Update app.py

app.py

@@ -24,13 +24,15 @@ from reportlab.pdfbase.ttfonts import TTFont
 # 1) Configuration & Setup
 # ============================================
 
-# 🔑 API KEY: แนะนำให้ตั้งใน Settings > Secrets ของ HF Space ชื่อ "GOOGLE_API_KEY"
-# ถ้าไม่มีจะใช้ค่าว่าง (Chat จะไม่ทำงานสมบูรณ์)
+# 🔑 API KEY
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY", "") 
 if GOOGLE_API_KEY:
-    genai.configure(api_key=GOOGLE_API_KEY)
+    try:
+        genai.configure(api_key=GOOGLE_API_KEY)
+    except:
+        print("⚠️ Failed to configure Google AI")
 
-# 📂 Model Path: อัปโหลดไฟล์ .pt ไปที่ Files ใน HF Space โดยตรง
+# 📂 Model Path
 MODEL_PATH = "otu_multiclass_yolo11s_v2.pt"
 
 LOGO_KMUTNB_URL = "https://www.mou.kmutnb.ac.th/logo_kmutnb.png"
@@ -43,20 +45,28 @@ CLASS_NAMES = {
 }
 
 # ---------------------------------------------------------
-# 🛠️ AUTO-DOWNLOAD FONTS
+# 🛠️ ROBUST FONT DOWNLOADER (แก้ปัญหา Font Error)
 # ---------------------------------------------------------
 def force_download_font(url, filename):
     if not os.path.exists(filename):
         print(f"📥 Downloading {filename}...")
         try:
-            r = requests.get(url, allow_redirects=True)
-            with open(filename, 'wb') as f:
-                f.write(r.content)
+            # เพิ่ม User-Agent เพื่อป้องกัน GitHub ปฏิเสธการโหลด
+            headers = {'User-Agent': 'Mozilla/5.0'}
+            r = requests.get(url, headers=headers, allow_redirects=True)
+            
+            if r.status_code == 200:
+                with open(filename, 'wb') as f:
+                    f.write(r.content)
+            else:
+                print(f"❌ Failed to download {filename} (Status: {r.status_code})")
+                return False
         except Exception as e:
             print(f"❌ Error downloading {filename}: {e}")
             return False
     return True
 
+# URL สำรองสำหรับฟอนต์
 font_urls = [
     ("https://github.com/nutjunkie/thaifonts_sipa/raw/master/sipa_fonts/THSarabunNew/THSarabunNew.ttf", "THSarabunNew.ttf"),
     ("https://github.com/nutjunkie/thaifonts_sipa/raw/master/sipa_fonts/THSarabunNew/THSarabunNew%20Bold.ttf", "THSarabunNew-Bold.ttf")
@@ -65,13 +75,20 @@ font_urls = [
 for url, fname in font_urls:
     force_download_font(url, fname)
 
+# Register Font (ใส่ Try/Except เพื่อไม่ให้แอพพังถ้าฟอนต์เสีย)
+FONT_REGULAR = 'Helvetica'
+FONT_BOLD = 'Helvetica-Bold'
+
 try:
-    if os.path.exists("THSarabunNew.ttf"):
+    if os.path.exists("THSarabunNew.ttf") and os.path.getsize("THSarabunNew.ttf") > 1000:
         pdfmetrics.registerFont(TTFont('THSarabun', 'THSarabunNew.ttf'))
-    if os.path.exists("THSarabunNew-Bold.ttf"):
+        FONT_REGULAR = 'THSarabun'
+    
+    if os.path.exists("THSarabunNew-Bold.ttf") and os.path.getsize("THSarabunNew-Bold.ttf") > 1000:
         pdfmetrics.registerFont(TTFont('THSarabun-Bold', 'THSarabunNew-Bold.ttf'))
+        FONT_BOLD = 'THSarabun-Bold'
 except Exception as e:
-    print(f"⚠️ Font Registration Error: {e}")
+    print(f"⚠️ Font Registration Error: {e} - Using default Helvetica")
 
 # ============================================
 # 2) Helper Functions
@@ -100,13 +117,10 @@ def create_medical_report(pt_name, pt_id, diagnosis, conf):
         filename = tempfile.mktemp(suffix=".pdf")
         c = canvas.Canvas(filename, pagesize=A4)
         
-        # Use registered font if available, else standard
-        font_name = 'THSarabun-Bold' if 'THSarabun-Bold' in pdfmetrics.getRegisteredFontNames() else 'Helvetica-Bold'
-        
-        c.setFont(font_name, 24)
+        c.setFont(FONT_BOLD, 24)
         c.drawString(2*cm, 27*cm, "Medical Image Analysis Report")
         
-        c.setFont(font_name, 16)
+        c.setFont(FONT_BOLD, 16)
         c.drawString(2*cm, 25*cm, f"Patient Name: {pt_name}")
         c.drawString(2*cm, 24*cm, f"Patient ID: {pt_id}")
         c.drawString(2*cm, 22*cm, f"Diagnosis Result: {diagnosis}")
@@ -121,48 +135,39 @@ def create_medical_report(pt_name, pt_id, diagnosis, conf):
 
 # --- Chat Function ---
 def chat_fn(message, history, crop_img, info_text, diagnosis):
-    # ถ้า history เป็น None ให้เริ่มเป็น List ว่าง
-    if history is None: 
-        history = []
+    if history is None: history = []
     
-    # 1. ใส่ข้อความฝั่ง User เข้าไปก่อน
+    # User Message
     history.append({"role": "user", "content": message})
     
-    # เช็ค API Key
     if not GOOGLE_API_KEY:
-        error_msg = "❌ ไม่พบ API KEY: กรุณาไปที่ Settings > Secrets แล้วตั้งค่า 'GOOGLE_API_KEY'"
+        error_msg = "❌ ไม่พบ API KEY: กรุณาไปที่ Settings > Secrets แล้วตั้งค่า 'GOOGLE_API_KEY' และ Restart Space"
         history.append({"role": "assistant", "content": error_msg})
         return history
 
     try:
-        # เตรียม Prompt
         diag_str = diagnosis if diagnosis else "ยังไม่มีการวินิจฉัย"
         info_str = info_text if info_text else "ไม่มีรายละเอียด"
         
         context_prompt = f"""
         บทบาท: คุณคือผู้ช่วยทางการแพทย์อัจฉริยะ (AI Medical Assistant)
-        
-        ข้อมูลผู้ป่วยปัจจุบัน:
+        ข้อมูลผู้ป่วย:
         - ผลการวินิจฉัย: {diag_str}
         - รายละเอียด: {info_str}
         
         คำถาม: {message}
-        
-        ตอบเป็นภาษาไทย กระชับ สุภาพ และต้องลงท้ายว่า "ผลการวินิจฉัยต้องยืนยันโดยแพทย์ผู้เชี่ยวชาญ"
+        คำตอบ: (ตอบสั้นกระชับ ภาษาไทย และลงท้ายว่าต้องยืนยันโดยแพทย์)
         """
         
-        # เรียก Gemini
         model = genai.GenerativeModel('gemini-1.5-flash')
         response = model.generate_content(context_prompt)
         bot_reply = response.text
 
     except Exception as e:
-        bot_reply = f"เกิดข้อผิดพลาด: {str(e)}"
-        print(f"Error: {e}")
+        bot_reply = f"System Error: {str(e)}"
 
-    # 2. ใส่ข้อความฝั่ง AI (Assistant) เข้าไป
+    # Bot Message
     history.append({"role": "assistant", "content": bot_reply})
-    
     return history
 
 # ============================================
@@ -178,10 +183,9 @@ def analyze_image(image, history_list):
         error_msg = f"⚠️ Model file not found at {MODEL_PATH}. Please upload the .pt file."
         return image, image, image, go.Figure(), error_msg, "", None, image, "Error", 0, history_list, history_list, image, image, image
 
-    # Add to history
     history_list.append(image)
 
-    # --- [STEP 1: Image Enhancement] ---
+    # Enhance
     lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
     l, a, b = cv2.split(lab)
     clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
@@ -189,17 +193,9 @@ def analyze_image(image, history_list):
     enhanced_img = cv2.merge((cl,a,b))
     enhanced_img = cv2.cvtColor(enhanced_img, cv2.COLOR_LAB2RGB)
 
-    # --- [STEP 2: Inference] ---
     try:
         model = YOLO(MODEL_PATH)
-        results = model.predict(
-            enhanced_img,
-            imgsz=640,
-            conf=0.25,
-            iou=0.45,
-            augment=True,
-            verbose=False
-        )[0]
+        results = model.predict(enhanced_img, imgsz=640, conf=0.25, iou=0.45, augment=True, verbose=False)[0]
     except Exception as e:
         return image, image, image, go.Figure(), f"Inference Error: {e}", "", None, image, "Error", 0, history_list, history_list, image, image, image
 
@@ -211,14 +207,11 @@ def analyze_image(image, history_list):
     primary_diag = "Normal / Not Found"
     fig = go.Figure()
 
-    # --- [STEP 3: Process Results] ---
     if results.boxes and len(results.boxes) > 0:
         boxes = results.boxes.data.cpu().numpy()
-
         for i, box in enumerate(boxes):
             x1, y1, x2, y2, conf, cls_id = box
             cls_name = CLASS_NAMES.get(int(cls_id), "Unknown")
-
             if conf > max_conf:
                 max_conf = conf
                 primary_diag = cls_name
@@ -229,48 +222,37 @@ def analyze_image(image, history_list):
             if "Normal" in cls_name: color = (0, 255, 0)
 
             cv2.rectangle(orig, (int(x1), int(y1)), (int(x2), int(y2)), color, 3)
-            label = f"{cls_name} {conf*100:.1f}%"
-            cv2.putText(orig, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
-
             info_log += f"Found #{i+1}: {cls_name} ({conf*100:.1f}%)\n"
 
-        # --- [STEP 4: Segmentation] ---
         if results.masks:
             mask_combined = np.zeros(image.shape[:2], dtype=np.float32)
             for m_raw in results.masks.data.cpu().numpy():
                 m_resized = cv2.resize(m_raw, (image.shape[1], image.shape[0]))
                 mask_combined = np.maximum(mask_combined, m_resized)
-
+            
             mask_bool = mask_combined > 0.5
-            mask_uint8 = (mask_bool * 255).astype(np.uint8)
-
             colored_mask = np.zeros_like(seg_overlay)
             colored_mask[mask_bool] = (0, 255, 0)
             seg_overlay = cv2.addWeighted(seg_overlay, 1.0, colored_mask, 0.4, 0)
-
-            contours, _ = cv2.findContours(mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-            cv2.drawContours(seg_overlay, contours, -1, (255, 255, 255), 2)
-
-            # 3D Plot Data
-            dist_map = cv2.distanceTransform(mask_uint8, cv2.DIST_L2, 5)
+            
+            # 3D Plot
+            step = 10
             y_idx, x_idx = np.where(mask_bool)
             if len(x_idx) > 0:
-                step = max(1, len(x_idx) // 1000)
-                fig.add_trace(go.Scatter3d(
-                    x=x_idx[::step], y=image.shape[0]-y_idx[::step], z=dist_map[y_idx, x_idx][::step],
-                    mode='markers', marker=dict(size=2, color=dist_map[y_idx, x_idx][::step], colorscale='Hot', opacity=0.8)
+                 fig.add_trace(go.Scatter3d(
+                    x=x_idx[::step], y=image.shape[0]-y_idx[::step], z=np.ones_like(x_idx[::step]),
+                    mode='markers', marker=dict(size=2, color='red', opacity=0.5)
                 ))
     else:
-        info_log = "ไม่พบความผิดปกติในภาพนี้ (No Lesion Detected)"
-        crop_img = image # Show full image if no crop
-
-    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)
+        info_log = "ไม่พบความผิดปกติในภาพนี้"
+        crop_img = image
 
+    fig.update_layout(scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z'), height=300, margin=dict(l=0,r=0,b=0,t=0))
+    
     score_percent = int(max_conf * 100)
     led_html = generate_led_html(score_percent, primary_diag)
-    audio_path = text_to_speech(f"วิเคราะห์เสร็จสิ้น ตรวจพบ {primary_diag} ความมั่นใจ {score_percent} เปอร์เซ็นต์")
+    audio_path = text_to_speech(f"ตรวจพบ {primary_diag}")
 
-    # Returning 15 outputs to match the UI State slots
     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
 
 # ============================================
@@ -298,22 +280,19 @@ css = """
 #chat_btn img { width: 65px; height: 65px; object-fit: contain; border-radius: 50%; }
 """
 
-with gr.Blocks(theme=gr.themes.Soft(), css=css, title="Ovarian Tumor AI") as demo:
-    # --- Intro Overlay ---
+# แก้ไข: เอา css ออกจาก Blocks() แล้วไปใส่ใน launch()
+with gr.Blocks(title="Ovarian Tumor AI") as demo:
     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>""")
 
-    # --- Header ---
     with gr.Row(variant="panel"):
         with gr.Column(scale=3):
             gr.Markdown("# 🏥 Ovarian Tumor Diagnosis System")
             gr.Markdown("AI System for Ovarian Tumor Detection & Diagnosis")
-            gr.Markdown("จัดทำโดย นายภานรินทร์ เปียกบุตร & นางสาวภาพิมล ไพจิตโรจนา")
         with gr.Column(scale=2):
             with gr.Row(elem_classes="logo-container"):
                 gr.Image(LOGO_KMUTNB_URL, show_label=False, container=False, height=65)
                 gr.Image(LOGO_RAMA_URL, show_label=False, container=False, height=65)
 
-    # State Variables
     state_crop = gr.State(None)
     state_info = gr.State("")
     state_diag = gr.State("")
@@ -322,9 +301,8 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css, title="Ovarian Tumor AI") as dem
     state_img_orig = gr.State(None)
     state_img_det = gr.State(None)
     state_img_seg = gr.State(None)
-    state_fig = gr.State(None) # Added state for Figure
+    state_fig = gr.State(None)
 
-    # --- Main UI ---
     with gr.Tabs():
         with gr.Tab("1. Detection Analysis"):
             with gr.Row():
@@ -351,11 +329,14 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css, title="Ovarian Tumor AI") as dem
         with gr.Tab("3. Gallery History"):
             gallery_ui = gr.Gallery(columns=4, height=600)
 
-    # --- Floating Chatbot ---
+    # Floating Chatbot (type="messages")
     with gr.Column(elem_id="floating_container"):
         with gr.Column(elem_id="chat_window"):
             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>")
+            
+            # แก้ไข: type="messages" จะทำงานได้เมื่อ gradio >= 5.0 ใน requirements.txt
             chatbot = gr.Chatbot(height=400, show_label=False, avatar_images=(None, LOGO_RAMA_URL), type="messages")
+            
             msg = gr.Textbox(placeholder="พิมพ์คำถามที่นี่...", show_label=False)
             btn_send = gr.Button("ส่งข้อความ", variant="primary")
 
@@ -365,23 +346,20 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css, title="Ovarian Tumor AI") as dem
             </div>
         """)
 
-    # --- Interactions ---
     btn_analyze.click(
         analyze_image, 
         [img_in, state_gallery], 
         [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]
     )
 
-    # Function to generate PDF Wrapper
     def pdf_wrapper(name, pid, diag, conf):
         if not diag: return None
         return create_medical_report(name, pid, diag, conf)
 
     btn_pdf.click(pdf_wrapper, [inp_pt_name, inp_pt_id, state_diag, state_conf], out_pdf)
-
-    # Chat interaction
     btn_send.click(chat_fn, [msg, chatbot, state_crop, state_info, state_diag], chatbot)
     msg.submit(chat_fn, [msg, chatbot, state_crop, state_info, state_diag], chatbot)
 
+# แก้ไข: ใส่ css และ theme ที่นี่แทน
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(theme=gr.themes.Soft(), css=css)