Update app.py

app.py

@@ -59,6 +59,66 @@ demo = gr.ChatInterface(
 if __name__ == "__main__":
     demo.launch()
 
+    import gradio as gr
+from huggingface_hub import InferenceClient
+import torch
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+# Crear la función de loop automatizado
+def experiment_loop(initial_question, max_cycles=10):
+    prompt = f"<thinking>{initial_question}</thinking>"
+    effectiveness = 100  # Inicializa el porcentaje de efectividad
+    communication = "Initializing experiment."
+    response_log = []
+
+    for cycle in range(max_cycles):
+        # Generar la respuesta del modelo
+        inputs = tokenizer(prompt, return_tensors="pt").input_ids
+        outputs = model.generate(inputs, max_length=200)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        # Descomponer la respuesta en afirmación y nueva pregunta
+        affirmation = extract_affirmation(response)
+        new_question = extract_question(response)
+
+        # Actualizar el estado de la efectividad
+        effectiveness = min(1000, effectiveness + 10 * cycle)  # Ejemplo de aumento de efectividad
+
+        # Comunicación con el usuario
+        communication = f"Cycle {cycle + 1}: Affirmation: '{affirmation}' | New Question: '{new_question}'"
+
+        # Guardar el ciclo actual en el log
+        response_log.append((affirmation, new_question, effectiveness, communication))
+
+        # Verificar si el modelo decide detenerse
+        if "Descanso" in response:
+            final_output = generate_final_output(response_log)
+            return final_output
+        
+        # Actualizar el prompt con la nueva afirmación y pregunta
+        prompt = f"<thinking>{affirmation} {new_question}</thinking>"
+
+    # Si se alcanza el número máximo de ciclos sin detenerse
+    final_output = generate_final_output(response_log)
+    return final_output
+
+# Funciones auxiliares para extraer afirmaciones, preguntas y generar la salida final
+def extract_affirmation(response):
+    return response.split('.')[0]
+
+def extract_question(response):
+    return response.split('?')[-2].strip() + "?"
+
+def generate_final_output(log):
+    final_affirmation = log[-1][0]
+    final_question = log[-1][1]
+    final_communication = f"Experiment completed. Final Affirmation: '{final_affirmation}' | Final Question: '{final_question}'"
+    return final_communication
+# Iniciar el experimento después de que la función ha sido definida
+initial_question = "What happens in the space between a response and its recreation?"
+result = experiment_loop(initial_question)
+print(result)
+
+
 # Define the experiment loop
 initial_question = "What happens in the space between a response and its recreation?"
 result = experiment_loop(initial_question)
@@ -127,4 +187,4 @@ def generate_final_output(log):
 # Iniciar el experimento
 initial_question = "What happens in the space between a response and its recreation?"
 result = experiment_loop(initial_question)
-print(result)
+print(result)