main.py

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import uvicorn
import asyncio

app = FastAPI()

client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")

class Item(BaseModel):
    prompt: str
    history: list
    system_prompt: str
    temperature: float = 0.0
    max_new_tokens: int = 1048
    top_p: float = 0.15
    repetition_penalty: float = 1.0

def format_prompt(message, history):
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt

async def generate(item: Item):
    temperature = max(float(item.temperature), 1e-2)  # Ensure temperature is not too low
    top_p = float(item.top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=item.max_new_tokens,
        top_p=top_p,
        repetition_penalty=item.repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
    
    # Stream the response from the model
    async def event_stream():
        stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
        
        async for response in stream:
            yield response.token.text  # Yield each token as it is received
            
            # Optional: Add a small delay to simulate streaming effect (if needed)
            await asyncio.sleep(0.1)

    return event_stream()

@app.post("/generate/")
async def generate_text(item: Item):
    return StreamingResponse(generate(item), media_type="text/event-stream")