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import os, glob
from flask import Flask, request, render_template
from transformers import pipeline
from ultralytics import YOLO
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'static/uploads'
app.config['MARKED_FOLDER'] = 'static/marked'
PREDICT_FOLDER = os.path.join(app.config['MARKED_FOLDER'], 'predict')
# สร้างโฟลเดอร์หากยังไม่มี
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
os.makedirs(PREDICT_FOLDER, exist_ok=True)
# โหลดโมเดล
pipe = pipeline("image-classification", model="JorgeGIT/Finetuned-Leukemia-cell")
yolo_model = YOLO("runs/detect/leukemia-yolo-model/weights/best.pt")
# 🎯 ฟังก์ชันรวมความเสี่ยง
def calculate_combined_risk(blast_risk, huggingface_score, age, sex):
score = 0.6 * blast_risk + 0.4 * huggingface_score
if age and int(age) > 60:
score += 5
if sex == 'male':
score += 2
return min(round(score, 1), 100)
@app.route('/', methods=['GET'])
def home():
return render_template('index.html')
@app.route('/upload', methods=['GET', 'POST'])
def upload_file():
if request.method == 'POST':
file = request.files.get('file')
age = request.form.get('age')
sex = request.form.get('sex')
if not file or file.filename == '' or not age or not sex:
return "กรุณากรอกข้อมูลให้ครบ"
filename = file.filename
filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
file.save(filepath)
results = yolo_model.predict(
source=filepath,
save=True,
project=app.config['MARKED_FOLDER'],
name='predict',
exist_ok=True
)
list_of_files = glob.glob(os.path.join(PREDICT_FOLDER, '*.jpg'))
latest_file = max(list_of_files, key=os.path.getctime)
marked_filename = os.path.relpath(latest_file, 'static').replace('\\', '/')
total_cells = len(results[0].boxes)
abnormal_cells = 0
for box in results[0].boxes:
cls_id = int(box.cls[0].item())
class_name = results[0].names[cls_id]
if 'blast' in class_name.lower():
abnormal_cells += 1
blast_risk = round((abnormal_cells / total_cells) * 100, 1) if total_cells else 0.0
result = pipe(filepath)
lla_result = [
{'label': 'โอกาสการเกิดโรค Leukemia = ', 'score': round(res['score'] * 1000, 1)}
for res in result if res['label'].lower() == 'lla'
]
huggingface_score = lla_result[0]['score'] if lla_result else 0.0
combined_risk = calculate_combined_risk(blast_risk, huggingface_score, age, sex)
return render_template('result.html',
filename=filename,
marked_filename=marked_filename,
total_cells=total_cells,
abnormal_cells=abnormal_cells,
blast_risk=blast_risk,
huggingface_score=huggingface_score,
combined_risk=combined_risk,
age=age,
sex=sex
)
return render_template('upload.html')
if __name__ == '__main__':
app.run(debug=True)
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