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 = "" for user_prompt, bot_response in history: prompt += f"[INST] {user_prompt} [/INST]" prompt += f" {bot_response} " 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))