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from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from deep_translator import GoogleTranslator
from datasets import load_dataset
from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd, torch, os
# === Config ===
HF_MODEL_ID = os.getenv("HF_MODEL_ID", "stvnnnnnn/tapex-wikisql-best")
TABLE_SPLIT = os.getenv("TABLE_SPLIT", "validation")
TABLE_INDEX = int(os.getenv("TABLE_INDEX", "10"))
MAX_ROWS = int(os.getenv("MAX_ROWS", "12"))
torch.set_num_threads(1)
app = FastAPI(title="NL→SQL – TAPEX + WikiSQL (HF Space)")
# CORS: permite que Vercel (o cualquier origen) consuma la API
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], allow_credentials=False,
allow_methods=["*"], allow_headers=["*"],
)
# Carga modelo/tokenizer con bajo pico de RAM (CPU)
tok = TapexTokenizer.from_pretrained(HF_MODEL_ID)
model = BartForConditionalGeneration.from_pretrained(
HF_MODEL_ID, low_cpu_mem_usage=True
).to("cpu")
class NLQuery(BaseModel):
nl_query: str
def get_example(split, index):
# streaming para no cargar todo WikiSQL en RAM
ds = load_dataset("Salesforce/wikisql", split=split, streaming=True)
for i, ex in enumerate(ds):
if i == index:
return ex
raise IndexError("Index fuera de rango")
def load_table(split=TABLE_SPLIT, index=TABLE_INDEX, max_rows=MAX_ROWS):
ex = get_example(split, index)
header = [str(h) for h in ex["table"]["header"]]
rows = ex["table"]["rows"][:max_rows]
return pd.DataFrame(rows, columns=header)
@app.get("/api/health")
def health():
return {"ok": True, "model": HF_MODEL_ID, "split": TABLE_SPLIT, "index": TABLE_INDEX}
@app.get("/api/preview")
def preview():
df = load_table()
return {"columns": df.columns.tolist(), "rows": df.head(8).to_dict(orient="records")}
@app.post("/api/nl2sql")
def nl2sql(q: NLQuery):
try:
text = (q.nl_query or "").strip()
if not text:
raise ValueError("Consulta vacía.")
is_ascii = all(ord(c) < 128 for c in text)
query_en = text if is_ascii else GoogleTranslator(source="auto", target="en").translate(text)
df = load_table()
enc = tok(table=df, query=query_en, return_tensors="pt", truncation=True)
out = model.generate(**enc, max_length=160, num_beams=1)
sql = tok.batch_decode(out, skip_special_tokens=True)[0]
return {"consulta_original": text, "consulta_traducida": query_en, "sql_generado": sql}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e)) |