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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 {
"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})
response = ""
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() |