app.py

from ultralytics import YOLO
import gradio as gr
from PIL import Image
import torch

# Load YOLOv5 model
model = YOLO("best.pt")  # assumes model is in the same directory

# Prediction function
def predict(image):
    results = model(image)
    boxes = results[0].boxes
    names = model.names
    output = ""

    if len(boxes) == 0:
        output = "✅ Healthy: No disease detected!"
    else:
        for box in boxes:
            class_id = int(box.cls[0])
            class_name = names[class_id]
            confidence = float(box.conf[0])
            output += f"🚨 Detected: {class_name} ({confidence*100:.2f}%)\n"

    return output

# Gradio UI
gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="🌿 AgroScan: Plant Disease Detector",
    description="Upload a plant leaf image to detect disease using a YOLOv5 model.",
).launch()