Add main chatbot app code with Gradio interface

app.py

@@ -1,70 +1,69 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
+# ---------------------------
+# MODELO LIGERO PARA CHAT
+# ---------------------------
+model_name = "TheBloke/guanaco-7B-GPTQ"  # Conversacional, rápido y gratuito
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+chat_pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-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")
+# ---------------------------
+# CV RESUMIDO
+# ---------------------------
+with open("cv.txt", "r", encoding="utf-8") as f:
+    cv_text = f.read()
 
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
+# ---------------------------
+# FUNCION DE RESPUESTA
+# ---------------------------
+def responder(pregunta):
+    prompt = f"""
+Usa solo la información de este CV para responder en español, primera persona, de forma breve y profesional.
+Pregunta: {pregunta}
+CV:
+{cv_text}
+"""
+    respuesta = chat_pipe(prompt, max_length=200, do_sample=False)[0]['generated_text']
+    return respuesta
 
-    messages.append({"role": "user", "content": message})
+# ---------------------------
+# INTERFAZ GRADIO
+# ---------------------------
+with gr.Blocks() as demo:
 
-    response = ""
+    # Imagen inicial
+    gr.Image(value="marianobot.png", interactive=False)
 
-    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
+    # Título grande del chat
+    gr.Markdown("<h2>🤖 MarianoBot – ¡Descubre y pregunta todo lo que quieras!</h2>", elem_id="titulo")
 
-        response += token
-        yield response
+    # Chatbot con saludo inicial
+    chatbot = gr.Chatbot(type="messages", value=[{"role":"assistant","content":"¡Hola! ¡Pregúntame para conocer más sobre mí!"}])
 
+    # Entrada de pregunta
+    question_input = gr.Textbox(
+        label="Escribe tu pregunta...",
+        placeholder="Pregunta sobre mi experiencia, habilidades o trayectoria",
+        lines=1
+    )
 
-"""
-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)",
-        ),
-    ],
-)
+    # Botón enviar
+    submit_button = gr.Button("Hacer pregunta", elem_id="boton-naranja")
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+    # Función para actualizar el chat
+    def enviar(input_text, history):
+        answer = responder(input_text)
+        history.append({"role":"user","content":input_text})
+        history.append({"role":"assistant","content":answer})
+        return history, ""
 
+    # Conectar textbox y botón
+    question_input.submit(enviar, [question_input, chatbot], [chatbot, question_input])
+    submit_button.click(enviar, [question_input, chatbot], [chatbot, question_input])
 
-if __name__ == "__main__":
-    demo.launch()
+# ---------------------------
+# LANZAR INTERFAZ
+# ---------------------------
+demo.launch()