import gradio as gr
from openai import AsyncOpenAI

base_url = "http://127.0.0.1:8080/v1"
client = AsyncOpenAI(base_url=base_url, api_key="-")

"""
frequency_penalty: Optional[float] = None,
logit_bias: Optional[List[float]] = None,
logprobs: Optional[bool] = None,
top_logprobs: Optional[int] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
presence_penalty: Optional[float] = None,
stream: bool = False,
seed: Optional[int] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
tools: Optional[List[Tool]] = None,
tool_choice: Optional[str] = None,
"""

def _default_parameters():
    return {
        "max_tokens": 256,
        "stream": True,
        "temperature": 0.9,
    }

def _translate_messages(history):
    messages = []

    for conv in history:
        messages.append({"role":"user", "content":conv[0]})
        messages.append({"role":"assistant", "content":conv[1]})

    return messages

async def echo(message, history):
    parameters = _default_parameters()
    messages = _translate_messages(history)
    messages.append({"role":"user", "content":message})
    
    responses = await client.chat.completions.create(
        model="tgi", messages=messages, **parameters
    )

    full_resp = ""    
    async for resp in responses:
        full_resp = full_resp + resp.choices[0].delta.content
        yield full_resp

demo = gr.ChatInterface(
    fn=echo, 
    examples=["hello", "how are you?", "What is Large Language Model?"], 
    title="Space of Gradio ➕ Text Generation Inference",
    multimodal=False
)

demo.queue().launch(server_name="0.0.0.0", server_port=3000)