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app.py
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app.py
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# app.py
from flask import Flask, render_template, request, jsonify
from flask_cors import CORS
import cv2
import numpy as np
import urllib.request
import base64
import json
from cellsize import classify_cell_size
from nucleussize import process_nucleus_image
class NumpyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray):
return obj.tolist()
return json.JSONEncoder.default(self, obj)
app = Flask(__name__)
CORS(app)
@app.route("/")
def index():
return render_template("index.html")
@app.route('/api/cell_size', methods=['POST'])
def cell_size_route():
try:
image_file = request.files.get('image')
if image_file is None:
return jsonify({'error': 'No image file provided'})
dataset_path = './dataset' # Provide the actual dataset path
# Read image file
image_bytes = image_file.read()
# Call the classification function with the dataset path
result = classify_cell_size(image_bytes, dataset_path)
return jsonify(result)
except Exception as e:
return jsonify({'error': str(e)})
@app.route('/api/nucleus_size', methods=['POST'])
def nucleus_size():
try:
image_file = request.files.get('image')
if image_file is None:
return jsonify({'error': 'No image file provided'})
# Read image file
image_bytes = image_file.read()
# Call the nucleus size processing function
result = process_nucleus_image(image_bytes)
return jsonify(result)
except Exception as e:
return jsonify({'error': str(e)})
if __name__ == "__main__":
app.run(debug=True)