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README.md

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+---
+title: DemoRefranesCastellano
+emoji: 💻
+colorFrom: pink
+colorTo: purple
+sdk: gradio
+sdk_version: 5.32.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: Demo Sabiduria POpular
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import gradio as gr
+
+# Constants for generation parameters
+MAX_NEW_TOKENS = 100
+TEMPERATURE = 0.5
+TOP_P = 0.95
+TOP_K = 50
+REPETITION_PENALTY = 1.05
+
+
+# Global variables to store model and tokenizer
+model = None
+tokenizer = None
+
+def load_model():
+    global model, tokenizer
+    return model, tokenizer
+
+
+def generate_response(input_text, max_tokens, temperature, top_p, repetition_penalty):
+    global model, tokenizer
+    
+    if model is None or tokenizer is None:
+        model, tokenizer = load_model()
+    
+    
+    return "Adios"
+
+def chat_interface(message, history, system_message, max_tokens, temperature, top_p, repetition_penalty):
+
+  prompt = f"{message}"
+  if system_message:
+      prompt = f"{system_message}\n{message}"
+      
+  response = generate_response(
+      prompt, 
+      max_tokens, 
+      temperature, 
+      top_p, 
+      repetition_penalty
+  )
+  return response
+
+demo = gr.ChatInterface(
+    chat_interface,
+    title="Sabiduría Popular - Refranes",
+    description="Esta aplicación explica el significado de refranes en español utilizando un modelo de lenguaje. Escribe un refrán y el modelo te explicará su significado.",
+    examples=[
+        ["A caballo regalado no le mires el diente"],
+        ["Más vale pájaro en mano que ciento volando"],
+        ["Quien a buen árbol se arrima, buena sombra le cobija"],
+        ["No por mucho madrugar amanece más temprano"]
+    ],
+    additional_inputs=[
+        gr.Textbox(
+            value="Eres un experto en sabiduría popular española. Tu tarea es explicar el significado de refranes en español de manera clara y concisa.",
+            label="System message"
+        ),
+        gr.Slider(
+            minimum=1,
+            maximum=500,
+            value=MAX_NEW_TOKENS,
+            step=1,
+            label="Max new tokens"
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=2.0,
+            value=TEMPERATURE,
+            step=0.1,
+            label="Temperature"
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=TOP_P,
+            step=0.05,
+            label="Top-p (nucleus sampling)"
+        ),
+        gr.Slider(
+            minimum=1.0,
+            maximum=2.0,
+            value=REPETITION_PENALTY,
+            step=0.05,
+            label="Repetition penalty"
+        ),
+    ],
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    print("Iniciando la aplicación. El modelo se cargará con la primera consulta.")
+    demo.launch()