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import os |
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import gradio as gr |
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from openai import OpenAI |
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from huggingface_hub import InferenceClient |
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from tenacity import retry, wait_random_exponential, stop_after_attempt |
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OPENAI_KEY = os.getenv("OPENAI_KEY") |
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client = OpenAI(api_key=OPENAI_KEY) |
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def get_current_weather(location, unit="celsius"): |
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"""Get the current weather in a given location""" |
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if "taipei" in location.lower(): |
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return json.dumps({"location": "Taipei", "temperature": "10", "unit": unit}) |
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else: |
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return json.dumps({"location": location, "temperature": "unknown"}) |
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@retry(wait=wait_random_exponential(multiplier=1, max=40), stop=stop_after_attempt(3)) |
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def chat_completion_request(messages, tools=None, tool_choice=None): |
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try: |
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response = client.chat.completions.create( |
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model='gpt-4o', |
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messages=messages, |
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tools=tools, |
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tool_choice=tool_choice, |
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) |
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return response |
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except Exception as e: |
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print("Unable to generate ChatCompletion response") |
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print(f"Exception: {e}") |
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return e |
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tools = [ |
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{ |
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"type": "function", |
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"function": { |
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"name": "get_current_weather", |
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"description": "Get the current weather", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"location": { |
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"type": "string", |
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"description": "The city and state, e.g. San Francisco, CA", |
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}, |
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"unit": { |
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"type": "string", |
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"enum": ["celsius", "fahrenheit"], |
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"description": "The temperature unit to use. Infer this from the users location.", |
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}, |
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}, |
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"required": ["location", "unit"], |
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}, |
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} |
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} |
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] |
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def respond( |
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message, |
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history: list[tuple[str, str]], |
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system_message, |
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): |
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messages = [{"role": "system", "content": system_message}] |
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for val in history: |
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if val[0]: |
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messages.append({"role": "user", "content": val[0]}) |
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if val[1]: |
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messages.append({"role": "assistant", "content": val[1]}) |
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messages.append({"role": "user", "content": message}) |
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response = chat_completion_request(messages, tools=tools, tool_choice='auto') |
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response_message = response.choices[0].message |
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tool_calls = response_message.tool_calls |
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if tool_calls: |
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available_functions = { |
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"get_current_weather": get_current_weather, |
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} |
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messages.append(response_message) |
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for tool_call in tool_calls: |
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function_name = tool_call.function.name |
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function_to_call = available_functions[function_name] |
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function_args = json.loads(tool_call.function.arguments) |
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function_response = function_to_call( |
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location=function_args.get("location"), |
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unit=function_args.get("unit"), |
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) |
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messages.append( |
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{ |
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"tool_call_id": tool_call.id, |
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"role": "tool", |
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"name": function_name, |
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"content": function_response, |
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} |
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) |
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second_response = chat_completion_request(messages) |
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print(second_response) |
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return second_response |
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""" |
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface |
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""" |
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demo = gr.ChatInterface( |
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respond, |
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additional_inputs=[ |
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gr.Textbox(value="", label="System message"), |
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider( |
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minimum=0.1, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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label="Top-p (nucleus sampling)", |
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), |
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], |
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) |
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if __name__ == "__main__": |
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demo.launch() |
