app.py

import torch
from transformers import ViTForImageClassification, ViTFeatureExtractor, ViTConfig
import gradio as gr
from PIL import Image
import os
import logging

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Define the class labels in the correct order as used during training
labels = ['Leggings', 'Jogger', 'Palazzo', 'Cargo', 'Dresspants', 'Chinos']
logging.info(f"Labels: {labels}")

# Define the path to the uploaded model file
model_path = "best_fine_tuned_vit_Leggings_Jogger_Palazzo_Cargo_Dresspants_Chinos_93.90243902439025_2024-08-26.pth"
logging.info(f"Looking for model file: {model_path}")

if os.path.exists(model_path):
    logging.info(f"Model file found: {model_path}")
else:
    logging.error(f"Model file not found: {model_path}")
    raise FileNotFoundError(f"Model file not found: {model_path}")

# Create label mappings consistent with training
id2label = {str(i): label for i, label in enumerate(labels)}
label2id = {label: str(i) for i, label in enumerate(labels)}

# Create a configuration for the model
config = ViTConfig.from_pretrained("google/vit-base-patch16-224-in21k")
config.num_labels = len(labels)
config.id2label = id2label
config.label2id = label2id

# Initialize the model with the configuration
model = ViTForImageClassification(config)

try:
    # Load the state dict of the fine-tuned model
    state_dict = torch.load(model_path, map_location=torch.device('cpu'))
    model.load_state_dict(state_dict)
    logging.info("Fine-tuned model loaded successfully")
except Exception as e:
    logging.error(f"Error loading model: {str(e)}")
    raise

model.eval()
logging.info("Model set to evaluation mode")

# Load feature extractor
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
logging.info("Feature extractor loaded")

# 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="Pants Shape Classifier"
)

# Launch the app
if __name__ == "__main__":
    logging.info("Launching the app")
    gradio_app.launch()