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from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load DeepSeek model (small one for local use)
# Try bigger models if you have a GPU with >12GB VRAM
model_name = "deepseek-ai/deepseek-coder-1.3b-instruct"
print("Loading model... this may take a minute ⏳")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto"
)
print("Model loaded ✅")
app = FastAPI()
class ChatRequest(BaseModel):
message: str
@app.post("/chat")
def chat(request: ChatRequest):
"""Chat endpoint using DeepSeek model"""
inputs = tokenizer(request.message, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
reply = tokenizer.decode(outputs[0], skip_special_tokens=True)
return {"reply": reply}
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