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app.py

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+import torch
+from transformers import ViTForImageClassification, ViTFeatureExtractor, AutoConfig
+import gradio as gr
+from PIL import Image
+import os
+import logging
+from safetensors.torch import load_file  # Import safetensors loading function
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Define the directory containing the model files
+model_dir = "."  # Use current directory
+
+# Define paths to the specific model files
+model_path = os.path.join(model_dir, "model.safetensors")
+config_path = os.path.join(model_dir, "config.json")
+preprocessor_path = os.path.join(model_dir, "preprocessor_config.json")
+
+# Check if all required files exist
+for path in [model_path, config_path, preprocessor_path]:
+    if not os.path.exists(path):
+        logging.error(f"File not found: {path}")
+        raise FileNotFoundError(f"Required file not found: {path}")
+    else:
+        logging.info(f"Found file: {path}")
+
+# Load the configuration
+config = AutoConfig.from_pretrained(config_path)
+
+# Ensure the labels are consistent with the model's config
+labels = list(config.id2label.values())
+logging.info(f"Labels: {labels}")
+
+# Load the feature extractor
+feature_extractor = ViTFeatureExtractor.from_pretrained(preprocessor_path)
+
+# Load the model using the safetensors file
+state_dict = load_file(model_path)  # Use safetensors to load the model weights
+model = ViTForImageClassification.from_pretrained(
+    pretrained_model_name_or_path=None,
+    config=config,
+    state_dict=state_dict
+)
+
+# Ensure the model is in evaluation mode
+model.eval()
+logging.info("Model set to evaluation mode")
+
+# Define the prediction function
+def predict(image):
+    logging.info("Starting prediction")
+    logging.info(f"Input image shape: {image.size}")
+    
+    # Preprocess the image
+    logging.info("Preprocessing image")
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    logging.info(f"Preprocessed input shape: {inputs['pixel_values'].shape}")
+    
+    logging.info("Running inference")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.nn.functional.softmax(logits[0], dim=0)
+    
+    logging.info(f"Raw logits: {logits}")
+    logging.info(f"Probabilities: {probabilities}")
+    
+    # Prepare the output dictionary
+    result = {labels[i]: float(probabilities[i]) for i in range(len(labels))}
+    logging.info(f"Prediction result: {result}")
+    
+    return result
+
+# Set up the Gradio Interface
+logging.info("Setting up Gradio interface")
+gradio_app = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type="pil"), 
+    outputs=gr.Label(num_top_classes=6),
+    title="General SleeveLength Classifier"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    logging.info("Launching the app")
+    gradio_app.launch()

requirements.txt

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+torch
+transformers
+gradio
+pillow