app.py

from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from PIL import Image
import gradio as gr

# Load the pretrained model and feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224")

# Define the function to classify images
def classify_image(image):
    image = Image.fromarray(image).convert("RGB")  # Convert input image to RGB
    inputs = feature_extractor(images=image, return_tensors="pt")  # Preprocess image
    outputs = model(**inputs)  # Get model predictions
    predicted_class_idx = outputs.logits.argmax(-1).item()  # Get predicted class index
    return model.config.id2label[predicted_class_idx]  # Return class label

# Create a Gradio app interface
app = gr.Interface(
    fn=classify_image,  # Function to run
    inputs=gr.Image(type="numpy"),  # Input: Image
    outputs="text",  # Output: Predicted class label
    title="Image Classification App"  # App title
)

# Launch the app
app.launch()