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from flask import Flask, request, jsonify
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
import os
# Initialize Flask application
app = Flask(__name__)
# Load your trained model
model = load_model('./butterfly_classifier.h5')
# Define a function to preprocess an image
def preprocess_image(image_path):
img = image.load_img(image_path, target_size=(224, 224))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
return img_array
# Define a route to predict butterfly species
@app.route('/predict', methods=['POST'])
def predict():
if 'file' not in request.files:
return jsonify({'error': 'No file part'})
file = request.files['file']
# Save the uploaded file to a temporary location
file_path = 'temp.jpg'
file.save(file_path)
# Preprocess the image
processed_image = preprocess_image(file_path)
# Make prediction
prediction = model.predict(processed_image)
# Get predicted label (assuming your classes are encoded as integers)
predicted_class = np.argmax(prediction, axis=1)
# Clean up: remove the temporary file
os.remove(file_path)
# Return the result as JSON
return jsonify({'predicted_class': predicted_class.item()})
# Define a welcome route
@app.route('/')
def welcome():
return 'Welcome to Butterfly Classification API'
# Run the application
if __name__ == '__main__':
app.run(debug=True)
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