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from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
app = FastAPI()
# نموذج مفتوح المصدر
MODEL_NAME = "TheBloke/vicuna-7B-1.1-HF"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto", torch_dtype=torch.float16)
# تخزين تاريخ المحادثة
chat_histories = {}
class ChatRequest(BaseModel):
user_id: str
message: str
@app.post("/chat")
def chat_endpoint(request: ChatRequest):
user_id = request.user_id
message = request.message
if user_id not in chat_histories:
chat_histories[user_id] = []
conversation = "NOVA AI: أنا كوميدي ومغربي. نفهم أي حاجة.\n"
for q, a in chat_histories[user_id]:
conversation += f"User: {q}\nNOVA AI: {a}\n"
conversation += f"User: {message}\nNOVA AI:"
inputs = tokenizer(conversation, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True).split("NOVA AI:")[-1].strip()
chat_histories[user_id].append((message, response))
if len(chat_histories[user_id]) > 10:
chat_histories[user_id] = chat_histories[user_id][-10:]
return {"response": response}
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