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from flask import Flask, request, jsonify
import tensorflow as tf
import numpy as np
from PIL import Image
import io
import json
app = Flask(__name__)
# Load the TensorFlow model
model = tf.keras.models.load_model('./plant_disease_detection_saved_model')
# Load categories
with open('./categories.json') as f:
categories = json.load(f)
def preprocess_image(image):
# Convert the image to a NumPy array
image = Image.open(io.BytesIO(image))
image = image.resize((224, 224)) # Adjust size as needed
image_array = np.array(image) / 255.0 # Normalize to [0, 1]
image_array = np.expand_dims(image_array, axis=0) # Add batch dimension
return image_array
@app.route('/predict', methods=['POST'])
def predict():
if 'image' not in request.files:
return jsonify({'error': 'No image provided'}), 400
image = request.files['image'].read()
image_array = preprocess_image(image)
# Make prediction
predictions = model.predict(image_array)
predicted_class = np.argmax(predictions, axis=1)[0]
# Map to category names
predicted_label = categories.get(str(predicted_class), 'Unknown')
return jsonify({'class': predicted_label, 'confidence': float(predictions[0][predicted_class])})
if __name__ == '__main__':
app.run(host='0.0.0.0', port=8080, debug=True)
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