app.py

import gradio as gr
from PIL import Image
from transformers import ViTImageProcessor, ViTForImageClassification
import torch

# Load the image processor and model
processor = ViTImageProcessor.from_pretrained('wambugu1738/crop_leaf_diseases_vit')
model = ViTForImageClassification.from_pretrained(
    'wambugu1738/crop_leaf_diseases_vit',
    ignore_mismatched_sizes=True
)

# Define a function to make predictions
def classify_image(image):
    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_idx = logits.argmax(-1).item()
    return model.config.id2label[predicted_class_idx]

# Create the Gradio interface
app = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="numpy"),  # Corrected input type
    outputs="text"
)
# Launch the Gradio app with a public link
app.launch(share=True)