main.py

from flask import Flask, request, jsonify
from PIL import Image
import numpy as np
import tensorflow as tf
import keras

app = Flask(__name__)

# Load the pre-trained model
classifier_model = keras.models.load_model('tomatoes.h5', compile=False)

# Class names for the predictions
class_names = [
    'Tomato_Bacterial_spot', 'Tomato_Early_blight', 'Tomato_Late_blight',
    'Tomato_Leaf_Mold', 'Tomato_Septoria_leaf_spot',
    'Tomato_Spider_mites_Two_spotted_spider_mite', 'Tomato_Target_Spot',
    'Tomato_Tomato_YellowLeaf_Curl_Virus', 'Tomato_Tomato_mosaic_virus', 'Tomato_healthy'
]

descriptions = {
    'Tomato_Bacterial_spot': "A bacterial infection causing angular, water-soaked lesions.",
    'Tomato_Early_blight': "Early blight causes dark, concentric lesions on leaves and stems.",
    'Tomato_Late_blight': "Late blight causes dark, irregular spots on leaves, stems, and fruit.",
    'Tomato_Leaf_Mold': "Leaf mold causes yellowing of leaves with fuzzy mold growth on the underside.",
    'Tomato_Septoria_leaf_spot': "Septoria leaf spot causes small, circular, dark lesions on leaves.",
    'Tomato_Spider_mites_Two_spotted_spider_mite': "Spider mites create tiny yellow or brown spots on leaves.",
    'Tomato_Target_Spot': "Target spot causes dark, concentric circles in lesions on the leaves.",
    'Tomato_Tomato_YellowLeaf_Curl_Virus': "This virus causes leaf curling and yellowing in tomato plants.",
    'Tomato_Tomato_mosaic_virus': "Mosaic virus causes distorted, mottled leaves and stunted growth.",
    'Tomato_healthy': "The plant is healthy with no signs of disease."
}

actions = {
    'Tomato_Bacterial_spot': "Remove affected leaves and ensure proper irrigation to reduce water on foliage.",
    'Tomato_Early_blight': "Use fungicides and practice crop rotation to avoid early blight.",
    'Tomato_Late_blight': "Use resistant varieties and apply fungicides as soon as symptoms are seen.",
    'Tomato_Leaf_Mold': "Improve air circulation and remove affected leaves.",
    'Tomato_Septoria_leaf_spot': "Remove infected leaves and apply fungicides to prevent further spread.",
    'Tomato_Spider_mites_Two_spotted_spider_mite': "Use miticides and increase humidity to deter mite infestation.",
    'Tomato_Target_Spot': "Remove infected leaves and apply fungicides to control the spread.",
    'Tomato_Tomato_YellowLeaf_Curl_Virus': "Remove infected plants to prevent virus spread to healthy plants.",
    'Tomato_Tomato_mosaic_virus': "Remove and destroy infected plants immediately to prevent further spread.",
    'Tomato_healthy': "The plant is in good condition, continue with standard care."
}

# Prediction function
def predict_class(image):
    # Resize the image for the model input
    test_image = image.resize((256, 256))
    test_image = keras.preprocessing.image.img_to_array(test_image)
    test_image /= 255.0  # Normalize image
    test_image = np.expand_dims(test_image, axis=0)

    # Predict and get the confidence
    prediction = classifier_model.predict(test_image)
    confidence = round(100 * (np.max(prediction[0])), 2)
    final_pred = class_names[np.argmax(prediction)]

    # Get description and recommended actions for the predicted disease
    description = descriptions[final_pred]
    action = actions[final_pred]

    return final_pred, confidence, description, action

# Route to handle file uploads and prediction
@app.route('/', methods=['POST'])
def predict():
    if 'file' not in request.files:
        return jsonify({'error': 'No file uploaded'}), 400

    file = request.files['file']
    if file.filename == '':
        return jsonify({'error': 'No file selected'}), 400

    if file:
        try:
            # Open the image file
            image = Image.open(file)
            result, confidence, description, actions = predict_class(image)

            return jsonify({
                'result': result,
                'confidence': confidence,
                'description': description,
                'actions': actions
            })
        except Exception as e:
            return jsonify({'error': str(e)}), 500

# Run the Flask app
if __name__ == '__main__':
    app.run(debug=True)