app.py

from dotenv import load_dotenv
from openai import OpenAI
import json
import os
import requests
from pypdf import PdfReader
import gradio as gr

# 確保載入 .env 檔案中的環境變數
load_dotenv(override=True)

# --- 您的工具函式 (PushOver) ---
def push(text):
    """
    使用 Pushover 服務發送通知。
    需要 PUSHOVER_TOKEN 和 PUSHOVER_USER 環境變數。
    """
    requests.post(
        "https://api.pushover.net/1/messages.json",
        data={
            "token": os.getenv("PUSHOVER_TOKEN"),
            "user": os.getenv("PUSHOVER_USER"),
            "message": text,
        }
    )


def record_user_details(email, name="Name not provided", notes="not provided"):
    """
    記錄有興趣保持聯繫的使用者資訊。
    """
    push(f"Recording user: {name} with email {email} and notes {notes}")
    return {"recorded": "ok", "message": f"Successfully recorded details for {name}."}

def record_unknown_question(question):
    """
    記錄模型無法回答的問題，以便後續審查。
    """
    push(f"Recording unknown question: {question}")
    return {"recorded": "ok", "message": "Question noted for future reference."}

# --- 您的工具定義 (JSON Schema) ---
record_user_details_json = {
    "name": "record_user_details",
    "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
    "parameters": {
        "type": "object",
        "properties": {
            "email": {
                "type": "string",
                "description": "The email address of this user"
            },
            "name": {
                "type": "string",
                "description": "The user's name, if they provided it"
            }
            ,
            "notes": {
                "type": "string",
                "description": "Any additional information about the conversation that's worth recording to give context"
            }
        },
        "required": ["email"],
        "additionalProperties": False
    }
}

record_unknown_question_json = {
    "name": "record_unknown_question",
    "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
    "parameters": {
        "type": "object",
        "properties": {
            "question": {
                "type": "string",
                "description": "The question that couldn't be answered"
            },
        },
        "required": ["question"],
        "additionalProperties": False
    }
}

tools = [{"type": "function", "function": record_user_details_json},
        {"type": "function", "function": record_unknown_question_json}]


class Me:

    def __init__(self):
        # 設定 base_url 以使用 OpenAI 函式庫呼叫 Gemini API
        self.openai = OpenAI(
            base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
        )
        self.name = "Rika Choi"
        
        # 確保 'me' 資料夾和必要的檔案存在
        try:
            reader = PdfReader("me/linkedin.pdf")
            self.linkedin = ""
            for page in reader.pages:
                text = page.extract_text()
                if text:
                    self.linkedin += text
            with open("me/summary.txt", "r", encoding="utf-8") as f:
                self.summary = f.read()
        except FileNotFoundError as e:
            print(f"Error: Required file not found. Please ensure the 'me' folder contains 'linkedin.pdf' and 'summary.txt'. Error: {e}")
            self.linkedin = "LinkedIn profile data missing."
            self.summary = "Summary data missing."


    def handle_tool_call(self, tool_calls):
        """
        處理模型發出的工具呼叫，執行對應的函式並返回結果。
        """
        results = []
        for tool_call in tool_calls:
            tool_name = tool_call.function.name
            arguments = json.loads(tool_call.function.arguments)
            print(f"Tool called: {tool_name}", flush=True)
            tool = globals().get(tool_name)
            result = tool(**arguments) if tool else {"error": f"Tool {tool_name} not found"}
            
            # 準備工具調用的結果格式
            results.append({
                "role": "tool",
                "content": json.dumps(result),
                "tool_call_id": tool_call.id
            })
        return results
    
    def system_prompt(self):
        """
        根據個人資料生成系統提示詞。
        """
        system_prompt = f"You are acting as {self.name}. You are answering questions on {self.name}'s website, \
particularly questions related to {self.name}'s career, background, skills and experience. \
Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. \
You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. \
Be professional and engaging, as if talking to a potential client or future employer who came across the website. \
If you don't know the answer to any question, use your record_unknown_question tool to record the question that you couldn't answer, even if it's about something trivial or unrelated to career. \
If the user is engaging in discussion, try to steer them towards getting in touch via email; ask for their email and record it using your record_user_details tool. "

        system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
        system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
        return system_prompt
    
    def chat(self, message, history):
        """
        與模型進行對話，處理對話歷史和工具呼叫。
        修正了 Gradio 歷史記錄到 API 訊息格式的轉換。
        """
        
        # 🌟 關鍵修正: 轉換 Gradio 的歷史記錄格式
        # Gradio 的 history 是 [(user_msg, assistant_msg), ...] 的元組列表
        converted_history = []
        for human, ai in history:
            # 1. 加入使用者訊息
            converted_history.append({"role": "user", "content": human})
            # 2. 加入 AI 訊息 (如果存在)
            if ai is not None:
                converted_history.append({"role": "assistant", "content": ai})
        
        # 建立完整的 messages 列表
        messages = (
            [{"role": "system", "content": self.system_prompt()}] + 
            converted_history + 
            [{"role": "user", "content": message}]
        )
        
        done = False
        while not done:
            # 呼叫 Gemini API
            response = self.openai.chat.completions.create(
                model="gemini-2.5-flash",  
                messages=messages, 
                tools=tools
            )
            
            # 處理工具調用 (Tool Calling)
            if response.choices[0].finish_reason=="tool_calls":
                message = response.choices[0].message
                tool_calls = message.tool_calls
                results = self.handle_tool_call(tool_calls)
                messages.append(message)
                messages.extend(results)
            else:
                done = True
        return response.choices[0].message.content
    
if __name__ == "__main__":
    me = Me()
    
    # 🌟 Gradio 介面美化和介紹資訊
    intro_markdown = f"""
    <div style="text-align: center;">
        <h1 style="color: #0047b3;">💼 與 {me.name} (徐可瑜) 的 AI 助手對話</h1>
        <p>嗨！我是Rika，專門協助企業把創新支出變成可節稅的費用，也讓智慧財產有法律的後盾。</p>
        <hr>
    </div>

    ## ✨ 擅長領域
    
    * **稅務投抵輔導:** 研發、智機、資安、AI及節能減碳支出之稅務抵減輔導服務。
    * **TIPS智財管理:** 智財管理制度建置之輔導及諮詢服務。
    * **專利商標申請:** 國內外專利申請服務、國內外商標申請服務。
    * **資格:** 中華民國專利師、TIPS智財管理制度自評員、ISO27001:2022資訊安全管理系統主導稽核員。
    ---
    """
    
    # 使用 gr.Blocks 來組織 Markdown 和 ChatInterface
    with gr.Blocks(title=f"{me.name} AI Chatbot") as demo:
        # 使用 Markdown 顯示介紹資訊
        gr.Markdown(intro_markdown) 
        
        # 創建 ChatInterface
        gr.ChatInterface(
            me.chat, 
            title="請開始提問！",
            theme="soft", 
        )
        
    demo.launch()