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app.py

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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)

detector_yolo.py

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+# detector_yolo.py
+import os, glob
+from ultralytics import YOLO
+
+# โหลดโมเดล YOLO ที่เทรนเอง
+yolo_model = YOLO("runs/detect/leukemia-yolo-model/weights/best.pt")
+
+def detect_cells_yolo(image_path, output_dir='static/marked/predict'):
+    # สร้างโฟลเดอร์ถ้ายังไม่มี
+    os.makedirs(output_dir, exist_ok=True)
+
+    # ตรวจจับและบันทึกภาพ
+    yolo_model.predict(source=image_path, save=True, project='static/marked', name='predict', exist_ok=True)
+
+    # หาภาพล่าสุดที่เพิ่งสร้าง
+    list_of_files = glob.glob(os.path.join(output_dir, '*.jpg'))
+    if not list_of_files:
+        return None
+    latest_file = max(list_of_files, key=os.path.getctime)
+    return os.path.relpath(latest_file, 'static')

train_yolo.py

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+from ultralytics import YOLO
+
+# โหลดโมเดล YOLOv8 เปล่า
+model = YOLO("yolov8n.pt")  # หรือ yolov8s.pt ถ้าเครื่องแรงขึ้น
+
+# เทรนโมเดลใหม่ด้วย dataset ที่โหลดมา
+model.train(
+    data="leukemia-detection/data.yaml",  # path ไปยัง dataset.yaml
+    epochs=50,
+    imgsz=640,
+    batch=8,
+    name="leukemia-yolo-model",
+)

yolov8n.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36
+size 6549796