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import os |
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import sys |
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import cv2 |
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import numpy as np |
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import gradio as gr |
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import plotly.graph_objects as go |
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from ultralytics import YOLO |
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import google.generativeai as genai |
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from PIL import Image |
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from gtts import gTTS |
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import tempfile |
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import datetime |
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import requests |
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import shutil |
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from reportlab.pdfgen import canvas |
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from reportlab.lib.pagesizes import A4 |
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from reportlab.lib.units import cm, mm |
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from reportlab.pdfbase import pdfmetrics |
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from reportlab.pdfbase.ttfonts import TTFont |
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GOOGLE_API_KEY = os.getenv("AIzaSyBYksOq03N5V2MjSYicHdsk4ESdyR9FABw") |
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if GOOGLE_API_KEY: |
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genai.configure(api_key=GOOGLE_API_KEY) |
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MODEL_PATH = "otu_multiclass_yolo11s_v2.pt" |
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LOGO_KMUTNB_URL = "https://www.mou.kmutnb.ac.th/logo_kmutnb.png" |
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LOGO_RAMA_URL ="https://www.rama.mahidol.ac.th/nursing/sites/default/files/public/Rama_Logo.png" |
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INTRO_SOUND_URL = "https://cdn.pixabay.com/download/audio/2022/03/24/audio_c8c8a73467.mp3?filename=cinematic-atmosphere-score-2-22136.mp3" |
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CLASS_NAMES = { |
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0: "Chocolate cyst", 1: "Serous cystadenoma", 2: "Teratoma", 3: "Theca cell tumor", |
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4: "Simple cyst", 5: "Normal ovary", 6: "Mucinous cystadenoma", 7: "High grade serous" |
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} |
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def force_download_font(url, filename): |
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if not os.path.exists(filename): |
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print(f"📥 Downloading {filename}...") |
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try: |
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r = requests.get(url, allow_redirects=True) |
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with open(filename, 'wb') as f: |
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f.write(r.content) |
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except Exception as e: |
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print(f"❌ Error downloading {filename}: {e}") |
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return False |
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return True |
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font_urls = [ |
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("https://github.com/nutjunkie/thaifonts_sipa/raw/master/sipa_fonts/THSarabunNew/THSarabunNew.ttf", "THSarabunNew.ttf"), |
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("https://github.com/nutjunkie/thaifonts_sipa/raw/master/sipa_fonts/THSarabunNew/THSarabunNew%20Bold.ttf", "THSarabunNew-Bold.ttf") |
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] |
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for url, fname in font_urls: |
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force_download_font(url, fname) |
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try: |
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if os.path.exists("THSarabunNew.ttf"): |
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pdfmetrics.registerFont(TTFont('THSarabun', 'THSarabunNew.ttf')) |
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if os.path.exists("THSarabunNew-Bold.ttf"): |
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pdfmetrics.registerFont(TTFont('THSarabun-Bold', 'THSarabunNew-Bold.ttf')) |
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except Exception as e: |
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print(f"⚠️ Font Registration Error: {e}") |
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def text_to_speech(text): |
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try: |
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tts = gTTS(text, lang='th') |
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f = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") |
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tts.save(f.name) |
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return f.name |
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except: return None |
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def generate_led_html(score, diagnosis): |
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color = "#ff4444" if "High grade" in diagnosis else "#00C851" if "Normal" in diagnosis else "#ffbb33" |
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return f""" |
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<div style='background-color: #2b2b2b; border: 3px solid {color}; border-radius: 12px; padding: 15px; text-align: center; box-shadow: 0 4px 8px rgba(0,0,0,0.2);'> |
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<span style='color: #e0e0e0; font-size: 14px; text-transform: uppercase;'>Primary Diagnosis</span><br> |
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<span style='color: {color}; font-size: 24px; font-weight: 800;'>{diagnosis}</span><br> |
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<hr style='border-color: #444; margin: 10px 0;'> |
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<span style='color: white; font-size: 32px; font-weight: bold;'>{score}%</span> |
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</div> |
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""" |
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def create_medical_report(pt_name, pt_id, diagnosis, conf): |
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try: |
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filename = tempfile.mktemp(suffix=".pdf") |
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c = canvas.Canvas(filename, pagesize=A4) |
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font_name = 'THSarabun-Bold' if 'THSarabun-Bold' in pdfmetrics.getRegisteredFontNames() else 'Helvetica-Bold' |
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c.setFont(font_name, 24) |
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c.drawString(2*cm, 27*cm, "Medical Image Analysis Report") |
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c.setFont(font_name, 16) |
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c.drawString(2*cm, 25*cm, f"Patient Name: {pt_name}") |
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c.drawString(2*cm, 24*cm, f"Patient ID: {pt_id}") |
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c.drawString(2*cm, 22*cm, f"Diagnosis Result: {diagnosis}") |
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c.drawString(2*cm, 21*cm, f"Confidence Score: {conf}%") |
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c.drawString(2*cm, 20*cm, f"Date: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M')}") |
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c.save() |
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return filename |
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except Exception as e: |
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print(f"PDF Error: {e}") |
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return None |
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def chat_fn(message, history, crop_img, info_text, diagnosis): |
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if history is None: history = [] |
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if not GOOGLE_API_KEY: |
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response = "❌ ไม่พบ API KEY: กรุณาไปที่ Settings > Secrets แล้วตั้งค่า 'GOOGLE_API_KEY' จากนั้นกด Restart Space" |
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history.append({"role": "user", "content": message}) |
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history.append({"role": "assistant", "content": response}) |
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return history, "" |
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try: |
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context_prompt = f""" |
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บทบาท: คุณคือผู้ช่วยทางการแพทย์อัจฉริยะ (AI Medical Assistant) |
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ข้อมูลบริบททางการแพทย์ของผู้ป่วยรายนี้: |
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- ผลการวินิจฉัยหลัก (Diagnosis): {diagnosis if diagnosis else "ยังไม่มีการวินิจฉัย"} |
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- ข้อมูลเพิ่มเติม: {info_text if info_text else "ไม่มี"} |
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คำถามจากผู้ใช้: {message} |
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คำแนะนำในการตอบ: |
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- ตอบเป็นภาษาไทย ให้กระชับ เข้าใจง่าย |
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- ถ้าเกี่ยวกับเรื่องซีสต์หรือเนื้องอก ให้ข้อมูลตามหลักการแพทย์ |
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- *สำคัญ*: ต้องลงท้ายเสมอว่า "ควรปรึกษาแพทย์ผู้เชี่ยวชาญเพื่อการวินิจฉัยที่แม่นยำที่สุด" |
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""" |
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model = genai.GenerativeModel('gemini-1.5-flash') |
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response = model.generate_content(context_prompt) |
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bot_reply = response.text |
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except Exception as e: |
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bot_reply = f"เกิดข้อผิดพลาด (System Error): {str(e)}" |
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print(f"DEBUG ERROR: {e}") |
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history.append({"role": "user", "content": message}) |
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history.append({"role": "assistant", "content": bot_reply}) |
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return history, "" |
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def analyze_image(image, history_list): |
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if history_list is None: history_list = [] |
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if image is None: |
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return [None]*15 |
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if not os.path.exists(MODEL_PATH): |
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error_msg = f"⚠️ Model file not found at {MODEL_PATH}. Please upload the .pt file." |
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return image, image, image, go.Figure(), error_msg, "", None, image, "Error", 0, history_list, history_list, image, image, image |
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history_list.append(image) |
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lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB) |
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l, a, b = cv2.split(lab) |
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clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8)) |
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cl = clahe.apply(l) |
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enhanced_img = cv2.merge((cl,a,b)) |
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enhanced_img = cv2.cvtColor(enhanced_img, cv2.COLOR_LAB2RGB) |
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try: |
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model = YOLO(MODEL_PATH) |
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results = model.predict( |
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enhanced_img, |
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imgsz=640, |
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conf=0.25, |
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iou=0.45, |
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augment=True, |
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verbose=False |
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)[0] |
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except Exception as e: |
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return image, image, image, go.Figure(), f"Inference Error: {e}", "", None, image, "Error", 0, history_list, history_list, image, image, image |
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orig = image.copy() |
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seg_overlay = image.copy() |
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crop_img = np.zeros_like(image) |
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info_log = "Analysis Results:\n" + "-"*20 + "\n" |
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max_conf = 0 |
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primary_diag = "Normal / Not Found" |
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fig = go.Figure() |
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if results.boxes and len(results.boxes) > 0: |
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boxes = results.boxes.data.cpu().numpy() |
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for i, box in enumerate(boxes): |
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x1, y1, x2, y2, conf, cls_id = box |
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cls_name = CLASS_NAMES.get(int(cls_id), "Unknown") |
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if conf > max_conf: |
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max_conf = conf |
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primary_diag = cls_name |
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crop_img = image[int(y1):int(y2), int(x1):int(x2)] |
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color = (0, 165, 255) |
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if "High grade" in cls_name: color = (255, 0, 0) |
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if "Normal" in cls_name: color = (0, 255, 0) |
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cv2.rectangle(orig, (int(x1), int(y1)), (int(x2), int(y2)), color, 3) |
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label = f"{cls_name} {conf*100:.1f}%" |
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cv2.putText(orig, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) |
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info_log += f"Found #{i+1}: {cls_name} ({conf*100:.1f}%)\n" |
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if results.masks: |
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mask_combined = np.zeros(image.shape[:2], dtype=np.float32) |
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for m_raw in results.masks.data.cpu().numpy(): |
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m_resized = cv2.resize(m_raw, (image.shape[1], image.shape[0])) |
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mask_combined = np.maximum(mask_combined, m_resized) |
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mask_bool = mask_combined > 0.5 |
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mask_uint8 = (mask_bool * 255).astype(np.uint8) |
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colored_mask = np.zeros_like(seg_overlay) |
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colored_mask[mask_bool] = (0, 255, 0) |
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seg_overlay = cv2.addWeighted(seg_overlay, 1.0, colored_mask, 0.4, 0) |
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contours, _ = cv2.findContours(mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) |
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cv2.drawContours(seg_overlay, contours, -1, (255, 255, 255), 2) |
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dist_map = cv2.distanceTransform(mask_uint8, cv2.DIST_L2, 5) |
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y_idx, x_idx = np.where(mask_bool) |
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if len(x_idx) > 0: |
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step = max(1, len(x_idx) // 1000) |
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fig.add_trace(go.Scatter3d( |
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x=x_idx[::step], y=image.shape[0]-y_idx[::step], z=dist_map[y_idx, x_idx][::step], |
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mode='markers', marker=dict(size=2, color=dist_map[y_idx, x_idx][::step], colorscale='Hot', opacity=0.8) |
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)) |
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else: |
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info_log = "ไม่พบความผิดปกติในภาพนี้ (No Lesion Detected)" |
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crop_img = image |
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fig.update_layout(scene=dict(xaxis_title='Width', yaxis_title='Height', zaxis_title='Density'), margin=dict(l=0,r=0,b=0,t=0), height=300) |
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score_percent = int(max_conf * 100) |
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led_html = generate_led_html(score_percent, primary_diag) |
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audio_path = text_to_speech(f"วิเคราะห์เสร็จสิ้น ตรวจพบ {primary_diag} ความมั่นใจ {score_percent} เปอร์เซ็นต์") |
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return orig, seg_overlay, crop_img, fig, info_log, led_html, audio_path, crop_img, primary_diag, score_percent, history_list, history_list, image, orig, seg_overlay |
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css = """ |
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@import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;700;900&display=swap'); |
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.logo-container { display: flex; justify-content: flex-end; align-items: center; gap: 20px; } |
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#intro-overlay { position: fixed; top: 0; left: 0; width: 100vw; height: 100vh; background-color: #000; z-index: 99999; display: flex; flex-direction: column; justify-content: center; align-items: center; animation: fadeOutOverlay 1s ease-in-out 4.5s forwards; pointer-events: none; } |
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.intro-content { display: flex; gap: 40px; align-items: center; animation: zoomInLogos 3s cubic-bezier(0.25, 0.46, 0.45, 0.94) forwards; } |
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.intro-logo { height: 120px; width: auto; filter: drop-shadow(0 0 10px rgba(255,255,255,0.3)); } |
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.intro-text-container { margin-top: 40px; text-align: center; opacity: 0; animation: textSlideUp 1.5s ease-out 1.2s forwards; } |
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.intro-title { color: #ffffff; font-family: 'Montserrat', sans-serif; font-size: 2.5rem; font-weight: 900; text-transform: uppercase; letter-spacing: 2px; text-shadow: 0 0 20px rgba(255, 255, 255, 0.6); line-height: 1.2; margin-bottom: 10px; } |
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.intro-subtitle { color: #b3b3b3; font-family: 'Montserrat', sans-serif; font-size: 1.2rem; font-weight: 400; letter-spacing: 4px; } |
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@keyframes zoomInLogos { 0% { transform: scale(0.8); opacity: 0; } 50% { transform: scale(1.05); opacity: 1; } 100% { transform: scale(1.0); opacity: 1; } } |
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@keyframes textSlideUp { 0% { transform: translateY(30px); opacity: 0; } 100% { transform: translateY(0); opacity: 1; } } |
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@keyframes fadeOutOverlay { to { opacity: 0; visibility: hidden; z-index: -1; } } |
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/* Floating Chatbot CSS */ |
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#floating_container { position: fixed; bottom: 25px; left: 25px; z-index: 9999; display: flex; flex-direction: column; align-items: flex-start; } |
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#chat_window { width: 380px; height: 550px; background: white; border-radius: 20px; box-shadow: 0 15px 50px rgba(0,0,0,0.25); margin-bottom: 15px; display: none; flex-direction: column; border: 1px solid #eee; overflow: hidden; } |
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.show-chat #chat_window { display: flex !important; } |
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#chat_btn { width: 80px; height: 80px; background: white; border-radius: 50%; cursor: pointer; display: flex; justify-content: center; align-items: center; box-shadow: 0 8px 30px rgba(0,0,0,0.2); transition: 0.3s; border: 2px solid #0072ff; } |
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#chat_btn:hover { transform: scale(1.1); } |
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#chat_btn img { width: 65px; height: 65px; object-fit: contain; border-radius: 50%; } |
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""" |
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with gr.Blocks(theme=gr.themes.Soft(), css=css, title="Ovarian Tumor AI") as demo: |
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gr.HTML(f"""<div id="intro-overlay"><audio autoplay><source src="{INTRO_SOUND_URL}" type="audio/mpeg"></audio><div class="intro-content"><img src="{LOGO_KMUTNB_URL}" class="intro-logo"><img src="{LOGO_RAMA_URL}" class="intro-logo"></div><div class="intro-text-container"><div class="intro-title">Deep Learning for<br>Ovarian Tumor Detection</div><div class="intro-title" style="font-size: 1.8rem; color: #E50914;">in Ultrasound Images</div><div class="intro-subtitle">AI MEDICAL DIAGNOSIS SYSTEM</div></div></div>""") |
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with gr.Row(variant="panel"): |
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with gr.Column(scale=3): |
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gr.Markdown("# 🏥 Ovarian Tumor Diagnosis System") |
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gr.Markdown("AI System for Ovarian Tumor Detection & Diagnosis") |
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gr.Markdown("จัดทำโดย นายภานรินทร์ เปียกบุตร & นางสาวภาพิมล ไพจิตโรจนา") |
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with gr.Column(scale=2): |
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with gr.Row(elem_classes="logo-container"): |
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gr.Image(LOGO_KMUTNB_URL, show_label=False, container=False, height=65) |
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gr.Image(LOGO_RAMA_URL, show_label=False, container=False, height=65) |
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state_crop = gr.State(None) |
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state_info = gr.State("") |
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state_diag = gr.State("") |
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state_conf = gr.State(0) |
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state_gallery = gr.State([]) |
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state_img_orig = gr.State(None) |
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state_img_det = gr.State(None) |
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state_img_seg = gr.State(None) |
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state_fig = gr.State(None) |
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with gr.Tabs(): |
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with gr.Tab("1. Detection Analysis"): |
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with gr.Row(): |
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with gr.Column(scale=2): |
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img_in = gr.Image(label="Upload Ultrasound Image", type="numpy", height=400) |
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btn_analyze = gr.Button("🔍 Analyze Image", variant="primary") |
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with gr.Column(scale=1): |
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html_led = gr.HTML() |
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aud = gr.Audio(label="Voice Assistant", autoplay=True) |
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txt_log = gr.Textbox(label="Detailed Findings", lines=8) |
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with gr.Row(): |
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img_det = gr.Image(label="AI Detection", interactive=False) |
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img_seg = gr.Image(label="Segmentation", interactive=False) |
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img_crop = gr.Image(label="Focused Lesion", interactive=False) |
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with gr.Tab("2. Medical Report"): |
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with gr.Row(): |
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inp_pt_name = gr.Textbox(label="Patient Name") |
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inp_pt_id = gr.Textbox(label="Patient ID (HN)") |
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btn_pdf = gr.Button("🖨️ Generate PDF Report", variant="primary") |
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out_pdf = gr.File(label="Download Report") |
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with gr.Tab("3. Gallery History"): |
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gallery_ui = gr.Gallery(columns=4, height=600) |
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with gr.Column(elem_id="floating_container"): |
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with gr.Column(elem_id="chat_window"): |
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gr.HTML(f"<div style='background:linear-gradient(90deg, #0072ff, #00c6ff); color:white; padding:15px; border-radius:15px 15px 0 0;'><b>💬 ปรึกษาน้องดูแล</b></div>") |
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chatbot = gr.Chatbot(height=400, show_label=False, avatar_images=(None, LOGO_RAMA_URL), type="messages") |
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msg = gr.Textbox(placeholder="พิมพ์คำถามที่นี่...", show_label=False) |
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btn_send = gr.Button("ส่งข้อความ", variant="primary") |
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|
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gr.HTML(f""" |
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<div id="chat_btn" onclick="document.getElementById('floating_container').classList.toggle('show-chat')"> |
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<img src="{LOGO_RAMA_URL}" /> |
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</div> |
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""") |
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btn_analyze.click( |
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analyze_image, |
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[img_in, state_gallery], |
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[img_det, img_seg, img_crop, state_fig, txt_log, html_led, aud, state_crop, state_diag, state_conf, gallery_ui, state_gallery, state_img_orig, state_img_det, state_img_seg] |
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) |
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def pdf_wrapper(name, pid, diag, conf): |
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if not diag: return None |
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return create_medical_report(name, pid, diag, conf) |
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btn_pdf.click(pdf_wrapper, [inp_pt_name, inp_pt_id, state_diag, state_conf], out_pdf) |
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btn_send.click(chat_fn, [msg, chatbot, state_crop, state_info, state_diag], [chatbot, msg]) |
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msg.submit(chat_fn, [msg, chatbot, state_crop, state_info, state_diag], [chatbot, msg]) |
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|
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if __name__ == "__main__": |
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demo.launch() |
