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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", temperature=0.0):
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, temperature)
start_time = time.time()
advanced_prompt = await enhancer.enhance_prompt(InputPrompt, perform_eval=False)
elapsed_time = time.time() - start_time
"""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"],
}"""
return 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)",
# ),
#],
#)
demo = gr.Interface(fn=advancedPromptPipeline,
inputs=[
gr.Textbox(lines=10, placeholder="Enter your prompt", label="Input Prompt", min_width=100),
gr.Radio(["gpt-4o-mini", "gpt-4o"], value="gpt-4o-mini", label="Select Model"),
gr.Slider(minimum=0.0, maximum=1.0, value=0.0, step=0.1, label="Temperature")
],
outputs=[
gr.Textbox(lines=20, label="Advanced Prompt", show_copy_button=True, autoscroll=False, min_width=220),
]
)
if __name__ == "__main__":
demo.launch() |