app.py

import os
import gradio as gr
from inference.inference import load_disease_pipeline, diagnose

"""
Step 5: Gradio demo for disease-only model with example images
"""
# load your published model or local checkpoint
pipe = load_disease_pipeline("linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification")

# Path to examples folder
examples = [
    ["Plants/Unhealthy_crop_1.jpg"],
    ["Plants/Unhealthy_crop_2.jpg"],
    ["Plants/Unhealthy_crop_3.jpg"],
    ["Plants/Unhealthy_crop_4.jpg"],
    ["Plants/Unhealthy_crop_5.jpg"],
    ["Plants/Healthy_crop_1.jpg"],
    ["Plants/Healthy_crop_2.jpg"]
]

iface = gr.Interface(
    fn=lambda img: diagnose(img, pipe),
    inputs=gr.Image(type="pil", label="Upload Leaf Image"),
    outputs=[
        gr.Textbox(label="Disease Predictions (Top 3)"),
        gr.Textbox(label="Care Advice")
    ],
    title="Plant Disease Monitor",
    description="Upload a crop leaf photo to detect diseases using a fine-tuned model.",
    examples=examples,
    allow_flagging="never"
)

if __name__ == "__main__":
    iface.launch()