Create app.py

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"
+print(device)
+# 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)
+else:
+    print("No se encontró el token de Hugging Face. Asegúrate de que la variable de entorno HF_TOKEN esté configurada.")
+
+# Configuración inicial
+tokenizer = AutoTokenizer.from_pretrained("Juliofc/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, "Juliofc/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
+
+# Función para generar respuestas del modelo
+import gradio as gr
+
+# Asume que todas tus importaciones previas y configuraciones del modelo están aquí
+
+# Aquí deberías tener definida la función `generate_response` tal como la compartiste
+# Función para generar respuestas del modelo
+def generate_response(user_input, chat_history):
+    # Preparar el input agregando el historial de chat
+    chat_history.append({"content": user_input, "role": "user"})
+    print(chat_history)
+    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)
+    
+    # Generar la respuesta
+    output_tokens = model_with_adapter.generate(**input_tokens, max_length=1024, pad_token_id=tokenizer.eos_token_id, top_k=50, top_p=0.95, temperature=0.7)
+    generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+
+    last_us = generated_text.rfind("</user>") + len("</user>")
+    last_as = generated_text.rfind("</assistant>")
+    generated_text = generated_text[last_us:last_as].strip() 
+    chat_history.append({"content": generated_text, "role": "assistant"})
+    return generated_text, chat_history
+    
+History = List[Tuple[str, str]]
+
+def clear_session() -> History:
+    return '', []
+
+def modify_system_session(system: str) -> str:
+    if system is None or len(system) == 0:
+        system = default_system
+    return system, system, []
+
+def model_chat(query: Optional[str], history: Optional[History], system: str
+) -> Tuple[str, str, History]:
+    if query is None:
+        query = ''
+    if history is None:
+        history = []
+    if not history or history[-1][0] != query:  # Asegurar que no se repita la última pregunta
+        history.append((query, ''))
+    response, history = generate_response(query, history)  # Tu función de modelo
+    return '', response, history
+
+placeholder = "This is a simulated conversation with your model."
+
+with gr.Blocks() as demo:
+    gr.Markdown("<center><font size=6>Chatbot with Your Model</center>")
+
+    with gr.Row():
+        system_input = gr.Textbox(value=default_system, lines=1, label='System')
+        modify_system = gr.Button("🛠️ Set system prompt and clear the history")
+        system_state = gr.Textbox(value=default_system, visible=False)
+    
+    chatbot = gr.Chatbot(label='Chat with Your Model', placeholder=placeholder)
+    textbox = gr.Textbox(lines=2, label='Input')
+
+    with gr.Row():
+        clear_history = gr.Button("🧹 Clear history")
+        submit = gr.Button("🚀 Send")
+
+    submit.click(model_chat,
+                 inputs=[textbox, chatbot, system_state],
+                 outputs=[textbox, chatbot, system_input])
+    clear_history.click(fn=clear_session,
+                        inputs=[],
+                        outputs=[textbox, chatbot])
+    modify_system.click(fn=modify_system_session,
+                        inputs=[system_input],
+                        outputs=[system_state, system_input, chatbot])
+
+demo.launch()