Update app.py

app.py

@@ -4,7 +4,6 @@ import gradio as gr
 from transformers import BertTokenizerFast, BertForTokenClassification
 
 # === ตั้งค่าโมเดลจาก Hub ===
-# เปลี่ยนเป็นโมเดลของคุณ เช่น "donla/htn-ner"
 MODEL_ID = "Donlagon007/htn-ner-v1"
 
 # โหลดโมเดล/โทเคนไนเซอร์ (CPU เป็นค่าเริ่มต้นใน Spaces)
@@ -140,16 +139,19 @@ def extract_structured(text):
         key_findings.append(row)
 
     risks = set()
+    # 4.1 Diabetes
     fpg = next((cn_num(p["value"]) for p in pairs if p["test"] == "空腹血糖"), None)
     a1c = next((cn_num(p["value"]) for p in pairs if p["test"] == "HbA1c"), None)
-    ldl = next((cn_num(p["value"]) for p in pairs if p["test"] == "LDL"), None)
-    bp_val = next((p["value"] for p in pairs if p["test"] in ["診間血壓","家庭血壓","24小時動態血壓"]), None)
     if (fpg is not None and fpg >= 126) or (a1c is not None and a1c >= 6.5):
         risks.add("糖尿病")
+    # 4.2 Hyperlipidemia via LDL
+    ldl = next((cn_num(p["value"]) for p in pairs if p["test"] == "LDL"), None)
     if ldl is not None and ldl >= 160:
         risks.add("高血脂")
     elif ldl is not None and ldl >= 130:
         risks.add("高血脂(輕度)")
+    # 4.3 Hypertension via BP or DISEASE mention
+    bp_val = next((p["value"] for p in pairs if p["test"] in ["診間血壓","家庭血壓","24小時動態血壓"]), None)
     if bp_val:
         sys, dia = parse_bp(bp_val)
         if sys and dia and (sys >= 140 or dia >= 90):
@@ -157,6 +159,7 @@ def extract_structured(text):
     if any(e["type"] == "DISEASE" and "高血壓" in e["text"] for e in entities):
         risks.add("高血壓")
 
+    # recommendations
     recs = []
     for e in entities:
         if e["type"] == "DRUG":
@@ -191,27 +194,69 @@ def extract_structured(text):
     }
     return tokens, entities, structured
 
+# ---------- Human-readable report ----------
+def make_readable_report(structured: dict) -> str:
+    name = structured.get("name") or "病人"
+    age  = structured.get("age")
+    sex  = structured.get("sex")
+    head = f"【健檢摘要】{name}"
+    if age is not None: head += f"（{age}歲"
+    else: head += "（年齡不詳"
+    if sex: head += f"，{sex}）"
+    else: head += "）"
+
+    # key findings
+    kfs = structured.get("key_findings", [])
+    abn_lines, nor_lines = [], []
+    for k in kfs:
+        t, v = k.get("test"), k.get("value")
+        st = k.get("status")
+        if st in ("異常","偏高"):
+            abn_lines.append(f"．{t}: {v}（{st}）")
+        elif st == "正常":
+            nor_lines.append(f"．{t}: {v}（正常）")
+        else:
+            nor_lines.append(f"．{t}: {v}")
+
+    risks = structured.get("disease_risk", [])
+    recs  = structured.get("recommendations", [])
+    summary = structured.get("summary") or ""
+
+    sections = [head, ""]
+    if abn_lines:
+        sections += ["【異常/偏高項目】"] + abn_lines + [""]
+    if nor_lines:
+        sections += ["【其他檢測】"] + nor_lines + [""]
+    if risks:
+        sections += ["【疾病風險/診斷】", "．" + "、".join(risks), ""]
+    if recs:
+        sections += ["【建議】", "．" + "、".join(recs), ""]
+    if summary:
+        sections += ["【摘要敘述】", summary, ""]
+    return "\n".join(sections).strip()
+
 # ---------- Gradio UI ----------
 EXAMPLE = "李偉（65歲，男），有高血壓與糖尿病。\n診間血壓152/94mmHg，空腹血糖138mg/dL，HbA1c 7.1%。\n建議使用ARB類藥物並低鹽飲食。"
 
 def run(text):
     tokens, entities, structured = extract_structured(text)
-    return json.dumps(structured, ensure_ascii=False, indent=2), \
-           json.dumps(entities, ensure_ascii=False, indent=2), \
-            json.dumps(tokens, ensure_ascii=False, indent=2)
-           
+    human_report = make_readable_report(structured)
+    return (
+        human_report,
+        json.dumps(structured, ensure_ascii=False, indent=2),
+        json.dumps(entities, ensure_ascii=False, indent=2),
+        json.dumps(tokens, ensure_ascii=False, indent=2),
+    )
 
 with gr.Blocks(title="HTN NER (Chinese)") as demo:
-    gr.Markdown("## Hypertension NER → Tokens / Entities / Structured JSON")
+    gr.Markdown("## Hypertension NER → Human Report / JSON / Entities / Tokens")
     inp = gr.Textbox(label="輸入文字 (中文)", lines=6, value=EXAMPLE)
     btn = gr.Button("Analyze")
-    out_struct = gr.Code(label="Structured Reports")
+    out_report   = gr.Textbox(label="Doctor Report", lines=12)
+    out_struct   = gr.Code(label="Structured JSON")
     out_entities = gr.Code(label="Entities (spans)")
-    out_tokens = gr.Code(label="Token-level (B/I/O)")
-    btn.click(run, inputs=inp, outputs=[out_struct, out_entities, out_tokens])
-
-demo.launch()
-
+    out_tokens   = gr.Code(label="Token-level (B/I/O)")
+    btn.click(run, inputs=inp, outputs=[out_report, out_struct, out_entities, out_tokens])
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0")