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