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from fastapi import FastAPI, HTTPException | |
from pydantic import BaseModel | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig | |
from peft import PeftModel | |
from langchain.memory import ConversationBufferWindowMemory | |
from fastapi.middleware.cors import CORSMiddleware | |
app = FastAPI() | |
# Add CORSMiddleware to the application | |
app.add_middleware( | |
CORSMiddleware, | |
allow_origins=["*"], | |
allow_credentials=True, | |
allow_methods=["*"], | |
allow_headers=["*"], | |
) | |
bnb_config = BitsAndBytesConfig( | |
load_in_4bit=True, | |
bnb_4bit_use_double_quant=True, | |
bnb_4bit_quant_type="nf4", | |
bnb_4bit_compute_dtype=torch.bfloat16, | |
) | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
base_model = "mistralai/Mistral-7B-Instruct-v0.2" | |
tokenizer = AutoTokenizer.from_pretrained(base_model, pad_token="[PAD]") | |
model = AutoModelForCausalLM.from_pretrained( | |
base_model, | |
quantization_config=bnb_config, | |
device_map="auto", | |
trust_remote_code=True, | |
) | |
ft_model = PeftModel.from_pretrained(model, "nuratamton/story_sculptor_mistral").eval() | |
memory = ConversationBufferWindowMemory(k=10) | |
class UserRequest(BaseModel): | |
message: str | |
async def generate_text(request: UserRequest): | |
user_in = request.message | |
if user_in.lower() in ["adventure", "mystery", "horror", "sci-fi"]: | |
memory.clear() | |
if user_in.lower() == "quit": | |
raise HTTPException(status_code=400, detail="User requested to quit") | |
memory_context = memory.load_memory_variables({})["history"] | |
user_input = f"{memory_context}[INST] Continue the game and maintain context: {user_in}[/INST]" | |
encodings = tokenizer(user_input, return_tensors="pt", padding=True).to(device) | |
input_ids, attention_mask = encodings["input_ids"], encodings["attention_mask"] | |
output_ids = ft_model.generate( | |
input_ids, | |
attention_mask=attention_mask, | |
max_new_tokens=1000, | |
num_return_sequences=1, | |
do_sample=True, | |
temperature=1.1, | |
top_p=0.9, | |
repetition_penalty=1.2, | |
) | |
generated_ids = output_ids[0, input_ids.shape[-1] :] | |
response = tokenizer.decode(generated_ids, skip_special_tokens=True) | |
memory.save_context({"input": user_in}, {"output": response}) | |
response = response.replace("AI: ", "") | |
# response = response.replace("Human: ", "") | |
return {"response": response} | |
def read_root(): | |
return {"message": "Hello from FastAPI"} | |