Initial commit

.gitignore

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+# Virtual environments
+venv

app.py

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+import gradio as gr
+from transformers import AutoModel, AutoProcessor
+import torch
+import requests
+from PIL import Image
+from io import BytesIO
+
+fashion_items = ['top', 'trousers', 'jumper']
+
+# Load model and processor
+model_name = 'Marqo/marqo-fashionSigLIP'
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+
+# Preprocess and normalize text data
+with torch.no_grad():
+    # Ensure truncation and padding are activated
+    processed_texts = processor(
+        text=fashion_items,
+        return_tensors="pt",
+        truncation=True,  # Ensure text is truncated to fit model input size
+        padding=True      # Pad shorter sequences so that all are the same length
+    )['input_ids']
+    
+    text_features = model.get_text_features(processed_texts)
+    text_features = text_features / text_features.norm(dim=-1, keepdim=True)
+
+# Prediction function
+def predict_from_url(url):
+    # Check if the URL is empty
+    if not url:
+        return {"Error": "Please input a URL"}
+    
+    try:
+        image = Image.open(BytesIO(requests.get(url).content))
+    except Exception as e:
+        return {"Error": f"Failed to load image: {str(e)}"}
+    
+    processed_image = processor(images=image, return_tensors="pt")['pixel_values']
+    
+    with torch.no_grad():
+        image_features = model.get_image_features(processed_image)
+        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
+        text_probs = (100 * image_features @ text_features.T).softmax(dim=-1)
+        
+    return {fashion_items[i]: float(text_probs[0, i]) for i in range(len(fashion_items))}
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict_from_url, 
+    inputs=gr.Textbox(label="Enter Image URL"),
+    outputs=gr.Label(label="Classification Results"), 
+    title="Fashion Item Classifier",
+    allow_flagging="never"
+)
+
+# Launch the interface
+demo.launch()

requirements.txt

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+transformers
+torch
+requests
+Pillow
+open_clip_torch
+ftfy
+
+# This is only needed for local deployment
+gradio