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Create main.py
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import os
import random
from typing import List, Tuple
from fastapi import FastAPI, Form, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from huggingface_hub import InferenceClient
# Initialize FastAPI app
app = FastAPI()
# Allow CORS for your frontend application
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Change this to your frontend's URL in production
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Initialize Hugging Face Inference Client
client = InferenceClient("mistralai/Mistral-Nemo-Instruct-2407")
def format_prompt(message: str, history: List[Tuple[str, str]]) -> str:
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
@app.post("/generate/")
async def generate(
prompt: str = Form(...),
history: str = Form(...),
temperature: float = Form(0.9),
max_new_tokens: int = Form(512),
top_p: float = Form(0.95),
repetition_penalty: float = Form(1.0)
):
try:
# Parse history from JSON string to list of tuples
chat_history = eval(history)
# Format the prompt
formatted_prompt = format_prompt(prompt, chat_history)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=random.randint(0, 10**7),
)
# Generate text using the model
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
return JSONResponse(content={"response": output})
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))