Update app.py

app.py

@@ -1,3 +1,26 @@
+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
 # ============================================
 
@@ -6,6 +29,22 @@ 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:
@@ -29,27 +68,69 @@ font_urls = [
 ]
 
 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:
-        filename = tempfile.mktemp(suffix=".pdf")
-        c = canvas.Canvas(filename, pagesize=A4)
+        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)
         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 (Dictionary Format)
 # ============================================
 def chat_fn(message, history, crop_img, info_text, diagnosis):
-
     if history is None: history = []
     
     # 1. API Key Check
     if not GOOGLE_API_KEY:
         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, ""
@@ -59,6 +140,15 @@ def chat_fn(message, history, crop_img, info_text, diagnosis):
         context_prompt = f"""
         บทบาท: คุณคือผู้ช่วยทางการแพทย์อัจฉริยะ (AI Medical Assistant)
         
+        ข้���มูลบริบททางการแพทย์ของผู้ป่วยรายนี้:
+        - 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"
         """
         
@@ -67,6 +157,7 @@ def chat_fn(message, history, crop_img, info_text, diagnosis):
         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}")
 
@@ -74,10 +165,183 @@ def chat_fn(message, history, crop_img, info_text, diagnosis):
     history.append({"role": "user", "content": message})
     history.append({"role": "assistant", "content": bot_reply})
     
-
     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
+
+# ============================================
+# 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)
 
@@ -92,8 +356,28 @@ def chat_fn(message, history, crop_img, info_text, diagnosis):
             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)
 
     # 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()