File size: 3,083 Bytes
cd63278 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from openai import OpenAI
from transformers.utils.versions import require_version
require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
def calculate_gpa(grades: list[str], hours: list[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 round(total_score / total_hour, 2)
def main():
client = OpenAI(
api_key="{}".format(os.getenv("API_KEY", "0")),
base_url="http://localhost:{}/v1".format(os.getenv("API_PORT", 8000)),
)
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"],
},
},
}
]
tool_map = {"calculate_gpa": calculate_gpa}
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)
if result.choices[0].message.tool_calls is None:
raise ValueError("Cannot retrieve function call from the response.")
messages.append(result.choices[0].message)
tool_call = result.choices[0].message.tool_calls[0].function
print(tool_call)
# Function(arguments='{"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}', name='calculate_gpa')
name, arguments = tool_call.name, json.loads(tool_call.arguments)
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 the grades and credit hours you provided, your Grade Point Average (GPA) is 3.42.
if __name__ == "__main__":
main()
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