app.py

import gradio as gr
import torch
from PIL import Image
import torchvision.transforms.functional as TF
import CNN
import numpy as np
import pandas as pd

# Load model and data
model = CNN.CNN(39)
model.load_state_dict(torch.load("plant_disease_model_1_latest.pt", map_location='cpu'))
model.eval()

disease_info = pd.read_csv('disease_info.csv', encoding='cp1252')
supplement_info = pd.read_csv('supplement_info.csv', encoding='cp1252')

# Prediction logic
def predict(image):
    image = image.resize((224, 224))
    input_data = TF.to_tensor(image).view((-1, 3, 224, 224))
    output = model(input_data)
    output = output.detach().numpy()
    index = np.argmax(output)

    # Fetch prediction details
    result = {
        "Disease Name": disease_info['disease_name'][index],
        "Description": disease_info['description'][index],
        "Prevention": disease_info['Possible Steps'][index],
        "Supplement": supplement_info['supplement name'][index],
        "Buy Link": supplement_info['buy link'][index],
        "Disease Image": disease_info['image_url'][index],
        "Supplement Image": supplement_info['supplement image'][index]
    }
    return result["Disease Name"], result["Description"], result["Prevention"], result["Supplement"], result["Buy Link"], result["Disease Image"], result["Supplement Image"]

# Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload Plant Leaf"),
    outputs=[
        gr.Text(label="Disease Name"),
        gr.Textbox(label="Description"),
        gr.Textbox(label="Prevention Steps"),
        gr.Text(label="Recommended Supplement"),
        gr.Textbox(label="Buy Link"),
        gr.Image(label="Disease Image"),
        gr.Image(label="Supplement Image")
    ],
    title="Plant Disease Diagnosis",
    description="Upload an image of the plant leaf to detect disease and get treatment info.",
    css="static/styles.css"
)

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