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app.py
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71 lines (52 loc) · 2.1 KB
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import os
from scripts import image_segmentation, cnn_prediction
from flask import Flask, request, jsonify, send_from_directory
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
@app.route('/upload', methods=['POST'])
def upload_file():
if 'file' not in request.files:
return jsonify({'error': 'No file part'})
file = request.files['file']
if file.filename == '':
response = jsonify({'error': 'No selected file'})
return response
# Clear the directory
for f in os.listdir('data/uploads'):
os.remove(os.path.join('data/uploads', f))
# Save the uploaded file
file_path = 'data/uploads/' + file.filename
file.save(file_path)
response = jsonify({'message': 'File uploaded successfully!'})
response.headers.add('Access-Control-Allow-Origin', '*')
return response
@app.route('/process', methods=['GET'])
def process_image():
try:
image_segmentation.segment_upload()
finally:
# Return the number of segments
num_segments = len(os.listdir('data/equation_segmented'))
response = jsonify({'message': 'Image processed successfully!', 'num_segments': num_segments})
response.headers.add('Access-Control-Allow-Origin', '*')
return response
@app.route('/download', methods=['GET'])
def download_image():
image_option = request.args.get('image')
match image_option:
case 'processed':
return send_from_directory('data/equation_processed', 'processed.png')
case 'contours':
return send_from_directory('data/equation_processed', 'contours.png')
case 'segmented':
segment_number = request.args.get('n')
return send_from_directory('data/equation_segmented', f'segmented_{segment_number}.png')
@app.route('/predict', methods=['GET'])
def predict():
latex_code, py_function_code = cnn_prediction.predict()
response = jsonify({'latex': latex_code, 'python': py_function_code})
response.headers.add('Access-Control-Allow-Origin', '*')
return response
if __name__ == '__main__':
app.run(host='0.0.0.0', port=8080)