Create app.py

app.py

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+from transformers import AutoImageProcessor, AutoModelForImageSegmentation
+from PIL import Image
+import torch
+import numpy as np
+import gradio as gr
+import matplotlib.pyplot as plt
+
+# --- ตั้งชื่อโมเดล (ใช้ของ Hugging Face) ---
+model_name = "Arya6967/SAMMed-2d"  # ตัวอย่าง
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageSegmentation.from_pretrained(model_name)
+
+# --- โหลดภาพ ---
+img_path = "your_image.png"  # เปลี่ยนเป็นภาพร่างกาย/diagram ของคุณ
+img = Image.open(img_path).convert("RGB")
+
+# --- ทำ segmentation ---
+inputs = processor(img, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# --- แปลง logits -> mask ---
+mask = outputs.logits.argmax(dim=1)[0].cpu().numpy()  # mask ของแต่ละ pixel
+# สมมติ organ_id เริ่มจาก 0
+organ_names = [
+    "Heart", "Liver", "Lung L", "Lung R", "Stomach", "Kidney L", "Kidney R",
+    "Brain", "Pancreas", "Spleen", "Bladder", "Intestine", "Esophagus", "Gallbladder",
+    "Appendix", "Thyroid", "Trachea", "Aorta", "Vena Cava", "Pelvis",
+    "Femur L", "Femur R", "Humerus L", "Humerus R", "Radius L", "Radius R",
+    "Ulna L", "Ulna R", "Clavicle L", "Clavicle R", "Scapula L", "Scapula R",
+    "Skull", "Spine", "Ribcage", "Sternum", "Patella L", "Patella R",
+    "Tibia L", "Tibia R", "Fibula L", "Fibula R", "Shoulder Joint L", "Shoulder Joint R",
+    "Elbow Joint L", "Elbow Joint R", "Knee Joint L", "Knee Joint R", "Ankle Joint L", "Ankle Joint R"
+]
+
+# --- ฟังก์ชันคลิก ---
+def show_organ(click_data):
+    # click_data: (x, y) pixel
+    x = int(click_data[0])
+    y = int(click_data[1])
+    organ_id = mask[y, x]
+    if organ_id < len(organ_names):
+        return f"อวัยวะ: {organ_names[organ_id]}"
+    else:
+        return "ไม่ทราบอวัยวะ"
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("## คลิกบนภาพเพื่อดูอวัยวะ")
+    img_component = gr.Image(value=np.array(img), interactive=True, tool="select")
+    output_text = gr.Textbox(label="ผลลัพธ์")
+    img_component.select(show_organ, inputs=img_component, outputs=output_text)
+
+demo.launch()