app.py

import os
from flask import Flask, redirect, render_template, request
from PIL import Image
import torchvision.transforms.functional as TF
import CNN
import numpy as np
import torch
import pandas as pd

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



model = CNN.CNN(39)    
model.load_state_dict(torch.load("plant_disease_model_1_latest.pt"))
model.eval()

def prediction(image_path):
    image = Image.open(image_path)
    image = image.resize((224, 224))
    input_data = TF.to_tensor(image)
    input_data = input_data.view((-1, 3, 224, 224))
    output = model(input_data)
    output = output.detach().numpy()
    index = np.argmax(output)
    return index


app = Flask(__name__)

@app.route('/')


@app.route('/index')
def ai_engine_page():
    return render_template('index.html')



@app.route('/submit', methods=['POST'])
def submit():
    if request.method == 'POST':
        image = request.files['image']
        filename = image.filename
        file_path = os.path.join('static/uploads', filename)
        image.save(file_path)
        print(file_path)
        pred = prediction(file_path)
        title = disease_info['disease_name'][pred]
        description = disease_info['description'][pred]
        prevent = disease_info['Possible Steps'][pred]
        image_url = disease_info['image_url'][pred]

        return render_template('index.html', filename=filename, title=title, description=description, prevent=prevent, image_url=image_url)

    # Add a default response in case the request method is not POST
    return redirect('/index')


if __name__ == "__main__":
    app.run(debug=True, host='0.0.0.0', port=5001)