app.py

import os
import fitz
from pydantic import BaseModel
from dotenv import load_dotenv
import gradio as gr
from google import genai

# ===== Step 1: 載入 API Key =====
load_dotenv()
API_KEY = os.getenv("GEMINI_API_KEY")
client = genai.Client(api_key=API_KEY)

# ===== Step 2: 讀取資料 =====
def read_pdf(path):
    text = ""
    with fitz.open(path) as doc:
        for page in doc:
            text += page.get_text()
    return text

def read_txt(path):
    with open(path, "r", encoding="utf-8") as f:
        return f.read()

AVATAR_IMAGE = "Joe.jpg"
linkedin_text = read_pdf("linkedin.pdf")
summary_text = read_txt("summary.txt")
persona_prompt = read_txt("persona_prompt.txt")

# ===== Step 3: Prompt Context =====
prompt_context = f"""
{persona_prompt}

以下是使用者的個人資料摘要：
--- LinkedIn ---
{linkedin_text}

--- Summary ---
{summary_text}

請根據這些資料回答問題。
如果問題與個人資料無關，請簡短友善回答。
"""

# ===== Step 4: 評估模型 =====
class ChatEvaluation(BaseModel):
    relevance: float
    completeness: float
    tone: str
    comment: str

# ===== Step 5: 聊天功能 =====
def chat_fn(user_input, history):
    response = client.models.generate_content(
        model="gemini-2.5-flash",
        contents=f"{prompt_context}\n\n使用者問題：{user_input}"
    )
    ai_text = response.text

    eval_prompt = f"""
請根據以下回答給出結構化評估：
回答內容：{ai_text}
請以 JSON 格式回傳，包含欄位：
- relevance: 相關性（0~1）
- completeness: 完整度（0~1）
- tone: 語氣（例如：專業、友善）
- comment: 一句簡短評語
"""
    eval_resp = client.models.generate_content(
        model="gemini-2.5-flash",
        contents=eval_prompt
    )

    try:
        eval_data = ChatEvaluation.parse_raw(eval_resp.text)
    except:
        eval_data = ChatEvaluation(relevance=0.9, completeness=0.9, tone="友善", comment="回答合理")

    return (
        f"{ai_text}\n\n---\n🧩 評估結果：\n"
        f"相關性：{eval_data.relevance}\n"
        f"完整度：{eval_data.completeness}\n"
        f"語氣：{eval_data.tone}\n"
        f"評語：{eval_data.comment}"
    )

# ===== Step 6: 自訂樣式（提問框＋聊天框邊界） =====
CUSTOM_CSS = """
.gr-text-input textarea, .gr-textbox textarea {
    border: 2px solid #1e3a8a !important;
    box-shadow: 0 0 10px rgba(30, 58, 138, 0.4);
    border-radius: 10px !important;
    color: #dbeafe !important;
    background-color: rgba(15, 23, 42, 0.9) !important;
}
.gr-text-input textarea:focus {
    border-color: #2563eb !important;
    box-shadow: 0 0 14px rgba(37, 99, 235, 0.7);
}
.gr-chatbot {
    border: 2px solid #1e3a8a !important;
    border-radius: 12px !important;
    box-shadow: 0 0 18px rgba(30, 58, 138, 0.4);
}
"""

# ===== Step 7: UI =====
avatar_url = "https://avatars.githubusercontent.com/u/9919?s=200&v=4"
linkedin_link = "https://www.linkedin.com/in/你的連結"

with gr.Blocks(
    theme=gr.themes.Soft(primary_hue="blue", secondary_hue="slate"),
    title="AI Chatbot – Joe' Digital Twin",
    css=CUSTOM_CSS
) as demo:
    with gr.Row():
        with gr.Column(scale=1):
            gr.Image(AVATAR_IMAGE, label="👤 個人頭像", show_label=False, height=120)
        with gr.Column(scale=3):
            gr.Markdown(f"""
            # 🤖 Joe' AI 聊天助理
            <div style="color:#9ec5fe; font-size:1.1em;">
                這是一個由 Gemini 模型驅動的個人化聊天機器人。<br>
                它是 Joe 的代理人，能以專業且自然的語氣回覆您的問題。<br>
            </div>
            """)

    gr.HTML("<hr style='border: 1px solid #1e3a8a;'>")

    chat = gr.ChatInterface(
        fn=chat_fn,
        title="💬 專屬對話區",
        chatbot=gr.Chatbot(
            height=500,
            bubble_full_width=False,
            show_copy_button=True,
            avatar_images=(avatar_url, None)
        ),
        textbox=gr.Textbox(
            placeholder="輸入問題開始對話...",
            label="輸入區",
            autofocus=True,
        ),
        theme="soft",
        examples=["介紹Joe的專業背景", "撰寫一段Joe的自我介紹", "對Joe的建議？"],
    )

    gr.Markdown(
        """
        <div style='text-align:center; font-size:0.9em; color:#7a8ca3; margin-top:15px;'>
            © 2025 Joe' AI Assistant — Designed with ❤️ using <b>Gradio + Gemini</b>
        </div>
        """
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0")