import time
import gradio as gr
import openai
import os
import requests
import json

# 從 Hugging Face secrets 中讀取 OpenAI API 金鑰
api_key = os.getenv('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')
if not api_key:
    raise ValueError("請設置 'OPENAI_API_KEY' 環境變數")

# OpenAI API key
openai_api_key = api_key

# 將 Gradio 的歷史紀錄轉換為 OpenAI 格式
def transform_history(history):
    new_history = []
    for chat in history:
        new_history.append({"role": "user", "content": chat[0]})
        new_history.append({"role": "assistant", "content": chat[1]})
    return new_history

# 回應生成函數，使用 requests 來呼叫 OpenAI API
def response(message, history):
    global conversation_history

    # 將 Gradio 的歷史紀錄轉換為 OpenAI 的格式
    conversation_history = transform_history(history)

    url = "https://api.openai.com/v1/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {openai_api_key}"
    }

    # 設置初始的 prompt_instruction
    prompt_instruction = """
    你是廖老師的專業小助教，名字叫做 '小確' ，要以專業、熱情、善解人意且非常有禮貌，親切的的口氣，與用戶互動並解答問題:
    """
    prompt_to_gpt = prompt_instruction + message

    # 新增至 conversation_history
    conversation_history.append({"role": "system", "content": prompt_to_gpt})

    # 設置請求的數據
    data = {
        "model": "gpt-4o",  # 確認使用的模型是 gpt-4 或 gpt-3.5-turbo
        "messages": conversation_history,
        "max_tokens": 200  # 控制生成的最大令牌數
    }

    # 發送請求到 OpenAI API
    response = requests.post(url, headers=headers, data=json.dumps(data))

    # 處理回應
    response_json = response.json()

    # 提取模型的回應並加入歷史紀錄
    if 'choices' in response_json and len(response_json['choices']) > 0:
        model_response = response_json['choices'][0]['message']['content']
        conversation_history.append({"role": "assistant", "content": model_response})

        # 逐字回傳生成的文字，實現打字機效果
        for i in range(len(model_response)):
            time.sleep(0.05)  # 每個字符間隔 0.05 秒
            yield model_response[: i+1]
    else:
        yield "Error: No response from the model."

# 建立 Gradio 聊天界面
gr.ChatInterface(response,
                 title='OpenAI Chat',
                 textbox=gr.Textbox(placeholder="Question to OpenAI")).launch(share=True)