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from fastapi import FastAPI, HTTPException
from transformers import AutoModelForCausalLM, AutoTokenizer
import sqlite3
import torch
app = FastAPI()
# Load Model & Tokenizer
MODEL_NAME = "deepseek-ai/deepseek-coder-1.3b-instruct"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16).to(device)
def generate_sql_query(user_input):
""" Convert natural language input into an SQL query """
inputs = tokenizer(user_input, return_tensors="pt", padding=True, truncation=True).to(device)
outputs = model.generate(**inputs, max_length=600, do_sample=False, num_beams=2)
return tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
@app.post("/chat")
def chat(request: dict):
user_input = request.get("message", "")
if not user_input:
raise HTTPException(status_code=400, detail="Message cannot be empty")
sql_query = generate_sql_query(user_input)
print(f"Generated SQL Query: {sql_query}")
return {"response": sql_query}
@app.get("/")
def home():
return {"message": "DeepSeek SQL Query API is running"}
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