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README (1).md

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+---
+title: Chaterapia Prueba
+emoji: 😻
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.25.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import gradio as gr
+import os
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Asegúrate de que tu token de Hugging Face está cargado como una variable de entorno
+hf_token = os.environ.get("token")
+if hf_token is not None:
+    from huggingface_hub import HfFolder
+    HfFolder.save_token(hf_token)
+
+# Configuración inicial
+tokenizer = AutoTokenizer.from_pretrained("somosnlp/chaterapia_model")
+model_base = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it").to(device)
+model_base.resize_token_embeddings(len(tokenizer))
+model_with_adapter = PeftModel.from_pretrained(model_base, "somosnlp/chaterapia_model").to(device)
+
+CHAT_TEMPLATE= """{% for message in messages %}
+    {% if message['role'] == 'user' %}
+        {{'<user> ' + message['content'].strip() + ' </user>' }}
+    {% elif message['role'] == 'system' %}
+        {{'<system>\\n' + message['content'].strip() + '\\n</system>\\n\\n' }}
+    {% elif message['role'] == 'assistant' %}
+        {{ message['content'].strip() + ' </assistant>' + eos_token }}
+    {% elif message['role'] == 'input' %}
+        {{'<input> ' + message['content'] + ' </input>' }}
+    {% endif %}
+{% endfor %}""" # Asegúrate de usar tu CHAT_TEMPLATE aquí
+tokenizer.chat_template = CHAT_TEMPLATE
+
+chat_history = []  # Historial de chat global
+chatbot_text = []
+def generate_response(user_input):
+    global chat_history
+    # Agregar input del usuario al historial
+    chat_history.append({"content": user_input, "role": "user"})
+    
+    # Preparación del input para el modelo
+    user_input = tokenizer.apply_chat_template(chat_history, tokenize=False)
+    input_tokens = tokenizer(user_input, return_tensors='pt', padding=True, truncation=True, max_length=1024).to(device)
+    
+    # Generación de la respuesta del modelo
+    output_tokens = model_with_adapter.generate(**input_tokens, max_length=1024, pad_token_id=tokenizer.eos_token_id, top_k=50, do_sample=True, top_p=0.95, temperature=0.7)
+    generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+    
+    # Extracción de la respuesta generada
+    last_us = generated_text.rfind("</user>") + len("</user>")
+    last_as = generated_text.rfind("</assistant>")
+    generated_text = generated_text[last_us:last_as].strip()
+    
+    # Agregar la respuesta del bot al historial
+    chat_history.append({"content": generated_text, "role": "assistant"})
+    return generated_text
+
+def respond(message):
+    global chatbot_text
+    if message:  # Verificar si el mensaje no está vacío
+        bot_response = generate_response(message)
+        chatbot_text.append((message, bot_response))
+        return chatbot_text
+    return [("", "")]
+
+
+def clear_chat_and_history():
+    global chat_history
+    global chatbot_text
+    chat_history.clear()# Vaciar el historial de chat
+    chatbot_text.clear()
+    return "", []
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    # Usar un Button regular en lugar de ClearButton para tener control sobre la función que se ejecuta
+    with gr.Row():
+        msg = gr.Textbox(label="Tu mensaje", placeholder="Escribe aquí...", lines=1)
+        send_btn = gr.Button("Enviar")
+    clear_btn = gr.Button("Limpiar Chat")
+    
+    # Acción al presionar el botón Enviar
+    send_btn.click(fn=respond, inputs=msg, outputs=chatbot)
+    clear_btn.click(fn=clear_chat_and_history, inputs=None, outputs=[msg, chatbot])
+
+demo.launch()

gitattributes

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requirements (1).txt

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+transformers==4.38.2
+torch
+gradio
+peft==0.10.0