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Update app.py
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app.py
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from huggingface_hub import InferenceClient
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import requests
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# Configura tu cliente de modelo de Hugging Face
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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# Tu clave de API de Google Custom Search
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GOOGLE_API_KEY = "AIzaSyDI48Q_Ez8-KXQ6Dfe_r7JyOkk-dloER0I"
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# Tu ID de motor de b煤squeda
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SEARCH_ENGINE_ID = "030a88810b398467c"
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def web_search(query):
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# Realiza la b煤squeda en Google
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url = f"https://www.googleapis.com/customsearch/v1?q={query}&key={GOOGLE_API_KEY}&cx={SEARCH_ENGINE_ID}"
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try:
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response = requests.get(url)
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response.raise_for_status() # Lanza un error si la respuesta no es exitosa
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results = response.json()
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if "items" in results:
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# Devuelve un resumen de los primeros resultados
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search_results = []
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for item in results["items"]:
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title = item.get("title", "No title")
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link = item.get("link", "")
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snippet = item.get("snippet", "")
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search_results.append(f"{title}: {snippet} ({link})")
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return "\n".join(search_results) # Devuelve los resultados como texto
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else:
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return "No se encontraron resultados relevantes."
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except requests.exceptions.RequestException as e:
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# Maneja errores de la API, como problemas de conexi贸n
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return f"Error al realizar la b煤squeda: {e}"
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def respond(message, history, system_message, max_tokens, temperature, top_p):
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# Prepara el contexto de la conversaci贸n para el modelo
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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# Incluye los resultados de la b煤squeda en el contexto para el modelo
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messages.append({"role": "system", "content": f"Search results:\n{search_summary}"})
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# Genera la respuesta del modelo
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response = client.text_completion(
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prompt=message, max_tokens=max_tokens, temperature=temperature, top_p=top_p
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)
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# Interfaz de Gradio
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with gr.Blocks() as demo:
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chatbot = gr.Chatbot()
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msg = gr.Textbox()
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clear = gr.Button("Clear")
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def chat_interface(user_message, history=[]):
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output = respond(
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user_message, history, "You are a helpful assistant.", 200, 0.7, 0.9
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)
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history.append((user_message, output))
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return history, chatbot.update(history)
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msg.submit(chat_interface, inputs=[msg, chatbot], outputs=[chatbot, chatbot])
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clear.click(lambda: [], None, chatbot)
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demo.launch()
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from transformers import pipeline
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pipe = pipeline("text-generation", model="carlosdimare/qclase")
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def responder(prompt):
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respuesta = pipe(prompt, max_new_tokens=200, do_sample=True)[0]["generated_text"]
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return respuesta
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import gradio as gr
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gr.Interface(fn=responder, inputs="text", outputs="text").launch()
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