Update app.py

app.py

@@ -1,32 +1,30 @@
 import os
 from huggingface_hub import login
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
-import torch
 
 # Autenticar usando el token almacenado como secreto
 hf_token = os.getenv("HF_API_TOKEN")
 login(hf_token)
 
 # Cargar el modelo y el tokenizador
-model_name = "mrm8488/t5-base-finetuned-spanish"
+model_name = "DeepESP/gpt2-spanish"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-def generate_text(input_text):
+def chat_with_gpt2_spanish(input_text):
     inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512)
-    with torch.no_grad():
-        outputs = model.generate(**inputs, max_length=200, num_beams=4, early_stopping=True)
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return generated_text
+    outputs = model.generate(**inputs, max_length=200, num_beams=4, early_stopping=True)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
 
 # Crear la interfaz con Gradio
 iface = gr.Interface(
-    fn=generate_text,
+    fn=chat_with_gpt2_spanish,
     inputs="text",
     outputs="text",
-    title="Generador de Texto en Español",
-    description="Genera texto en español utilizando un modelo de lenguaje preentrenado."
+    title="Chat con GPT-2 en Español",
+    description="Interfaz simple para comunicarte con el modelo GPT-2 en español."
 )
 
-iface.launch()
+iface.launch()