Create app.py

app.py

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+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 session state
+if 'models_loaded' not in st.session_state:
+    # Image processor
+    st.session_state.image_processor = AutoImageProcessor.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-topwear")
+    # Topwear model
+    st.session_state.top_wear_model = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-topwear")
+    # Pattern model
+    st.session_state.pattern_model = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-pattern-rgb")
+    # Print model
+    st.session_state.print_model = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-print")
+    # Sleeve length model
+    st.session_state.sleeve_length_model = AutoModelForImageClassification.from_pretrained("vishalkatheriya18/convnextv2-tiny-1k-224-finetuned-sleeve-length")
+    st.session_state.models_loaded = True
+
+# Functions for predictions
+def topwear(encoding):
+    outputs = st.session_state.top_wear_model(**encoding)
+    predicted_class_idx = outputs.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)
+    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]
+
+def imageprocessing(url):
+    response = requests.get(url)
+    image = Image.open(BytesIO(response.content))
+    encoding = st.session_state.image_processor(image.convert("RGB"), return_tensors="pt")
+    return encoding, image
+
+def pipes(imagepath):
+    encoding, image = imageprocessing(imagepath)
+    # Using threading for faster results
+    results = [None] * 4
+    threads = [
+        threading.Thread(target=lambda: results.__setitem__(0, topwear(encoding))),
+        threading.Thread(target=lambda: results.__setitem__(1, patterns(encoding))),
+        threading.Thread(target=lambda: results.__setitem__(2, prints(encoding))),
+        threading.Thread(target=lambda: results.__setitem__(3, sleevelengths(encoding))),
+    ]
+    for thread in threads:
+        thread.start()
+    for thread in threads:
+        thread.join()
+
+    dicts = {"top": results[0], "pattern": results[1], "print": results[2], "sleeve_length": results[3]}
+    return dicts, image
+
+# Streamlit app UI
+st.title("Clothing Classification Pipeline")
+
+image_url = st.text_input("Enter Image URL")
+
+if image_url:
+    start_time = time.time()
+    results, img = pipes(image_url)
+    st.image(img.resize((200, 200)), caption="Uploaded Image", use_column_width=False)
+    
+    # Display results
+    st.write("Classification Results:")
+    st.write(f"Topwear: {results['top']}")
+    st.write(f"Pattern: {results['pattern']}")
+    st.write(f"Print: {results['print']}")
+    st.write(f"Sleeve Length: {results['sleeve_length']}")
+    
+    st.write(f"Time taken: {time.time() - start_time:.2f} seconds")