Update app.py

app.py

@@ -1,41 +1,24 @@
-import gradio as gr
-from transformers import pipeline, T5Tokenizer, T5ForConditionalGeneration, AutoTokenizer, AutoModelForSeq2SeqLM
-import os
 import requests
+import os
 
-# Load environment variable for Hugging Face API token
+# Configurar el token de Hugging Face como variable de entorno
 token = os.getenv("HF_TOKEN")
-headers = {"Authorization": f"Bearer {token}"}
-
-# Load summarization model and tokenizer
-tokenizer = T5Tokenizer.from_pretrained("sumedh/t5-base-amazonreviews", clean_up_tokenization_spaces=True)
-model = T5ForConditionalGeneration.from_pretrained("sumedh/t5-base-amazonreviews")
-summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
+if not token:
+    raise ValueError("El token HF_TOKEN no se encontró en las variables de entorno. Asegúrate de configurarlo correctamente.")
 
-# Translation API details
+# Configuración de headers para la API de traducción
 API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-en-es"
+headers = {"Authorization": f"Bearer {token}"}
 
-# Summarization and Translation Function
-def texto_sum(text):
-    # Summarize the input text
-    summary = summarizer(text, do_sample=False)[0]['summary_text']
-    
-    # Translate summary using the Hugging Face API
-    response = requests.post(API_URL, headers=headers, json={"inputs": summary})
-    translation = response.json()
+# Prueba de traducción para verificar el token
+def test_translation_api():
+    test_response = requests.post(API_URL, headers=headers, json={"inputs": "Hello, how are you?"})
+    response_data = test_response.json()
     
-    # Check if translation is successful
-    if 'error' in translation:
-        return f"Error in translation: {translation['error']}"
-    
-    return translation[0]['translation_text']
-
-# Gradio interface
-demo = gr.Interface(
-    fn=texto_sum,
-    inputs=gr.Textbox(label="Texto a introducir:", placeholder="Introduce el texto a resumir aquí..."),
-    outputs="text"
-)
+    # Comprobar si hay errores en la respuesta
+    if 'error' in response_data:
+        raise ValueError(f"Error en la API de traducción: {response_data['error']}")
+    print("Token válido y API accesible. Respuesta de prueba:", response_data)
 
-# Launch the interface
-demo.launch()
+# Ejecutar la prueba de token
+test_translation_api()