as e:
        st.error(f"Error processing the image: {str(e)}")