app.py

import gradio as gr
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification
from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomHorizontalFlip, RandomRotation
from PIL import Image
from datasets import load_dataset
import traceback

# Load dataset to get labels
dataset = load_dataset("bentrevett/caltech-ucsd-birds-200-2011")
labels = dataset['train'].features['label'].names

# Load model and processor
model_name = "riyadifirman/klasifikasiburung_new"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

# Define image transformations
normalize = Normalize(mean=processor.image_mean, std=processor.image_std)
transform = Compose([
    Resize((224, 224)),
    RandomHorizontalFlip(),
    RandomRotation(10),
    ToTensor(),
    normalize,
])

def predict(image):
    try:
        image = Image.fromarray(image)
        inputs = transform(image).unsqueeze(0)
        outputs = model(inputs)
        logits = outputs.logits
        predicted_class_idx = logits.argmax(-1).item()
        predicted_class = labels[predicted_class_idx]
        return predicted_class
    except Exception as e:
        # Menampilkan error
        print("An error occurred:", e)
        print(traceback.format_exc())  # Ini akan print error secara detail
        return "An error occurred while processing your request."

# Create Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="numpy"),
    outputs="text",
    title="Bird Classification",
    description="Upload an image of a bird to classify it."
)

if __name__ == "__main__":
    interface.launch(share=True)