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| import os | |
| import logging | |
| from flask import Flask, render_template_string, send_file, abort | |
| from huggingface_hub import hf_hub_download, login as hf_login | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| hf_token = os.getenv("HF_TOKEN") | |
| try: | |
| hf_login(token=hf_token) | |
| logger.info("Login en Hugging Face exitoso con el token proporcionado.") | |
| except Exception as e: | |
| logger.error(f"Error durante el login en Hugging Face: {e}") | |
| app = Flask(__name__) | |
| MODEL_FILENAME = 'gemma3-1b-it-int4.task' | |
| HUGGINGFACE_REPO = 'litert-community/Gemma3-1B-IT' | |
| MODEL_LOCAL_PATH = os.path.join(os.getcwd(), MODEL_FILENAME) | |
| def download_model_file(): | |
| if not os.path.exists(MODEL_LOCAL_PATH): | |
| logger.info("Archivo de modelo no encontrado localmente. Iniciando descarga desde Hugging Face a la carpeta local...") | |
| try: | |
| hf_hub_download( | |
| repo_id=HUGGINGFACE_REPO, | |
| filename=MODEL_FILENAME, | |
| local_dir=".", | |
| local_dir_use_symlinks=False | |
| ) | |
| logger.info(f"Descarga completada: {MODEL_LOCAL_PATH}") | |
| except Exception as e: | |
| logger.error(f"Error al descargar el archivo de modelo: {e}") | |
| raise | |
| else: | |
| logger.info("El archivo de modelo ya existe localmente.") | |
| return MODEL_LOCAL_PATH | |
| model_file_path = download_model_file() | |
| logger.info(f"Ruta del archivo de modelo: {model_file_path}") | |
| HTML_CONTENT = """<!doctype html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <title>LLM Inference Web Demo</title> | |
| <style> | |
| body { font-family: Arial, sans-serif; margin: 20px; } | |
| textarea { font-family: monospace; } | |
| </style> | |
| </head> | |
| <body> | |
| <h1>Demo de Inferencia LLM</h1> | |
| <p>Ingresa el texto de entrada y presiona "Get Response".</p> | |
| <label for="input">Input:</label><br /> | |
| <textarea id="input" style="height: 300px; width: 600px"></textarea><br /> | |
| <input type="button" id="submit" value="Get Response" disabled /><br /><br /> | |
| <label for="output">Result:</label><br /> | |
| <textarea id="output" style="height: 300px; width: 600px"></textarea> | |
| <div id="error-message" style="color: red;"></div> | |
| <script type="module" src="/index.js"></script> | |
| </body> | |
| </html> | |
| """ | |
| JS_CONTENT = """import {FilesetResolver, LlmInference} from 'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-genai'; | |
| const input = document.getElementById('input'); | |
| const output = document.getElementById('output'); | |
| const submit = document.getElementById('submit'); | |
| const modelFileName = '/download'; | |
| const errorMessageDiv = document.getElementById('error-message'); | |
| function displayPartialResults(partialResults, complete) { | |
| output.textContent += partialResults; | |
| if (complete) { | |
| if (!output.textContent) { | |
| output.textContent = 'Result is empty'; | |
| } | |
| submit.disabled = false; | |
| } | |
| } | |
| async function runDemo() { | |
| const genaiFileset = await FilesetResolver.forGenAiTasks( | |
| 'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-genai/wasm'); | |
| let llmInference; | |
| submit.onclick = () => { | |
| output.textContent = ''; | |
| submit.disabled = true; | |
| llmInference.generateResponse(input.value, displayPartialResults); | |
| }; | |
| submit.value = 'Loading the model...'; | |
| try { | |
| LlmInference | |
| .createFromOptions(genaiFileset, { | |
| baseOptions: {modelAssetPath: modelFileName}, | |
| }) | |
| .then(llm => { | |
| llmInference = llm; | |
| submit.disabled = false; | |
| submit.value = 'Get Response'; | |
| }) | |
| .catch(error => { | |
| console.error("Error initializing LlmInference:", error); | |
| errorMessageDiv.innerHTML = `Failed to initialize the task due to WebGPU limitations. Details: ${error} <br><br>` + | |
| `**Troubleshooting Steps:**<br>` + | |
| `1. **Verify WebGPU Support:**<br>` + | |
| ` - **Browser:** Use **Google Chrome** or **Microsoft Edge (latest versions).**<br>` + | |
| ` - **Check chrome://gpu (or edge://gpu):** In your browser address bar, type \`chrome://gpu\` (or \`edge://gpu\`) and press Enter. Look for "WebGPU" section. <br>` + | |
| ` - **Status:** Should say "Hardware accelerated". If disabled or "Software only", WebGPU is not working correctly.<br>` + | |
| ` - **maxStorageBufferBindingSize:** Verify the reported value. If it's very low, your GPU/browser might be too limited.<br>` + | |
| ` - **Enable WebGPU Flags (if needed):** In chrome://flags (or edge://flags), search for "WebGPU" and try enabling flags like \`#enable-unsafe-webgpu\` and restart browser.<br><br>` + | |
| `2. **Update Browser and GPU Drivers:**<br>` + | |
| ` - **Browser:** Update Chrome/Edge to the latest version.<br>` + | |
| ` - **GPU Drivers:** Download and install the latest drivers from NVIDIA, AMD, or Intel websites for your specific GPU and operating system. **Restart your computer after driver install.**<br><br>` + | |
| `3. **Restart Your Computer:** A simple restart can resolve temporary issues.<br><br>` + | |
| `4. **Try a Different Browser/Computer:** Test with a different WebGPU-compatible browser (Chrome/Edge) or on a different computer with a more capable GPU if possible.<br><br>` + | |
| `5. **Check GPU Compatibility:** Older or very low-end GPUs might have limited WebGPU support.<br><br>` + | |
| `If the issue persists after these steps, your GPU or browser may have inherent limitations for running this LLM demo in WebGPU.`; | |
| submit.disabled = true; | |
| submit.value = 'Failed to load model'; | |
| }); | |
| } catch (e) { | |
| console.error("Error during LlmInference setup:", e); | |
| errorMessageDiv.textContent = `Failed to set up LlmInference. Details: ${e}`; | |
| submit.disabled = true; | |
| submit.value = 'Failed to load model'; | |
| } | |
| } | |
| runDemo(); | |
| """ | |
| def index(): | |
| return render_template_string(HTML_CONTENT) | |
| def serve_js(): | |
| return JS_CONTENT, 200, {'Content-Type': 'application/javascript'} | |
| def download_file(): | |
| logger.info(f"Solicitud para descargar el modelo desde: {model_file_path}") | |
| if os.path.exists(model_file_path): | |
| return send_file(model_file_path) | |
| else: | |
| logger.error(f"Archivo de modelo no encontrado en la ruta: {model_file_path}") | |
| abort(404, description="Archivo de modelo no encontrado.") | |
| if __name__ == '__main__': | |
| logger.info("Iniciando la aplicación Flask en el puerto 5000") | |
| app.run(debug=True, host="0.0.0.0", port=7860) |