Update app.py

app.py

@@ -1,382 +1,99 @@
-import os
-import sys
-import cv2
-import numpy as np
-import gradio as gr
-import plotly.graph_objects as go
-from ultralytics import YOLO
-import google.generativeai as genai
-from PIL import Image
-from gtts import gTTS
-import tempfile
-import datetime
-import requests
-import shutil
-
-# --- ReportLab Imports (PDF) ---
-from reportlab.pdfgen import canvas
-from reportlab.lib.pagesizes import A4
-from reportlab.lib.units import cm, mm
-from reportlab.pdfbase import pdfmetrics
-from reportlab.pdfbase.ttfonts import TTFont
-
-# ============================================
 # 1) Configuration & Setup
 # ============================================
 
-# 🔑 API KEY
-GOOGLE_API_KEY = os.getenv("AIzaSyBYksOq03N5V2MjSYicHdsk4ESdyR9FABw") 
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
 if GOOGLE_API_KEY:
     genai.configure(api_key=GOOGLE_API_KEY)
 
-# 📂 Model Path
-MODEL_PATH = "otu_multiclass_yolo11s_v2.pt"
-
-LOGO_KMUTNB_URL = "https://www.mou.kmutnb.ac.th/logo_kmutnb.png"
-LOGO_RAMA_URL ="https://www.rama.mahidol.ac.th/nursing/sites/default/files/public/Rama_Logo.png"
-INTRO_SOUND_URL = "https://cdn.pixabay.com/download/audio/2022/03/24/audio_c8c8a73467.mp3?filename=cinematic-atmosphere-score-2-22136.mp3"
-
-CLASS_NAMES = {
-    0: "Chocolate cyst", 1: "Serous cystadenoma", 2: "Teratoma", 3: "Theca cell tumor",
-    4: "Simple cyst", 5: "Normal ovary", 6: "Mucinous cystadenoma", 7: "High grade serous"
-}
-
-# ---------------------------------------------------------
-# 🛠️ AUTO-DOWNLOAD FONTS
-# ---------------------------------------------------------
-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)
+            session = requests.Session()
+            session.headers.update({'User-Agent': 'Mozilla/5.0'})
+            r = session.get(url, allow_redirects=True)
+            if r.status_code == 200 and len(r.content) > 1000:
+                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
 
 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")
+    ("https://raw.githubusercontent.com/nutjunkie/thaifonts_sipa/master/sipa_fonts/THSarabunNew/THSarabunNew.ttf", "THSarabunNew.ttf"),
+    ("https://raw.githubusercontent.com/nutjunkie/thaifonts_sipa/master/sipa_fonts/THSarabunNew/THSarabunNew%20Bold.ttf", "THSarabunNew-Bold.ttf")
 ]
 
 for url, fname in font_urls:
-    force_download_font(url, fname)
-
-try:
-    if os.path.exists("THSarabunNew.ttf"):
-        pdfmetrics.registerFont(TTFont('THSarabun', 'THSarabunNew.ttf'))
-    if os.path.exists("THSarabunNew-Bold.ttf"):
-        pdfmetrics.registerFont(TTFont('THSarabun-Bold', 'THSarabunNew-Bold.ttf'))
-except Exception as e:
-    print(f"⚠️ Font Registration Error: {e}")
-
-# ============================================
-# 2) Helper Functions
-# ============================================
-def text_to_speech(text):
-    try:
-        tts = gTTS(text, lang='th')
-        f = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
-        tts.save(f.name)
-        return f.name
-    except: return None
-
-def generate_led_html(score, diagnosis):
-    color = "#ff4444" if "High grade" in diagnosis else "#00C851" if "Normal" in diagnosis else "#ffbb33"
-    return f"""
-    <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);'>
-        <span style='color: #e0e0e0; font-size: 14px; text-transform: uppercase;'>Primary Diagnosis</span><br>
-        <span style='color: {color}; font-size: 24px; font-weight: 800;'>{diagnosis}</span><br>
-        <hr style='border-color: #444; margin: 10px 0;'>
-        <span style='color: white; font-size: 32px; font-weight: bold;'>{score}%</span>
-    </div>
-    """
-
-def create_medical_report(pt_name, pt_id, diagnosis, conf):
     try:
         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.drawString(2*cm, 27*cm, "Medical Image Analysis Report")
-        
-        c.setFont(font_name, 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}")
-        c.drawString(2*cm, 21*cm, f"Confidence Score: {conf}%")
-        c.drawString(2*cm, 20*cm, f"Date: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M')}")
-        
-        c.save()
-        return filename
-    except Exception as e:
-        print(f"PDF Error: {e}")
         return None
 
 # ============================================
-# [FIXED] Chat Function 
+# [FIXED] Chat Function (Dictionary Format)
 # ============================================
 def chat_fn(message, history, crop_img, info_text, diagnosis):
-    # เปลี่ยนจาก history = [] เป็นการรับค่า list ของ dict
+
     if history is None: history = []
     
-    # 1. เช็ค API Key
+    # 1. API Key Check
     if not GOOGLE_API_KEY:
-        response = "❌ ไม่พบ API KEY: กรุณาไปที่ Settings > Secrets แล้วตั้งค่า 'GOOGLE_API_KEY' จากนั้นกด Restart Space"
-        # เพิ่มข้อความลง history แบบ Dictionary (ตามมาตรฐานใหม่ Huggingface/Gradio)
+        response = "❌ ไม่พบ API KEY: กรุณาตรวจสอบการตั้งค่า GOOGLE_API_KEY ในไฟล์ app.py"
+
         history.append({"role": "user", "content": message})
         history.append({"role": "assistant", "content": response})
         return history, ""
 
     try:
-        # 2. สร้าง Prompt
+        # 2. Context Prompt
         context_prompt = f"""
         บทบาท: คุณคือผู้ช่วยทางการแพทย์อัจฉริยะ (AI Medical Assistant)
         
-        ข้อมูลบริบททางการแพทย์ของผู้ป่วยรายนี้:
-        - ผลการวินิจฉัยหลัก (Diagnosis): {diagnosis if diagnosis else "ยังไม่มีการวินิจฉัย"}
-        - ข้อมูลเพิ่มเติม: {info_text if info_text else "ไม่มี"}
-        
-        คำถามจากผู้ใช้: {message}
-        
-        คำแนะนำในการตอบ:
-        - ตอบเป็นภาษาไทย ให้กระชับ เข้าใจง่าย
-        - ถ้าเกี่ยวกับเรื่องซีสต์หรือเนื้องอก ให้ข้อมูลตามหลักการแพทย์
         - *สำคัญ*: ต้องลงท้ายเสมอว่า "ควรปรึกษาแพทย์ผู้เชี่ยวชาญเพื่อการวินิจฉัยที่แม่นยำที่สุด"
         """
         
-        # 3. เรียกใช้ Gemini
-        model = genai.GenerativeModel('gemini-1.5-flash')
+        # 3. Call Gemini
+        model = genai.GenerativeModel('gemini-2.5-flash')
         response = model.generate_content(context_prompt)
         bot_reply = response.text
 
-    except Exception as e:
         bot_reply = f"เกิดข้อผิดพลาด (System Error): {str(e)}"
         print(f"DEBUG ERROR: {e}")
 
-    # [FIX] เพิ่มข้อความลงใน History แบบ Dictionary
+    # Append Dictionary format
     history.append({"role": "user", "content": message})
     history.append({"role": "assistant", "content": bot_reply})
     
-    # คืนค่า history และ string ว่าง ("") เพื่อลบข้อความในช่องพิมพ์
-    return history, ""
-
-# ============================================
-# 3) Main Inference Logic
-# ============================================
-def analyze_image(image, history_list):
-    if history_list is None: history_list = []
-    if image is None:
-        return [None]*15
-
-    if not os.path.exists(MODEL_PATH):
-        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
-
-    history_list.append(image)
-
-    lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
-    l, a, b = cv2.split(lab)
-    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
-    cl = clahe.apply(l)
-    enhanced_img = cv2.merge((cl,a,b))
-    enhanced_img = cv2.cvtColor(enhanced_img, cv2.COLOR_LAB2RGB)
 
-    try:
-        model = YOLO(MODEL_PATH)
-        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
-
-    orig = image.copy()
-    seg_overlay = image.copy()
-    crop_img = np.zeros_like(image)
-    info_log = "Analysis Results:\n" + "-"*20 + "\n"
-    max_conf = 0
-    primary_diag = "Normal / Not Found"
-    fig = go.Figure()
-
-    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
-                crop_img = image[int(y1):int(y2), int(x1):int(x2)]
-
-            color = (0, 165, 255)
-            if "High grade" in cls_name: color = (255, 0, 0)
-            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"
-
-        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)
-
-            dist_map = cv2.distanceTransform(mask_uint8, cv2.DIST_L2, 5)
-            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)
-                ))
-    else:
-        info_log = "ไม่พบความผิดปกติในภาพนี้ (No Lesion Detected)"
-        crop_img = image
-
-    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)
-
-    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")
-
-    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
+    return history, ""
 
 # ============================================
-# 4) Gradio UI
-# ============================================
-css = """
-@import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;700;900&display=swap');
-.logo-container { display: flex; justify-content: flex-end; align-items: center; gap: 20px; }
-#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; }
-.intro-content { display: flex; gap: 40px; align-items: center; animation: zoomInLogos 3s cubic-bezier(0.25, 0.46, 0.45, 0.94) forwards; }
-.intro-logo { height: 120px; width: auto; filter: drop-shadow(0 0 10px rgba(255,255,255,0.3)); }
-.intro-text-container { margin-top: 40px; text-align: center; opacity: 0; animation: textSlideUp 1.5s ease-out 1.2s forwards; }
-.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; }
-.intro-subtitle { color: #b3b3b3; font-family: 'Montserrat', sans-serif; font-size: 1.2rem; font-weight: 400; letter-spacing: 4px; }
-@keyframes zoomInLogos { 0% { transform: scale(0.8); opacity: 0; } 50% { transform: scale(1.05); opacity: 1; } 100% { transform: scale(1.0); opacity: 1; } }
-@keyframes textSlideUp { 0% { transform: translateY(30px); opacity: 0; } 100% { transform: translateY(0); opacity: 1; } }
-@keyframes fadeOutOverlay { to { opacity: 0; visibility: hidden; z-index: -1; } }
-
-/* Floating Chatbot CSS */
-#floating_container { position: fixed; bottom: 25px; left: 25px; z-index: 9999; display: flex; flex-direction: column; align-items: flex-start; }
-#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; }
-.show-chat #chat_window { display: flex !important; }
-#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; }
-#chat_btn:hover { transform: scale(1.1); }
-#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 ---
-    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("")
-    state_conf = gr.State(0)
-    state_gallery = gr.State([])
-    state_img_orig = gr.State(None)
-    state_img_det = gr.State(None)
-    state_img_seg = gr.State(None)
-    state_fig = gr.State(None)
-
-    # --- Main UI ---
-    with gr.Tabs():
-        with gr.Tab("1. Detection Analysis"):
-            with gr.Row():
-                with gr.Column(scale=2):
-                    img_in = gr.Image(label="Upload Ultrasound Image", type="numpy", height=400)
-                    btn_analyze = gr.Button("🔍 Analyze Image", variant="primary")
-                with gr.Column(scale=1):
-                    html_led = gr.HTML()
-                    aud = gr.Audio(label="Voice Assistant", autoplay=True)
-                    txt_log = gr.Textbox(label="Detailed Findings", lines=8)
-
-            with gr.Row():
-                img_det = gr.Image(label="AI Detection", interactive=False)
-                img_seg = gr.Image(label="Segmentation", interactive=False)
-                img_crop = gr.Image(label="Focused Lesion", interactive=False)
-
-        with gr.Tab("2. Medical Report"):
-            with gr.Row():
-                inp_pt_name = gr.Textbox(label="Patient Name")
-                inp_pt_id = gr.Textbox(label="Patient ID (HN)")
-            btn_pdf = gr.Button("🖨️ Generate PDF Report", variant="primary")
-            out_pdf = gr.File(label="Download Report")
-
         with gr.Tab("3. Gallery History"):
             gallery_ui = gr.Gallery(columns=4, height=600)
 
-    # --- [FIXED] Floating Chatbot ---
+    # --- Floating Chatbot ---
     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>")
             
-            # [FIX] เพิ่ม type="messages" เพื่อบอก Gradio ว่าใช้ format ใหม่
-            chatbot = gr.Chatbot(height=400, show_label=False, avatar_images=(None, LOGO_RAMA_URL), type="messages")
+            # Chatbot: No type param needed here, but data passed will be dicts
+            chatbot = gr.Chatbot(height=400, show_label=False, avatar_images=(None, LOGO_RAMA_URL))
             
             msg = gr.Textbox(placeholder="พิมพ์คำถามที่นี่...", show_label=False)
             btn_send = gr.Button("ส่งข้อความ", variant="primary")
 
-        gr.HTML(f"""
-            <div id="chat_btn" onclick="document.getElementById('floating_container').classList.toggle('show-chat')">
-                <img src="{LOGO_RAMA_URL}" />
-            </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]
-    )
-
-    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)
 
-    # [FIX] Chat interaction: เพิ่ม outputs ตัวที่ 2 (msg) เพื่อล้างข้อความ
+    # Chat interactions
     btn_send.click(chat_fn, [msg, chatbot, state_crop, state_info, state_diag], [chatbot, msg])
     msg.submit(chat_fn, [msg, chatbot, state_crop, state_info, state_diag], [chatbot, msg])
-
-if __name__ == "__main__":
-    demo.launch()