Перепиши этот код под использование данной апи
```
api_name: /predict
copy
from gradio_client import Client

client = Client("yuntian-deng/o1")
result = client.predict(
                inputs="Hello!!",
                top_p=1,
                temperature=1,
                chat_counter=0,
                chatbot=[],
                api_name="/predict"
)
print(result)
Accepts 5 parameters:
inputs str Required

The input value that is provided in the "Type an input and press Enter" Textbox component.

top_p float Default: 1

The input value that is provided in the "Top-p (nucleus sampling)" Slider component.

temperature float Default: 1

The input value that is provided in the "Temperature" Slider component.

chat_counter float Default: 0

The input value that is provided in the "parameter_16" Number component.

chatbot Tuple[str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None, str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None] Default: []

The input value that is provided in the "parameter_4" Chatbot component.

Returns tuple of 4 elements
[0] Tuple[str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None, str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None]

The output value that appears in the "value_4" Chatbot component.

[1] float

The output value that appears in the "value_16" Number component.

[2] str

The output value that appears in the "Status code from OpenAI server" Textbox component.

[3] str

The output value that appears in the "Type an input and press Enter" Textbox component.
```
main.py
```
import gradio as gr
import os
import sys
import json
import copy
import requests
import random
import timeout_decorator
from tenacity import retry, wait_fixed, stop_after_attempt

MODEL = "o1-preview"
API_URL = os.getenv("API_URL")
DISABLED = os.getenv("DISABLED") == 'True'
OPENAI_API_KEYS = os.getenv("OPENAI_API_KEYS").split(',')
print (API_URL)
print (OPENAI_API_KEYS)
NUM_THREADS = int(os.getenv("NUM_THREADS"))

print (NUM_THREADS)

#@timeout_decorator.timeout(120)
@retry(stop=stop_after_attempt(5), wait=wait_fixed(2))
def call_openai_api(payload, headers):
    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
    response.raise_for_status()
    return response

def exception_handler(exception_type, exception, traceback):
    print("%s: %s" % (exception_type.__name__, exception))
sys.excepthook = exception_handler
sys.tracebacklimit = 0
    
def predict(inputs, top_p, temperature, chat_counter, chatbot, history, request:gr.Request):
    orig_history = copy.deepcopy(history)
    orig_chat_counter = chat_counter
    payload = {
        "model": MODEL,
        "messages": [{"role": "user", "content": f"{inputs}"}],
        "temperature" : 1.0,
        "top_p":1.0,
        "n" : 1,
        "stream": True,
        "presence_penalty":0,
        "frequency_penalty":0,
    }
    OPENAI_API_KEY = random.choice(OPENAI_API_KEYS)
    print (OPENAI_API_KEY)
    headers_dict = {key.decode('utf-8'): value.decode('utf-8') for key, value in request.headers.raw}
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {OPENAI_API_KEY}",
        "Headers": f"{headers_dict}"
    }

    # print(f"chat_counter - {chat_counter}")
    if chat_counter != 0 :
        messages = []
        for i, data in enumerate(history):
            if i % 2 == 0:
                role = 'user'
            else:
                role = 'assistant'
            message = {}
            message["role"] = role
            message["content"] = data
            messages.append(message)
        
        message = {}
        message["role"] = "user" 
        message["content"] = inputs
        messages.append(message)
        payload = {
            "model": MODEL,
            "messages": messages,
            "temperature" : temperature,
            "top_p": top_p,
            "n" : 1,
            "stream": True,
            "presence_penalty":0,
            "frequency_penalty":0,
        }

    chat_counter += 1

    history.append(inputs)
    token_counter = 0 
    partial_words = "" 
    counter = 0

    try:
        # make a POST request to the API endpoint using the requests.post method, passing in stream=True
        response = call_openai_api(payload, headers) #requests.post(API_URL, headers=headers, json=payload, stream=True)
        response_code = f"{response}"
        #if response_code.strip() != "<Response [200]>":
        #    #print(f"response code - {response}")
        #    raise Exception(f"Sorry, hitting rate limit. Please try again later. {response}")
        
        for chunk in response.iter_lines():
            #print (chunk)
            #sys.stdout.flush()
            #Skipping first chunk
            if counter == 0:
                counter += 1
                continue
                #counter+=1
            # check whether each line is non-empty
            if chunk.decode() :
                chunk = chunk.decode()
                # decode each line as response data is in bytes
                if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
                    partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
                    if token_counter == 0:
                        history.append(" " + partial_words)
                    else:
                        history[-1] = partial_words
                    token_counter += 1
                    yield [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=False), gr.update(interactive=False)  # resembles {chatbot: chat, state: history}  
    except Exception as e:
        print (f'error found: {e}')
        return [(orig_history[i], orig_history[i + 1]) for i in range(0, len(orig_history) - 1, 2) ], orig_history, orig_chat_counter, 'Error! Please try again', gr.update(interactive=True), gr.update(interactive=True)
        

    yield [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=True), gr.update(interactive=True)
    print(json.dumps({"chat_counter": chat_counter, "payload": payload, "partial_words": partial_words, "token_counter": token_counter, "counter": counter}))
                   

def reset_textbox():
    return gr.update(value='', interactive=False), gr.update(interactive=False)

title = """<h1 align="center">OpenAI-O1-Preview: Research Preview (Short-Term Availability)</h1>"""
if DISABLED:
    title = """<h1 align="center" style="color:red">This app has reached OpenAI's usage limit. Please check back tomorrow.</h1>"""
description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
```
User: <utterance>
Assistant: <utterance>
User: <utterance>
Assistant: <utterance>
...
```
In this app, you can explore the outputs of a gpt-4 turbo LLM.
"""

theme = gr.themes.Default(primary_hue="green")                

with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
                #chatbot {height: 520px; overflow: auto;}""",
              theme=theme) as demo:
    gr.HTML(title)
    gr.HTML("""<h3 align="center" style="color: red;">If this app doesn't respond, consider trying our O1-mini app:<br/><a href="https://huggingface.co/spaces/yuntian-deng/o1mini">https://huggingface.co/spaces/yuntian-deng/o1mini</a></h3>""")

    #gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
    with gr.Column(elem_id = "col_container", visible=False) as main_block:
        #GPT4 API Key is provided by Huggingface 
        #openai_api_key = gr.Textbox(type='password', label="Enter only your GPT4 OpenAI API key here")
        chatbot = gr.Chatbot(elem_id='chatbot') #c
        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
        state = gr.State([]) #s
        with gr.Row():
            with gr.Column(scale=7):
                b1 = gr.Button(visible=not DISABLED) #.style(full_width=True)
            with gr.Column(scale=3):
                server_status_code = gr.Textbox(label="Status code from OpenAI server", )
    
        #inputs, top_p, temperature, top_k, repetition_penalty
        with gr.Accordion("Parameters", open=False):
            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
            #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
            #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
            chat_counter = gr.Number(value=0, visible=False, precision=0)
    
    with gr.Column(elem_id = "user_consent_container") as user_consent_block:
        # Get user consent
        accept_checkbox = gr.Checkbox(visible=False)
        js = "(x) => confirm('By clicking \"OK\", I agree that my data may be published or shared.')"
        with gr.Accordion("User Consent for Data Collection, Use, and Sharing", open=True):
            gr.HTML("""
            <div>
                <p>By using our app, which is powered by OpenAI's API, you acknowledge and agree to the following terms regarding the data you provide:</p>
                <ol>
                    <li><strong>Collection:</strong> We may collect information, including the inputs you type into our app, the outputs generated by OpenAI's API, and certain technical details about your device and connection (such as browser type, operating system, and IP address) provided by your device's request headers.</li>
                    <li><strong>Use:</strong> We may use the collected data for research purposes, to improve our services, and to develop new products or services, including commercial applications, and for security purposes, such as protecting against unauthorized access and attacks.</li>
                    <li><strong>Sharing and Publication:</strong> Your data, including the technical details collected from your device's request headers, may be published, shared with third parties, or used for analysis and reporting purposes.</li>
                    <li><strong>Data Retention:</strong> We may retain your data, including the technical details collected from your device's request headers, for as long as necessary.</li>
                </ol>
                <p>By continuing to use our app, you provide your explicit consent to the collection, use, and potential sharing of your data as described above. If you do not agree with our data collection, use, and sharing practices, please do not use our app.</p>
            </div>
            """)
            accept_button = gr.Button("I Agree")

        def enable_inputs():
            return gr.update(visible=False), gr.update(visible=True)

    accept_button.click(None, None, accept_checkbox, js=js, queue=False)
    accept_checkbox.change(fn=enable_inputs, inputs=[], outputs=[user_consent_block, main_block], queue=False)

    inputs.submit(reset_textbox, [], [inputs, b1], queue=False)
    inputs.submit(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],)  #openai_api_key
    b1.click(reset_textbox, [], [inputs, b1], queue=False)
    b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],)  #openai_api_key
             
    demo.queue(max_size=20, default_concurrency_limit=NUM_THREADS, api_open=False).launch(share=False)
```