Update app.py

app.py

@@ -1,30 +1,91 @@
 import gradio as gr
-from transformers import pipeline
+from huggingface_hub import InferenceClient
 
-# Modelos de texto e imagen
-chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct", device_map="auto")
-image_gen = pipeline("text-to-image", model="stabilityai/stable-diffusion-2")
 
-def responder(mensaje, historial):
-    respuesta = chatbot(mensaje, max_new_tokens=200, temperature=0.7)[0]["generated_text"]
-    historial.append((mensaje, respuesta))
-    return historial, historial
+def respond(
+    message,
+    history: list[dict[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token: gr.OAuthToken,
+):
+    """
+    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(token=hf_token.token, model="openai/gpt-oss-20b")
 
-def generar_imagen(prompt):
-    imagen = image_gen(prompt)[0]["image"]
-    return imagen
+    messages = [{"role": "system", "content": system_message}]
+
+    messages.extend(history)
+
+    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,
+    ):
+        choices = message.choices
+        token = ""
+        if len(choices) and choices[0].delta.content:
+            token = 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
+"""
+chatbot = gr.ChatInterface(
+    respond,
+    type="messages",
+    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)",
+        ),
+    ],
+)
 
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 Mi IA Chat + Imagen")
+    with gr.Sidebar():
+        gr.LoginButton()
+    chatbot.render()
+
 
-    with gr.Tab("💬 Chat"):
-        chat = gr.Chatbot()
-        entrada = gr.Textbox()
-        entrada.submit(responder, [entrada, chat], [chat, chat])
+if __name__ == "__main__":
+    demo.launch()
+def chat(prompt, max_length=200):
+    # Convertimos el prompt en tensores para el modelo
+    inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
+    
+    # Generamos la respuesta del modelo
+    outputs = model.generate(
+        inputs,
+        max_length=max_length,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=True,
+        top_p=0.9,
+        temperature=0.7
+    )
+    
+    # ⚡ Aquí ponemos el código para quitar la columna de tokens
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Devolvemos solo la respuesta en texto plano
+    return response[len(prompt):].strip()
 
-    with gr.Tab("🎨 Imagen"):
-        prompt = gr.Textbox(label="Escribe lo que quieres ver")
-        salida = gr.Image()
-        prompt.submit(generar_imagen, inputs=prompt, outputs=salida)
 
-demo.launch()