import os
import json
from openai import OpenAI
from dotenv import load_dotenv
load_dotenv()

client = OpenAI()
deployment = "gpt-3.5-turbo"

student_1_description="Emily Johnson is a sophomore majoring in computer science at Duke University. She has a 3.7 GPA. Emily is an active member of the university's Chess Club and Debate Team. She hopes to pursue a career in software engineering after graduating."
 
student_2_description = "Michael Lee is a sophomore majoring in computer science at Stanford University. He has a 3.8 GPA. Michael is known for his programming skills and is an active member of the university's Robotics Club. He hopes to pursue a career in artificial intelligence after finshing his studies."

prompt1 = f'''
Please extract the following information from the given text and return it as a JSON object:

name
major
school
grades
club

This is the body of text to extract the information from:
{student_1_description}
'''


prompt2 = f'''
Please extract the following information from the given text and return it as a JSON object:

name
major
school
grades
club

This is the body of text to extract the information from:
{student_2_description}
'''

response1 = client.chat.completions.create(
    model=deployment, 
    messages=[{"role": "user", "content": prompt1}]
    )
print(response1.choices[0].message.content)

response2 = client.chat.completions.create(
    model=deployment,
    messages=[{"role": "user", "content": prompt2}]
    )
print(response2.choices[0].message.content)

import os
import json
from openai import OpenAI
from dotenv import load_dotenv
load_dotenv()

client = OpenAI()
deployment = "gpt-3.5-turbo"

# step 1 : creating messages
messages= [ {"role": "user", "content": "Find me a good course for a beginner student to learn Azure."} ]
# step 2 : creating functions
functions = [
   {
      "name":"search_courses",
      "description":"Retrieves courses from the search index based on the parameters provided",
      "parameters":{
         "type":"object",
         "properties":{
            "role":{
               "type":"string",
               "description":"The role of the learner (i.e. developer, data scientist, student, etc.)"
            },
            "product":{
               "type":"string",
               "description":"The product that the lesson is covering (i.e. Azure, Power BI, etc.)"
            },
            "level":{
               "type":"string",
               "description":"The level of experience the learner has prior to taking the course (i.e. beginner, intermediate, advanced)"
            }
         },
         "required":[
            "role"
         ]
      }
   }
]
# step 3 : Making the function call
response = client.chat.completions.create(model=deployment,
                                        messages=messages,
                                        functions=functions,
                                        function_call="auto")

print(response.choices[0].message)

import os
import json
import requests
from openai import OpenAI
from dotenv import load_dotenv
load_dotenv()

client = OpenAI()
deployment = "gpt-3.5-turbo"

# step 1 : creating messages
messages= [ {"role": "user", "content": "Find me a good course for a beginner student to learn Azure."} ]
# step 2 : creating functions
functions = [
   {
      "name":"search_courses",   # 函数名称
      "description":"Retrieves courses from the search index based on the parameters provided", # 函数功能描述，简介准确
      "parameters":{   # 函数参数
         "type":"object", # 参数的类型，为object，即字典
         "properties":{   # 参数的属性有哪些，即字典中包含哪些属性
            "role":{     # 属性名称
               "type":"string", # 属性类型
               "description":"The role of the learner (i.e. developer, data scientist, student, etc.)" # 属性描述
            },
            "product":{
               "type":"string",
               "description":"The product that the lesson is covering (i.e. Azure, Power BI, etc.)"
            },
            "level":{
               "type":"string",
               "description":"The level of experience the learner has prior to taking the course (i.e. beginner, intermediate, advanced)"
            }
         },
         "required":[  # 必须参数，即参数中必须包含的属性名称
            "role"
         ]
      }
   }
]
# step 3 : Making the function call
response = client.chat.completions.create(model=deployment,
                                        messages=messages,
                                        functions=functions,  # 把函数描述放入这里，对于LLM来说，这就是可调用的函数列表
                                        function_call="auto") # 让LLM决定何时调用函数
print("response_message:")
print(response.choices[0].message)
print()

response_message = response.choices[0].message

def search_courses(role, product, level):   # 函数的实际实现
   url = "https://learn.microsoft.com/api/catalog/"   # 从目标api获取数据
   params = {
      "role": role,
      "product": product,
      "level": level
   }
   response = requests.get(url, params=params)
   modules = response.json()["modules"]
   results = []
   for module in modules[:5]:
      title = module["title"]
      url = module["url"]
      results.append({"title": title, "url": url})
   return str(results)

# Check if the model wants to call a function
if response_message.function_call.name:  # 这里在返回值会告诉你要调用函数
   print("Recommended Function call:")
   print(response_message.function_call.name)  # 打印要调用的函数名称
   print()

   # Call the function.
   function_name = response_message.function_call.name

   available_functions = {
            "search_courses": search_courses,
   }
   function_to_call = available_functions[function_name]  

   function_args = json.loads(response_message.function_call.arguments)
   print("function_args:")
   print(function_args)
   print()
   function_response = function_to_call(**function_args) # 实际根据函数名称，调用了真的函数，**在这里的作用是将一个字典展开为关键字参数（keyword arguments）

   print("Output of function call:")
   print(function_response)
   print(type(function_response))
   print()


   # Add the assistant response and function response to the messages
   messages.append( # adding assistant response to messages
      {
            "role": response_message.role,
            "function_call": {
               "name": function_name,
               "arguments": response_message.function_call.arguments,
            },
            "content": None
      }
   )
   messages.append( # adding function response to messages
      {
            "role": "function",
            "name": function_name,
            "content":function_response,
      }
   )

print("Messages in next request:")
print(messages)
print()

second_response = client.chat.completions.create(
   messages=messages,
   model=deployment,
   function_call="auto",
   functions=functions,
   temperature=0
      )  # get a new response from GPT where it can see the function response

print(second_response.choices[0].message.content)