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import os
import json
from openai import OpenAI
import gradio as gr
from my_calling_functions import FUNCTIONS, get_trending_searches, google_res
def llm_calls(country, output_lang):
# OpenAI API client
client = OpenAI() # 無參數，即自動抓取環境變數
LLM_DEPLOYMENT_NAME = os.getenv('LLM_DEPLOYMENT_NAME')
# 定義query內容
# query = f"{country}的今日熱門關鍵字有哪些，選擇一個關鍵字，選擇的方式以新聞事件或人名為優先，其次是趣味性，並以google搜尋，了解此關鍵字內容，根據內容用{output_lang}撰寫一篇100字社群貼文，第一行為標題，適度的使用表情符號作為項目符號或表達情感，並在結尾加上三個hashtag"
query = f"Identify today's trending keywords in {country}. Select one keyword, prioritizing based on news events or personal names, \
followed by interesting or entertaining topics. Use Google search to understand the context of the selected keyword. \
Based on the information, write a 100-word social media post in {output_lang}, starting with a headline. \
Use emojis appropriately as bullet points or to express emotions, and conclude the post with three hashtags."
# 第一次呼叫OpenAI API，取回需要執行的function和參數，正解應為get_trending_searches和三個參數
response_1 = client.chat.completions.create(
model=LLM_DEPLOYMENT_NAME, # 特定模型版本才有支援function calling
messages=[{"role":"user", "content": query}],
tools=FUNCTIONS,
tool_choice="required"
)
print('===== response_1 =====')
print(response_1)
print('===== response_1.choices[0].message.tool_calls[0] =====')
print(response_1.choices[0].message.tool_calls[0])
# 為下一次呼叫OpenAI API準備function call和function call的結果
# 因為OpenAI API不接受response.choices[0].message裡面的某些key，所以用此function保留它接受的
def get_tool_message(response):
tool_msg = response.choices[0].message
tool_msg_dict = tool_msg.model_dump()
clean_tool_msg_dict = {
'role': tool_msg_dict['role'],
'content': tool_msg_dict['content'],
'tool_calls': tool_msg_dict['tool_calls']
}
return clean_tool_msg_dict
tool_msg_1 = get_tool_message(response_1)
print("===== tool_msg_1 =====")
print(tool_msg_1)
# 依照tool_msg_1的指示執行function
country_name = json.loads(tool_msg_1['tool_calls'][0]['function']['arguments'])['country_name']
hl = json.loads(tool_msg_1['tool_calls'][0]['function']['arguments'])['hl']
tz = json.loads(tool_msg_1['tool_calls'][0]['function']['arguments'])['tz']
function_call_result_1 = get_trending_searches(country_name, hl, tz)
# 做成OpenAI API要的格式
function_call_result_message_1 = {
"role": "tool",
"content": json.dumps(function_call_result_1, ensure_ascii=False),
"tool_call_id": tool_msg_1['tool_calls'][0]['id'],
}
print("===== function_call_result_message_1 =====")
print(function_call_result_message_1)
# 第二次呼叫OpenAI API，取回需要執行的function和參數，正解應為google_res和一個參數
response_2 = client.chat.completions.create(
model=LLM_DEPLOYMENT_NAME,
messages=[
{"role":"user", "content": query},
get_tool_message(response_1), # 附上第一次呼叫的資訊
function_call_result_message_1,
],
tools=FUNCTIONS,
tool_choice="required"
)
print('===== response_2 =====')
print(response_2.choices[0].message)
# 為下一次呼叫OpenAI API準備function call和function call的結果
# 因為OpenAI API不接受response.choices[0].message裡面的某些key，所以用此function保留它接受的
tool_msg_2 = get_tool_message(response_2)
print("===== tool_msg_2 =====")
print(tool_msg_2)
# 依照tool_msg_2的指示執行function
search_keyword = json.loads(tool_msg_2['tool_calls'][0]['function']['arguments'])['search_keyword']
function_call_result_2 = google_res(search_keyword)
# 做成OpenAI API要的格式
function_call_result_message_2 = {
"role": "tool",
"content": json.dumps(function_call_result_2, ensure_ascii=False),
"tool_call_id": tool_msg_2['tool_calls'][0]['id'],
}
print("===== function_call_result_message_2 =====")
print(function_call_result_message_2)
# 第三次呼叫OpenAI API，根據前兩次呼叫的所獲得的資訊，得出最終產出結果
response_3 = client.chat.completions.create(
model=LLM_DEPLOYMENT_NAME,
messages=[
{"role":"user", "content": query},
# 附上第一和第二次呼叫的資訊
get_tool_message(response_1),
function_call_result_message_1,
get_tool_message(response_2),
function_call_result_message_2,
],
)
print('===== Query =====')
print(query)
print('===== response_3 =====')
print(response_3)
print('===== response_3.choices[0].message.content =====')
print(response_3.choices[0].message.content)
return response_3.choices[0].message.content
gradio_app = gr.Interface(
llm_calls,
inputs=[gr.Dropdown(
choices=[("Taiwan", "台灣"), ("Japan", "日本"), ("South Korea", "南韓"), ("Czech Republic", "捷克")], label="Country", info="Will use current trends of this country"
), gr.Dropdown(
choices=[("English", "英文"), ("Chinese", "繁體中文")],
label="Output Language", info="Will use this language to write"
)],
outputs=["textbox"],
title="Social Media Writer",
description="Generate a social media post based on the trend of the selected country, in the selected language"
)
if __name__ == "__main__":
gradio_app.launch()
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