| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import os | |
| import time | |
| import asyncio | |
| from pipeline import PromptEnhancer | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| async def advancedPromptPipeline(InputPrompt): | |
| model="gpt-4o-mini" | |
| if model == "gpt-4o": | |
| i_cost=5/10**6 | |
| o_cost=15/10**6 | |
| elif model == "gpt-4o-mini": | |
| i_cost=0.15/10**6 | |
| o_cost=0.6/10**6 | |
| enhancer = PromptEnhancer(model) | |
| start_time = time.time() | |
| advanced_prompt = await enhancer.enhance_prompt(input_prompt, perform_eval=False) | |
| elapsed_time = time.time() - start_time | |
| yield advanced_prompt["advanced_prompt"] | |
| #return { | |
| #"model": model, | |
| #"elapsed_time": elapsed_time, | |
| #"prompt_tokens": enhancer.prompt_tokens, | |
| #"completion_tokens": enhancer.completion_tokens, | |
| #"approximate_cost": (enhancer.prompt_tokens*i_cost)+(enhancer.completion_tokens*o_cost), | |
| #"inout_prompt": input_prompt, | |
| #"advanced_prompt": advanced_prompt["advanced_prompt"], | |
| } | |
| def respond( | |
| message, | |
| #history: list[tuple[str, str]], | |
| #system_message, | |
| #max_tokens, | |
| #temperature, | |
| #top_p, | |
| ): | |
| #messages = [{"role": "system", "content": system_message}] | |
| #for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # | |
| #messages.append({"role": "user", "content": message}) | |
| messages = [] | |
| response = "" | |
| advancedPromptPipeline(InputPrompt) | |
| #for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| #): | |
| # token = message.choices[0].delta.content | |
| # response += token | |
| # yield response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| demo = gr.ChatInterface( | |
| advancedPromptPipeline, | |
| #respond, | |
| #additional_inputs=[ | |
| #gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| #gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| #gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| #gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)", | |
| # ), | |
| #], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |