Update app.py

app.py

@@ -1,70 +1,53 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
 
-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")
+# Cargar dataset
+with open("intents.json") as f:
+    data = json.load(f)
 
-    messages = [{"role": "system", "content": system_message}]
+X = [d["text"] for d in data]
+y = [d["intent"] for d in data]
 
-    messages.extend(history)
+# Vectorizar texto
+vectorizer = CountVectorizer()
+X_vect = vectorizer.fit_transform(X)
 
-    messages.append({"role": "user", "content": message})
+# Entrenar modelo
+clf = MultinomialNB()
+clf.fit(X_vect, y)
 
-    response = ""
+responses = {
+    "greet": "¡Hola! ¿En qué puedo ayudarte?",
+    "goodbye": "¡Hasta luego! Que tengas un buen día.",
+    "ask_hours": "Estamos abiertos de lunes a viernes de 9 a 18h.",
+    "ask_registration": "Para registrarte, visita nuestra web y completa el formulario."
+}
 
-    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
+def chatbot_response(text):
+    X_new = vectorizer.transform([text])
+    intent = clf.predict(X_new)[0]
+    return responses.get(intent, "Lo siento, no entendí tu pregunta.")
 
-        response += token
-        yield response
+while True:
+    msg = input("Tú: ")
+    if msg.lower() in ["salir", "adiós"]:
+        print("Bot:", responses["goodbye"])
+        break
+    print("Bot:", chatbot_response(msg))
 
+!pip install gradio --quiet
 
-"""
-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)",
-        ),
-    ],
-)
+import gradio as gr
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+def chat_gradio(message):
+    return chatbot_response(message)
 
+iface = gr.Interface(
+    fn=chat_gradio,
+    inputs="text",
+    outputs="text",
+    title="Chatbot Demo",
+    description="Demo de chatbot entrenado con anotaciones simples"
+)
 
-if __name__ == "__main__":
-    demo.launch()
+iface.launch()