Update app.py

app.py

@@ -1,64 +1,46 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
+import torch
+
+# ---------- MODELO DE SIMPLIFICACIÓN ----------
+simplifier_model_name = "google/flan-t5-small"
+simplifier_tokenizer = AutoTokenizer.from_pretrained(simplifier_model_name)
+simplifier_model = AutoModelForSeq2SeqLM.from_pretrained(simplifier_model_name)
+
+def simplificar_texto(texto):
+    prompt = f"Simplify this text: {texto}"
+    inputs = simplifier_tokenizer(prompt, return_tensors="pt", truncation=True)
+    outputs = simplifier_model.generate(**inputs, max_new_tokens=100)
+    resultado = simplifier_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return resultado
+
+# ---------- MODELO DE PREDICCIÓN DE TEXTO ----------
+predictor_model_name = "distilgpt2"
+predictor_tokenizer = AutoTokenizer.from_pretrained(predictor_model_name)
+predictor_model = AutoModelForCausalLM.from_pretrained(predictor_model_name)
+
+def predecir_texto(texto_inicial):
+    inputs = predictor_tokenizer.encode(texto_inicial, return_tensors="pt")
+    outputs = predictor_model.generate(inputs, max_new_tokens=20, do_sample=True, top_k=50)
+    texto_generado = predictor_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return texto_generado[len(texto_inicial):]  # Solo mostrar lo nuevo
+
+# ---------- INTERFAZ GRADIO ----------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 Chatbot Simplificador y Teclado Predictivo")
+    
+    with gr.Tab("Simplificación de texto"):
+        gr.Markdown("Introduce un texto complejo y obtén una versión más sencilla.")
+        entrada_simplificar = gr.Textbox(label="Texto original", lines=4, placeholder="Ej. Un párrafo de un documento legal...")
+        salida_simplificar = gr.Textbox(label="Texto simplificado")
+        boton_simplificar = gr.Button("Simplificar")
+        boton_simplificar.click(fn=simplificar_texto, inputs=entrada_simplificar, outputs=salida_simplificar)
+
+    with gr.Tab("Texto Predictivo"):
+        gr.Markdown("Escribe el inicio de una frase y recibe sugerencias.")
+        entrada_predecir = gr.Textbox(label="Frase incompleta", placeholder="Ej. Me gustaría ir a la...")
+        salida_predecir = gr.Textbox(label="Sugerencia")
+        boton_predecir = gr.Button("Predecir")
+        boton_predecir.click(fn=predecir_texto, inputs=entrada_predecir, outputs=salida_predecir)
+
+demo.launch()