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import gradio as gr
from nlp_transform import extract_conditions
from area_calculator import calculate_area, get_missing_keys
def chat(user_input, history):
try:
result = extract_conditions(user_input)
print("🧠 NLP 萃取結果：", result)
response_lines = []
# 檢查缺失欄位
missing = get_missing_keys(result)
# 顯示已抓到欄位
if "site_area" in result:
response_lines.append(f"📏 基地面積：{result['site_area']} m²")
if "BCR" in result:
response_lines.append(f"🏗️ 建蔽率：{result['BCR']}%")
if "FAR" in result:
response_lines.append(f"🏙️ 容積率：{result['FAR']}%")
if "bonus_far" in result:
response_lines.append(f"🎁 容積獎勵：{result['bonus_far']}%")
# 如果資料齊全 → 計算面積
if not missing:
area_result = calculate_area(result)
response_lines.append(area_result["summary"])
else:
response_lines.append("⚠️ 缺少以下資料：" + "、".join(missing))
final_response = "\n".join(response_lines) if response_lines else "⚠️ 無法辨識有用資訊，請重新描述～"
# 更新歷史
new_history = history + [
{"role": "user", "content": user_input},
{"role": "assistant", "content": final_response},
]
return new_history, new_history, ""
except Exception as e:
print("❌ 錯誤訊息：", e)
error_msg = f"❌ 發生錯誤：{str(e)}"
new_history = history + [
{"role": "user", "content": user_input},
{"role": "assistant", "content": error_msg},
]
return new_history, new_history, ""
# ✅ Gradio 介面區塊
with gr.Blocks() as demo:
gr.Markdown("## 🏗️ 建築條件助理")
chatbot = gr.Chatbot(label="條件分析助手", type="messages")
msg = gr.Textbox(label="請輸入建築條件", placeholder="例：基地面積2000平方公尺，容積率200%，建蔽率50%", lines=2)
send_btn = gr.Button("📨 傳送")
state = gr.State([])
def handle_input(user_input, history):
return chat(user_input, history)
msg.submit(handle_input, [msg, state], [chatbot, state, msg])
send_btn.click(handle_input, [msg, state], [chatbot, state, msg])
# ✅ 執行 Gradio 介面
demo.launch(share=True) |