app.py

import gradio as gr
from transformers import pipeline, AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

# Load the pre-trained model and image processor
model_name = "Diginsa/Plant-Disease-Detection-Project"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

# Create the prediction pipeline
pipe = pipeline("image-classification", model=model, image_processor=processor)

def predict_disease(image):
    """Predicts the plant disease based on the input image."""
    predictions = pipe(image)
    
    # Format the predictions for display
    results = []
    for pred in predictions:
        results.append(f"{pred['label']}: {pred['score']:.4f}")
    
    return "\n".join(results)  # Return predictions as a single string

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_disease,
    inputs=gr.Image(type="pil"),  # Input is a PIL Image
    outputs="text",  # Output is a text string with predictions
    title="Plant Disease Detection",
    description="Upload an image of a plant to detect potential diseases.",
)

# Launch the Gradio interface
iface.launch()