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import os
from typing import List, Dict
import logging
import dotenv
import torch
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse, HTMLResponse
from pydantic import BaseModel
from llama_cpp import Llama
from huggingface_hub import hf_hub_download, login
import uvicorn
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
AutoModelForCausalLM,
pipeline
)
from UofTearsBot import UofTearsBot
MODEL_REPO="bartowski/Mistral-7B-Instruct-v0.3-GGUF"
MODEL_FILE="Mistral-7B-Instruct-v0.3-Q4_K_M.gguf"
CHAT_FORMAT="mistral-instruct"
dotenv.load_dotenv()
login(token=os.getenv("HF_TOKEN"))
MODEL_PATH = hf_hub_download(
repo_id=MODEL_REPO,
filename=MODEL_FILE,
local_dir="/tmp/models",
local_dir_use_symlinks=False,
)
llm = Llama(
model_path=MODEL_PATH,
n_ctx=int(os.getenv("N_CTX", "4096")),
n_threads=os.cpu_count() or 4,
n_batch=int(os.getenv("N_BATCH", "256")),
chat_format=CHAT_FORMAT,
)
# Start the FastAPI app
app = FastAPI()
chatbots: Dict[str, UofTearsBot] = {}
class ChatRequest(BaseModel):
user_id: str
user_text: str
@app.post("/chat")
async def chat(request: ChatRequest):
if request.user_id not in chatbots:
chatbots[request.user_id] = UofTearsBot(llm)
current_bot = chatbots[request.user_id]
response = current_bot.converse(request.user_text)
return JSONResponse(content={"response": response, "history": current_bot.history})
@app.get("/", response_class=HTMLResponse)
async def home():
return "<h1>App is running 🚀</h1>"
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860) # huggingface port |