Update app.py

app.py

@@ -1,128 +1,3 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-import torch
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
-
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    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,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-demo = gr.ChatInterface(
-    respond,
-    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)",
-        ),
-    ],
-)
-
-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)
-print(result)
 import torch
 from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 
@@ -187,4 +62,4 @@ 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)