flux_app/enhance_v2.py

# flux_app/enhance.py
import time
from huggingface_hub import InferenceClient
import gradio as gr

# Initialize the inference client with the new LLM
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

# Define the system prompt for enhancing user prompts
SYSTEM_PROMPT = (
    "You are a prompt enhancer and your work is to enhance the given prompt under 100 words "
    "without changing the essence, only write the enhanced prompt and nothing else."
)

def format_prompt(message):
    """
    Format the input message using the system prompt and a timestamp to ensure uniqueness.
    """
    timestamp = time.time()
    formatted = (
        f"<s>[INST] SYSTEM: {SYSTEM_PROMPT} [/INST]"
        f"[INST] {message} {timestamp} [/INST]"
    )
    return formatted

def generate(message, max_new_tokens=256, temperature=0.9, top_p=0.95, repetition_penalty=1.0):
    """
    Generate an enhanced prompt using the new LLM.
    This function yields intermediate results as they are generated.
    """
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)
    generate_kwargs = {
        "temperature": temperature,
        "max_new_tokens": int(max_new_tokens),
        "top_p": top_p,
        "repetition_penalty": float(repetition_penalty),
        "do_sample": True,
    }
    formatted_prompt = format_prompt(message)
    stream = client.text_generation(
        formatted_prompt,
        **generate_kwargs,
        stream=True,
        details=True,
        return_full_text=False,
    )
    output = ""
    for response in stream:
        token_text = response.token.text
        output += token_text
        yield output.strip('</s>')
    return output.strip('</s>')