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Update app.py
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app.py
CHANGED
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@@ -2,14 +2,30 @@ import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Cargar modelo m谩s peque帽o para generar c贸digo
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model_name = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16 # Usar float16 para ahorrar memoria
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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def generate_code(prompt):
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"""Genera c贸digo basado en el prompt del usuario."""
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messages = [
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@@ -18,16 +34,39 @@ def generate_code(prompt):
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{"role": "assistant", "content": ""}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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model_inputs = tokenizer([text], return_tensors="pt")
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=
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do_sample=True,
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temperature=0.7
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)
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response = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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return response
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def preview_app(html_code, css_code, js_code):
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"""Devuelve una vista previa interactiva de la aplicaci贸n."""
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html_content = f"""
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@@ -52,24 +91,7 @@ def run_chatbot(user_input):
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code_output = generate_code(user_input)
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# Extraer HTML, CSS y JS del c贸digo generado
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html_code =
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css_code = ""
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js_code = ""
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if "<style>" in code_output:
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css_start = code_output.find("<style>") + len("<style>")
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css_end = code_output.find("</style>")
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css_code = code_output[css_start:css_end].strip()
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if "<script>" in code_output:
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js_start = code_output.find("<script>") + len("<script>")
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js_end = code_output.find("</script>")
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js_code = code_output[js_start:js_end].strip()
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if "<body>" in code_output:
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html_start = code_output.find("<body>") + len("<body>")
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html_end = code_output.find("</body>")
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html_code = code_output[html_start:html_end].strip()
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# Previsualizar la aplicaci贸n
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preview = preview_app(html_code, css_code, js_code)
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Verificar si hay GPU disponible (Zero-GPU)
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if torch.cuda.is_available():
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device = "cuda" # Usar GPU Zero
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print("Zero-GPU detectada. Usando GPU para acelerar la inferencia.")
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else:
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device = "cpu" # Usar CPU si no hay GPU
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print("No se detect贸 GPU. Usando CPU.")
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# Cargar modelo m谩s peque帽o para generar c贸digo
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model_name = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
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print("Cargando modelo...")
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16, # Usar float16 para ahorrar memoria
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device_map="auto" if device == "cuda" else None # Distribuir autom谩ticamente en GPU si est谩 disponible
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Mover el modelo expl铆citamente a GPU si es necesario
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if device == "cuda":
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model.to("cuda")
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print("Modelo cargado con 茅xito.")
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def generate_code(prompt):
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"""Genera c贸digo basado en el prompt del usuario."""
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messages = [
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{"role": "assistant", "content": ""}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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model_inputs = tokenizer([text], return_tensors="pt").to(device) # Mover entradas al dispositivo correspondiente
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=128, # Reducir tokens para respuestas m谩s r谩pidas y ahorrar memoria
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do_sample=True,
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temperature=0.7
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)
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response = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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return response
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def extract_code(output):
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"""Extrae HTML, CSS y JavaScript del texto generado."""
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html_code = ""
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css_code = ""
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js_code = ""
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if "<style>" in output:
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css_start = output.find("<style>") + len("<style>")
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css_end = output.find("</style>")
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css_code = output[css_start:css_end].strip()
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if "<script>" in output:
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js_start = output.find("<script>") + len("<script>")
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js_end = output.find("</script>")
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js_code = output[js_start:js_end].strip()
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if "<body>" in output:
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html_start = output.find("<body>") + len("<body>")
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html_end = output.find("</body>")
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html_code = output[html_start:html_end].strip()
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return html_code, css_code, js_code
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def preview_app(html_code, css_code, js_code):
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"""Devuelve una vista previa interactiva de la aplicaci贸n."""
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html_content = f"""
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code_output = generate_code(user_input)
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# Extraer HTML, CSS y JS del c贸digo generado
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html_code, css_code, js_code = extract_code(code_output)
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# Previsualizar la aplicaci贸n
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preview = preview_app(html_code, css_code, js_code)
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