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from fastapi import FastAPI, HTTPException
from transformers import AutoModel, AutoTokenizer
import gradio as gr
# Inicializar FastAPI
app = FastAPI()
# Cargar el modelo y el tokenizador desde Hugging Face
model_name = "ancerlop/ToxicBERTMultilabelTextClassification"
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Definir funci贸n de predicci贸n
def predict(text):
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
return outputs.logits
# Crear una interfaz Gradio
iface = gr.Interface(
fn=predict,
inputs=gr.inputs.Textboxbox(),
outputs=gr.outputs.Label(num_top_classes=5),
live=True,
title="Modelo de Clasificaci贸n de Texto",
description="Este modelo clasifica texto en diferentes categor铆as."
)
# Definir una ruta en FastAPI para la API
@app.post("/api/predict/")
def predict_api(text: str):
try:
result = predict(text)
return {"predictions": result.tolist()}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
# Montar Gradio en FastAPI
@app.get("/gradio")
def gradio_interface():
return iface.launch()
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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