Initial commit

Author: Simoon-F

.DS_Store

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+# Auto detect text files and perform LF normalization
+* text=auto

.gitattributes

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+.env

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+# OpenAi Function Calling Use Examples
+
+A simple example that demonstrates how to use the function call feature of the OpenAI API
+
+## Libraries that need to be installed
+
+1. openai
+2. streamlit
+3. python-dotenv
+
+## installation
+
+1. Clone the current repository
+
+```shell
+$ git clone https://github.com/Simoon-F/openai-function-calling-use-exaples
+```
+
+2. Enter the project root directory
+
+```shell
+$ cd openai-function-calling-use-exaples
+```
+
+3. Create the `.env` file and add your api key
+
+4. Run Project
+```shell
+$ streamlit run main.py
+```
+
+[function-calling-and-other-api-updates](https://openai.com/blog/function-calling-and-other-api-updates)

README.md

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+import streamlit as st
+import openai
+import json
+import requests as rq
+from dotenv import dotenv_values
+
+
+class SkillsList:
+    def __init__(self):
+        """
+        The api of Gaud Open Platform is used here.
+        https://lbs.amap.com/api/webservice/guide/api/weatherinfo
+        """
+        self.weather_api_url = "https://restapi.amap.com/v3/weather/weatherInfo"
+        self.amap_api_key = env['AMAP_API_KEY']
+
+    def query_city_weather(self, city):
+        """
+        Query the weather temperature of the city.
+
+        Args:
+            city (str): Cities that should be queried.
+        """
+        params = {
+            "key": self.amap_api_key,
+            "city": city,
+            "output": "json",
+            "extensions": "all",
+        }
+
+        response = rq.get(self.weather_api_url, params=params)
+
+        response.raise_for_status()
+
+        weather_data = response.json()
+
+        for item in weather_data['forecasts']:
+            st.markdown(f"{item['province'] + item['city']} is as follows：")
+            for cast in item['casts']:
+                st.markdown(
+                    f"**{cast['date']}**  ：`dayweather`：{cast['dayweather']}，`nightweather`：{cast['nightweather']}, `daytemp`: {cast['daytemp']}, `nighttemp`：{cast['nighttemp']}")
+
+
+def call_gpt(user_input):
+    """
+    Make a ChatCompletion API call to OpenAI GPT-3.5-turbo model.
+
+    Args:
+        user_input (str): The user's prompt or input text.
+
+    Returns:
+        str: The generated response from the API call.
+    """
+    messages = [{"role": "user", "content": user_input}]
+
+    function = {
+        "name": "query_city_weather",
+        "description": "query weather temperature",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "city": {
+                    "type": "string",
+                    "description": "The city",
+                },
+            },
+            "required": ["city"],
+        },
+    }
+
+    completion = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-0613",
+        messages=messages,
+        functions=[function],
+        function_call="auto",
+    )
+    return completion.choices[0].message
+
+
+if __name__ == "__main__":
+    st.title("Small assistant")
+
+    env = dotenv_values()
+    openai.api_key = env['OPENAI_API_KEY']
+
+    skills_list_obj = SkillsList()
+
+    prompt = st.text_input("Enter your command:")
+
+    if prompt:
+        reply_content = call_gpt(prompt)
+
+        reply_content_dict = reply_content.to_dict()
+        method_name = reply_content_dict['function_call']['name']
+        method_args = reply_content_dict['function_call']['arguments']
+
+        print(method_name, method_args)
+
+        method_args_dict = json.loads(method_args)
+
+        method = getattr(skills_list_obj, method_name)
+        method(method_args_dict['city'])