app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Model configuration
MODEL_NAME = "deepseek-ai/DeepSeek-V3-Base"  # Hugging Face model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load model and tokenizer
try:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME,
        device_map="auto",
        trust_remote_code=True,
        low_cpu_mem_usage=True,
        revision="main"
    ).to(device)
except Exception as e:
    print(f"Error loading model: {e}")
    raise

# FastAPI app initialization
app = FastAPI()

# Input schema
class Query(BaseModel):
    input_text: str

@app.post("/predict")
async def predict(query: Query):
    input_text = query.input_text
    if not input_text:
        raise HTTPException(status_code=400, detail="Input text cannot be empty.")
    inputs = tokenizer(input_text, return_tensors="pt").to(device)
    outputs = model.generate(inputs["input_ids"], max_new_tokens=50, temperature=0.7)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"response": response}