Update app.py

app.py

@@ -1,8 +1,19 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from accelerate import init_empty_weights, infer_auto_device_map
+import torch
 
-# Inicializar el cliente para la API de Hugging Face
-client = InferenceClient(model="bushai/sar-i-7b")
+# Nombre del modelo en Hugging Face
+model_name = "bushai/sar-i-7b"
+
+# Inicializa un modelo vacío para ahorrar memoria
+with init_empty_weights():
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Definir cómo distribuir el modelo entre CPU y GPU
+device_map = infer_auto_device_map(model, max_memory={0: "14GB", "cpu": "2GB"})
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map=device_map)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 def respond(
     message,
@@ -12,43 +23,25 @@ def respond(
     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]})
+    # Tokenizar la entrada del usuario
+    inputs = tokenizer(message, return_tensors="pt").to(model.device)
 
-    messages.append({"role": "user", "content": message})
-
-    # Llamar a la API de Hugging Face para generar la respuesta
-    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
+    # Generar la respuesta del modelo
+    outputs = model.generate(inputs['input_ids'], max_new_tokens=max_tokens, temperature=temperature, top_p=top_p)
+    
+    # Decodificar la salida generada
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return response
 
 # Configurar la interfaz de Gradio
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        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)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )