app.py

import gradio as gr
import gradio_client
from gradio_client import Client 



def chat_api_interface(messages, model, frequency_penalty=0, logit_bias=None, max_tokens=None, n=1, presence_penalty=0, response_format="text", seed=None, stop=None, stream=False, temperature=1, top_p=1, tools=None, tool_choice="none", user=None):
    # PASS
    # PASS
    return "API response here"

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            messages = gr.Textbox(label="Messages (JSON format)", placeholder="Enter messages as a JSON array")
            model = gr.Textbox(label="Model ID", placeholder="Enter the model ID")
            frequency_penalty = gr.Number(label="Frequency Penalty", value=0)
            logit_bias = gr.Textbox(label="Logit Bias (JSON format)", placeholder="Enter logit bias as JSON")
            max_tokens = gr.Number(label="Max Tokens", optional=True)
            n = gr.Number(label="N", value=1)
            presence_penalty = gr.Number(label="Presence Penalty", value=0)
            response_format = gr.Radio(label="Response Format", choices=["text", "json_object"], value="text")
            seed = gr.Number(label="Seed", optional=True)
            stop = gr.Textbox(label="Stop Sequence(s)", placeholder="Enter stop sequences")
            stream = gr.Checkbox(label="Stream", value=False)
            temperature = gr.Slider(label="Temperature", minimum=0, maximum=2, value=1)
            top_p = gr.Slider(label="Top P", minimum=0, maximum=1, value=1)
            tools = gr.Textbox(label="Tools (JSON format)", optional=True, placeholder="Enter tools as JSON")
            tool_choice = gr.Textbox(label="Tool Choice", value="none", placeholder="Enter tool choice")
            user = gr.Textbox(label="User", optional=True, placeholder="Enter user identifier")

        with gr.Column():
            submit_btn = gr.Button("Submit")
            output = gr.Textbox(label="API Response", readonly=True)

    submit_btn.click(
        chat_api_interface, 
        inputs=[messages, model, frequency_penalty, logit_bias, max_tokens, n, presence_penalty, response_format, seed, stop, stream, temperature, top_p, tools, tool_choice, user],
        outputs=output
    )

demo.launch()