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import json | |
import os | |
from typing import Sequence | |
from openai import OpenAI | |
os.environ["OPENAI_BASE_URL"] = "http://192.168.0.1:8000/v1" | |
os.environ["OPENAI_API_KEY"] = "0" | |
def calculate_gpa(grades: Sequence[str], hours: Sequence[int]) -> float: | |
grade_to_score = {"A": 4, "B": 3, "C": 2} | |
total_score, total_hour = 0, 0 | |
for grade, hour in zip(grades, hours): | |
total_score += grade_to_score[grade] * hour | |
total_hour += hour | |
return total_score / total_hour | |
tool_map = {"calculate_gpa": calculate_gpa} | |
if __name__ == "__main__": | |
client = OpenAI() | |
tools = [ | |
{ | |
"type": "function", | |
"function": { | |
"name": "calculate_gpa", | |
"description": "Calculate the Grade Point Average (GPA) based on grades and credit hours", | |
"parameters": { | |
"type": "object", | |
"properties": { | |
"grades": {"type": "array", "items": {"type": "string"}, "description": "The grades"}, | |
"hours": {"type": "array", "items": {"type": "integer"}, "description": "The credit hours"}, | |
}, | |
"required": ["grades", "hours"], | |
}, | |
}, | |
} | |
] | |
messages = [] | |
messages.append({"role": "user", "content": "My grades are A, A, B, and C. The credit hours are 3, 4, 3, and 2."}) | |
result = client.chat.completions.create(messages=messages, model="test", tools=tools) | |
tool_call = result.choices[0].message.tool_calls[0].function | |
name, arguments = tool_call.name, json.loads(tool_call.arguments) | |
messages.append( | |
{"role": "function", "content": json.dumps({"name": name, "argument": arguments}, ensure_ascii=False)} | |
) | |
tool_result = tool_map[name](**arguments) | |
messages.append({"role": "tool", "content": json.dumps({"gpa": tool_result}, ensure_ascii=False)}) | |
result = client.chat.completions.create(messages=messages, model="test", tools=tools) | |
print(result.choices[0].message.content) | |
# Based on your grades and credit hours, your calculated Grade Point Average (GPA) is 3.4166666666666665. | |