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| import os | |
| import sys | |
| import torch | |
| import random | |
| import re | |
| import json | |
| import math | |
| import copy | |
| import requests | |
| from functools import lru_cache | |
| from tqdm import tqdm | |
| from torch.nn.parameter import Parameter | |
| from sklearn.datasets import fetch_20newsgroups | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.multiclass import OneVsRestClassifier | |
| import time | |
| import threading | |
| import queue | |
| import httpx | |
| import asyncio | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import uuid | |
| import wget | |
| from duckduckgo_search import DDGS | |
| import warnings | |
| from datetime import datetime | |
| import unicodedata | |
| import nltk | |
| import torchaudio | |
| import logging | |
| from PIL import Image | |
| from io import BytesIO | |
| import sentencepiece as spm | |
| from flask import Flask, request, jsonify, send_file, Response | |
| from flask_cors import CORS | |
| nltk.download('punkt', quiet=True) | |
| GPT2_FOLDER = "./GPT2" | |
| MODEL_FILE = "gpt2-pytorch_model.bin" | |
| ENCODER_FILE = "encoder.json" | |
| VOCAB_FILE = "vocab.bpe" | |
| MODEL_URL = "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-pytorch_model.bin" | |
| ENCODER_URL = "https://raw.githubusercontent.com/graykode/gpt-2-Pytorch/refs/heads/master/GPT2/GPT2/encoder.json" | |
| VOCAB_URL = "https://raw.githubusercontent.com/graykode/gpt-2-Pytorch/refs/heads/master/GPT2/GPT2/vocab.bpe" | |
| GPT2_FILES_URLS = [ | |
| (MODEL_URL, MODEL_FILE), | |
| (ENCODER_URL, ENCODER_FILE), | |
| (VOCAB_URL, VOCAB_FILE), | |
| ] | |
| TEXT_GENERATION_RATE = 40000 | |
| MAX_LENGTH = 1024 | |
| MAX_XDD = 5 | |
| END_OF_TEXT_TOKEN = "<|endoftext|>" | |
| html_code = """<!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>AI Text Generation</title> | |
| <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/animate.css/4.1.1/animate.min.css"/> | |
| <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css" integrity="sha512-9usAa10IRO0HhonpyAIVpjrylPvoDwiPUiKdWk5t3PyolY1cOd4DSE0Ga+ri4AuTroPR5aQvXU9xC6qOPnzFeg==" crossorigin="anonymous" referrerpolicy="no-referrer" /> | |
| <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script> | |
| <style> | |
| body { | |
| font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; | |
| background: #f0f0f0; | |
| color: #333; | |
| margin: 0; | |
| padding: 0; | |
| display: flex; | |
| flex-direction: column; | |
| align-items: center; | |
| min-height: 100vh; | |
| } | |
| .container { | |
| width: 95%; | |
| max-width: 900px; | |
| padding: 20px; | |
| background-color: #fff; | |
| box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); | |
| border-radius: 8px; | |
| margin-top: 20px; | |
| margin-bottom: 20px; | |
| display: flex; | |
| flex-direction: column; | |
| } | |
| .header { | |
| text-align: center; | |
| margin-bottom: 20px; | |
| } | |
| .header h1 { | |
| font-size: 2em; | |
| color: #333; | |
| } | |
| .form-group { | |
| margin-bottom: 15px; | |
| } | |
| .form-group textarea { | |
| width: 100%; | |
| padding: 10px; | |
| border: 1px solid #ccc; | |
| border-radius: 5px; | |
| font-size: 16px; | |
| box-sizing: border-box; | |
| resize: vertical; | |
| } | |
| button { | |
| padding: 10px 15px; | |
| border: none; | |
| border-radius: 5px; | |
| background-color: #007bff; | |
| color: white; | |
| font-size: 18px; | |
| cursor: pointer; | |
| transition: background-color 0.3s ease; | |
| } | |
| button:hover { | |
| background-color: #0056b3; | |
| } | |
| #output { | |
| margin-top: 20px; | |
| padding: 15px; | |
| border: 1px solid #ddd; | |
| border-radius: 5px; | |
| background-color: #f9f9f9; | |
| white-space: pre-wrap; | |
| word-break: break-word; | |
| overflow-y: auto; | |
| max-height: 100vh; | |
| } | |
| #output strong { | |
| font-weight: bold; | |
| } | |
| .animated-text { | |
| position: fixed; | |
| top: 20px; | |
| left: 20px; | |
| font-size: 1.5em; | |
| color: rgba(0, 0, 0, 0.1); | |
| pointer-events: none; | |
| z-index: -1; | |
| } | |
| @media (max-width: 768px) { | |
| .container { | |
| width: 98%; | |
| margin-top: 10px; | |
| margin-bottom: 10px; | |
| padding: 15px; | |
| } | |
| .header h1 { | |
| font-size: 1.8em; | |
| } | |
| .form-group textarea, .form-group input[type="text"] { | |
| font-size: 14px; | |
| padding: 8px; | |
| } | |
| button { | |
| font-size: 16px; | |
| padding: 8px 12px; | |
| } | |
| #output { | |
| font-size: 14px; | |
| padding: 10px; | |
| margin-top: 15px; | |
| } | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="animated-text animate__animated animate__fadeIn animate__infinite infinite">AI POWERED</div> | |
| <div class="container"> | |
| <div class="header animate__animated animate__fadeInDown"> | |
| </div> | |
| <div class="form-group animate__animated animate__fadeInLeft"> | |
| <textarea id="text" rows="5" placeholder="Enter text"></textarea> | |
| </div> | |
| <button onclick="generateText()" class="animate__animated animate__fadeInUp">Generate Reasoning</button> | |
| <div id="output" class="animate__animated"> | |
| <strong >Response:</strong><br> | |
| <div id="generatedText"></div> | |
| </div> | |
| </div> | |
| <script> | |
| let eventSource = null; | |
| let accumulatedText = ""; | |
| let lastResponse = ""; | |
| let currentSpan = null; | |
| let messageCounter = 0; | |
| async function generateText() { | |
| const inputText = document.getElementById("text").value; | |
| const generatedTextDiv = document.getElementById("generatedText"); | |
| generatedTextDiv.innerHTML = ""; | |
| accumulatedText = ""; | |
| lastResponse = ""; | |
| currentSpan = null; | |
| messageCounter = 0; | |
| if (eventSource) { | |
| eventSource.close(); | |
| } | |
| const temp = 0.7; | |
| const top_k_val = 40; | |
| const top_p_val = 0.0; | |
| const repetition_penalty_val = 1.2; | |
| eventSource = new EventSource(`/generate_stream?text=${encodeURIComponent(inputText)}&temp=${temp}&top_k=${top_k_val}&top_p=${top_p_val}&reppenalty=${reppenalty_val}`); | |
| eventSource.onmessage = function(event) { | |
| if (event.data === "<END_STREAM>") { | |
| eventSource.close(); | |
| const currentResponse = accumulatedText.replace("<|endoftext|>", "").replace(re.compile(r'\\s+(?=[.,，。])'), '').trim(); | |
| if (currentResponse === lastResponse.trim()) { | |
| accumulatedText = "**Response is repetitive. Please try again or rephrase your query.**"; | |
| } else { | |
| lastResponse = currentResponse; | |
| } | |
| document.getElementById("generatedText").innerHTML = marked.parse(accumulatedText); | |
| return; | |
| } | |
| try { | |
| const jsonData = JSON.parse(event.data); | |
| const token = jsonData.token; | |
| if (token === "<|endoftext|>" || token === "<END_STREAM>") { | |
| return; | |
| } | |
| if (token === "<NEW_MESSAGE>") { | |
| messageCounter++; | |
| if (messageCounter > 1) { | |
| generatedTextDiv.innerHTML += "<br><br><hr style='border-top: 1px dashed #8c8b8b; margin-top: 10px; margin-bottom: 10px;'><strong>Continued Response:</strong><br><div id='generatedText_" + messageCounter + "'></div>"; | |
| generatedTextDiv = document.getElementById("generatedText_" + messageCounter); | |
| accumulatedText = ""; | |
| } | |
| return; | |
| } | |
| accumulatedText += token + " "; | |
| } catch (e) { | |
| console.error("Error parsing SSE data:", event.data, e); | |
| } | |
| }; | |
| eventSource.onerror = function(error) { | |
| console.error("SSE error", error); | |
| eventSource.close(); | |
| }; | |
| const outputDiv = document.getElementById("output"); | |
| outputDiv.classList.add("show"); | |
| } | |
| </script> | |
| </body> | |
| </html> | |
| """ | |
| TRANSLATION_FOLDER = "./TranslationModel" | |
| TRANSLATION_MODEL_WEIGHTS_FILE = "pytorch_model.bin" | |
| TRANSLATION_MODEL_CONFIG_FILE = "config.json" | |
| TRANSLATION_MODEL_VOCAB_FILE = "sentencepiece.bpe.model" | |
| TRANSLATION_MODEL_WEIGHTS_URL = "https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt/resolve/main/pytorch_model.bin" | |
| TRANSLATION_MODEL_CONFIG_URL = "https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt/resolve/main/config.json" | |
| TRANSLATION_MODEL_VOCAB_URL = "https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt/resolve/main/sentencepiece.bpe.model" | |
| TRANSLATION_MODEL_FILES_URLS = [ | |
| (TRANSLATION_MODEL_WEIGHTS_URL, TRANSLATION_MODEL_WEIGHTS_FILE), | |
| (TRANSLATION_MODEL_CONFIG_URL, TRANSLATION_MODEL_CONFIG_FILE), | |
| (TRANSLATION_MODEL_VOCAB_URL, TRANSLATION_MODEL_VOCAB_FILE), | |
| ] | |
| CODEGEN_FOLDER = "./CodeGenModel" | |
| CODEGEN_MODEL_NAME = "codegen-350M-multi" | |
| CODEGEN_MODEL_WEIGHTS = "pytorch_model.bin" | |
| CODEGEN_CONFIG = "config.json" | |
| CODEGEN_VOCAB = "vocab.json" | |
| CODEGEN_MERGES = "merges.txt" | |
| CODEGEN_MODEL_WEIGHTS_URL = "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/pytorch_model.bin" | |
| CODEGEN_CONFIG_URL = "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json" | |
| CODEGEN_VOCAB_URL = "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/vocab.json" | |
| CODEGEN_MERGES_URL = "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/merges.txt" | |
| CODEGEN_FILES_URLS = [ | |
| (CODEGEN_MODEL_WEIGHTS_URL, CODEGEN_MODEL_WEIGHTS), | |
| (CODEGEN_CONFIG_URL, CODEGEN_CONFIG), | |
| (CODEGEN_VOCAB_URL, CODEGEN_VOCAB), | |
| (CODEGEN_MERGES_URL, CODEGEN_MERGES), | |
| ] | |
| TTS_FOLDER = "./TTSModel" | |
| TTS_MODEL_NAME = "vits" | |
| TTS_MODEL_CONFIG = "config.json" | |
| TTS_MODEL_WEIGHTS = "pytorch_model.bin" | |
| TTS_VOCAB = "vocab.json" | |
| TTS_CONFIG_URL = "https://huggingface.co/kakao-enterprise/vits-vctk/resolve/main/config.json" | |
| TTS_MODEL_WEIGHTS_URL = "https://huggingface.co/kakao-enterprise/vits-vctk/resolve/main/pytorch_model.bin" | |
| TTS_VOCAB_URL = "https://huggingface.co/kakao-enterprise/vits-vctk/resolve/main/vocab.json" | |
| TTS_FILES_URLS = [ | |
| (TTS_CONFIG_URL, TTS_MODEL_CONFIG), | |
| (TTS_MODEL_WEIGHTS_URL, TTS_MODEL_WEIGHTS), | |
| (TTS_VOCAB_URL, TTS_VOCAB), | |
| ] | |
| STT_FOLDER = "./STTModel" | |
| STT_MODEL_NAME = "wav2vec2" | |
| STT_MODEL_WEIGHTS = "pytorch_model.bin" | |
| STT_CONFIG = "config.json" | |
| STT_VOCAB = "vocab.json" | |
| STT_MODEL_WEIGHTS_URL = "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/pytorch_model.bin" | |
| STT_CONFIG_URL = "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json" | |
| STT_VOCAB_URL = "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json" | |
| STT_FILES_URLS = [ | |
| (STT_MODEL_WEIGHTS_URL, STT_MODEL_WEIGHTS), | |
| (STT_CONFIG_URL, STT_CONFIG), | |
| (STT_VOCAB_URL, STT_VOCAB), | |
| ] | |
| SENTIMENT_FOLDER = "./SentimentModel" | |
| SENTIMENT_MODEL_WEIGHTS = "pytorch_model.bin" | |
| SENTIMENT_VOCAB = "sentiment_vocab.json" | |
| SENTIMENT_CONFIG = "config.json" | |
| SENTIMENT_MODEL_WEIGHTS_URL = "https://huggingface.co/cardiffnlp/distilroberta-base-sentiment/resolve/main/pytorch_model.bin" | |
| SENTIMENT_VOCAB_URL = "https://huggingface.co/cardiffnlp/distilroberta-base-sentiment/resolve/main/vocab.json" | |
| SENTIMENT_CONFIG_URL = "https://huggingface.co/cardiffnlp/distilroberta-base-sentiment/resolve/main/config.json" | |
| SENTIMENT_FILES_URLS = [ | |
| (SENTIMENT_MODEL_WEIGHTS_URL, SENTIMENT_MODEL_WEIGHTS), | |
| (SENTIMENT_VOCAB_URL, SENTIMENT_VOCAB), | |
| (SENTIMENT_CONFIG_URL, SENTIMENT_CONFIG), | |
| ] | |
| IMAGEGEN_FOLDER = "./ImageGenModel" | |
| IMAGEGEN_MODEL_WEIGHTS = "diffusion_pytorch_model.bin" | |
| IMAGEGEN_CONFIG = "config.json" | |
| IMAGEGEN_MODEL_WEIGHTS_URL = "https://huggingface.co/stabilityai/sd-vae-ft-mse/resolve/main/diffusion_pytorch_model.bin" | |
| IMAGEGEN_CONFIG_URL = "https://huggingface.co/stabilityai/sd-vae-ft-mse/resolve/main/config.json" | |
| IMAGEGEN_FILES_URLS = [ | |
| (IMAGEGEN_MODEL_WEIGHTS_URL, IMAGEGEN_MODEL_WEIGHTS), | |
| (IMAGEGEN_CONFIG_URL, IMAGEGEN_CONFIG), | |
| ] | |
| LIPSYNC_FOLDER = "./LipSyncModel" | |
| LIPSYNC_MODEL_WEIGHTS = "lipsync_expert.pth" | |
| LIPSYNC_MODEL_WEIGHTS_URL = "https://iiitaphyd-my.sharepoint.com/personal/radrabha_m_research_iiit_ac_in/_layouts/15/download.aspx?SourceUrl=%2Fpersonal%2Fradrabha%5Fm%5Fresearch%5Fiiit%5Fac%5Fin%2FDocuments%2FWav2Lip%5FModels%2Flipsync%5Fexpert%2Epth" | |
| LIPSYNC_FILES_URLS = [ | |
| (LIPSYNC_MODEL_WEIGHTS_URL, LIPSYNC_MODEL_WEIGHTS), | |
| ] | |
| WAV2LIP_FOLDER = "./Wav2LipModel" | |
| WAV2LIP_MODEL_WEIGHTS = "wav2lip_gan.pth" | |
| WAV2LIP_MODEL_WEIGHTS_URL = "https://iiitaphyd-my.sharepoint.com/personal/radrabha_m_research_iiit_ac_in/_layouts/15/download.aspx?SourceUrl=%2Fpersonal%2Fradrabha%5Fm%5Fresearch%5Fiiit%5Fac%5Fin%2FDocuments%2FWav2Lip%5FModels%2Fwav2lip%5Fgan%2Epth" | |
| WAV2LIP_FILES_URLS = [ | |
| (WAV2LIP_MODEL_WEIGHTS_URL, WAV2LIP_MODEL_WEIGHTS), | |
| ] | |
| MUSICGEN_FOLDER = "./MusicGenModel" | |
| MUSICGEN_MODEL_NAME = "melody" | |
| MUSICGEN_MODEL_WEIGHTS = "pytorch_model.bin" | |
| MUSICGEN_CONFIG = "config.json" | |
| MUSICGEN_SAMPLE_RATE = 32000 | |
| MUSICGEN_DURATION = 8 | |
| MUSICGEN_MODEL_WEIGHTS_URL = "https://huggingface.co/facebook/musicgen-small/resolve/main/pytorch_model.bin" | |
| MUSICGEN_CONFIG_URL = "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json" | |
| MUSICGEN_FILES_URLS = [ | |
| (MUSICGEN_MODEL_WEIGHTS_URL, MUSICGEN_MODEL_WEIGHTS), | |
| (MUSICGEN_CONFIG_URL, MUSICGEN_CONFIG), | |
| ] | |
| CODEGEN_SPM_URL = "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/spm.model" | |
| CODEGEN_SPM = "spm.model" | |
| TRANSLATION_SPM_URL = "https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt/resolve/main/sentencepiece.bpe.model" | |
| TRANSLATION_SPM = "sentencepiece.bpe.model" | |
| TEXT_TO_VIDEO_FOLDER = "./TextToVideoModel" | |
| TEXT_TO_VIDEO_MODEL_WEIGHTS = "pytorch_model.bin" | |
| TEXT_TO_VIDEO_CONFIG = "config.json" | |
| TEXT_TO_VIDEO_VOCAB = "vocab.json" | |
| TEXT_TO_VIDEO_MODEL_WEIGHTS_URL = "https://huggingface.co/Searchium-ai/clip4clip-webvid150k/resolve/main/pytorch_model.bin" | |
| TEXT_TO_VIDEO_CONFIG_URL = "https://huggingface.co/Searchium-ai/clip4clip-webvid150k/resolve/main/config.json" | |
| TEXT_TO_VIDEO_VOCAB_URL = "https://huggingface.co/Searchium-ai/clip4clip-webvid150k/resolve/main/vocab.json" | |
| TEXT_TO_VIDEO_FILES_URLS = [ | |
| (TEXT_TO_VIDEO_MODEL_WEIGHTS_URL, TEXT_TO_VIDEO_MODEL_WEIGHTS), | |
| (TEXT_TO_VIDEO_CONFIG_URL, TEXT_TO_VIDEO_CONFIG), | |
| (TEXT_TO_VIDEO_VOCAB_URL, TEXT_TO_VIDEO_VOCAB), | |
| ] | |
| SUMMARIZATION_FOLDER = "./SummarizationModel" | |
| SUMMARIZATION_MODEL_WEIGHTS = "pytorch_model.bin" | |
| SUMMARIZATION_CONFIG = "config.json" | |
| SUMMARIZATION_VOCAB = "vocab.json" | |
| SUMMARIZATION_MODEL_WEIGHTS_URL = "https://huggingface.co/facebook/bart-large-cnn/resolve/main/pytorch_model.bin" | |
| SUMMARIZATION_CONFIG_URL = "https://huggingface.co/facebook/bart-large-cnn/resolve/main/config.json" | |
| SUMMARIZATION_VOCAB_URL = "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json" | |
| SUMMARIZATION_FILES_URLS = [ | |
| (SUMMARIZATION_MODEL_WEIGHTS_URL, SUMMARIZATION_MODEL_WEIGHTS), | |
| (SUMMARIZATION_CONFIG_URL, SUMMARIZATION_CONFIG), | |
| (SUMMARIZATION_VOCAB_URL, SUMMARIZATION_VOCAB), | |
| ] | |
| IMAGE_TO_3D_FOLDER = "./ImageTo3DModel" | |
| IMAGE_TO_3D_MODEL_WEIGHTS = "pytorch_model.bin" | |
| IMAGE_TO_3D_CONFIG = "config.json" | |
| IMAGE_TO_3D_MODEL_URL = "https://huggingface.co/zxhezexin/openlrm-obj-base-1.1/resolve/main/pytorch_model.bin" | |
| IMAGE_TO_3D_CONFIG_URL = "https://huggingface.co/zxhezexin/openlrm-obj-base-1.1/resolve/main/config.json" | |
| IMAGE_TO_3D_FILES_URLS = [ | |
| (IMAGE_TO_3D_MODEL_URL, IMAGE_TO_3D_MODEL_WEIGHTS), | |
| (IMAGE_TO_3D_CONFIG_URL, IMAGE_TO_3D_CONFIG), | |
| ] | |
| state_dict = None | |
| enc = None | |
| config = None | |
| model = None | |
| device = torch.device("cpu") | |
| news_clf = None | |
| tfidf_vectorizer = None | |
| text_queue = queue.Queue() | |
| categories = None | |
| is_training = False | |
| background_threads = [] | |
| feedback_queue = queue.Queue() | |
| reasoning_queue = queue.Queue() | |
| seen_responses = set() | |
| tts_model = None | |
| stt_model = None | |
| sentiment_model = None | |
| imagegen_model = None | |
| lipsync_model = None | |
| wav2lip_model = None | |
| musicgen_model = None | |
| translation_model = None | |
| codegen_model = None | |
| text_to_video_model = None | |
| summarization_model = None | |
| image_to_3d_model = None | |
| tts_pipeline = False | |
| stt_pipeline = False | |
| sentiment_pipeline = False | |
| imagegen_pipeline = False | |
| translation_pipeline = False | |
| codegen_pipeline = False | |
| text_to_video_pipeline = False | |
| summarization_pipeline = False | |
| image_to_3d_pipeline = False | |
| stt_tokenizer = None | |
| stt_processor = None | |
| sentiment_tokenizer = None | |
| sentiment_model_instance = None | |
| imagegen_vae = None | |
| imagegen_unet = None | |
| imagegen_scheduler = None | |
| musicgen_model_instance = None | |
| musicgen_tokenizer = None | |
| musicgen_processor = None | |
| translation_model_instance = None | |
| translation_tokenizer = None | |
| codegen_model_instance = None | |
| codegen_tokenizer = None | |
| codegen_sp = None | |
| translation_sp = None | |
| text_to_video_tokenizer = None | |
| text_to_video_model_instance = None | |
| summarization_tokenizer = None | |
| summarization_model_instance = None | |
| image_to_3d_config = None | |
| image_to_3d_model_instance = None | |
| app = Flask(__name__) | |
| CORS(app) | |
| from gpt2_pytorch import * | |
| from tts_vits import * | |
| from stt_wav2vec2 import * | |
| from sentiment_roberta import * | |
| from imagegen_vae_unet import * | |
| from musicgen_torch import * | |
| from translation_mbart import * | |
| from codegen_torch import * | |
| from text_to_video_clip4clip import * | |
| from summarization_bart import * | |
| from image_to_3d_openlrm import * | |
| def download_file(url, filename): | |
| os.makedirs(os.path.dirname(filename), exist_ok=True) # Ensure directory exists | |
| if not os.path.exists(filename): | |
| print(f"Downloading {filename} from {url}...") | |
| try: | |
| wget.download(url, out=filename) # Specify output filename directly | |
| print(f"Downloaded {filename} successfully.") | |
| except Exception as e: | |
| print(f"Error downloading {filename}: {e}") | |
| def ensure_folder_and_files_exist(folder_path, files_urls): | |
| if not os.path.exists(folder_path): | |
| os.makedirs(folder_path) | |
| print(f"Folder '{folder_path}' created.") | |
| for url, filename in files_urls: | |
| filepath = os.path.join(folder_path, filename) | |
| download_file(url, filepath) | |
| def ensure_single_file_exists(folder_path, file_url, filename): | |
| if not os.path.exists(folder_path): | |
| os.makedirs(folder_path) | |
| print(f"Folder '{folder_path}' created.") | |
| filepath = os.path.join(folder_path, filename) | |
| download_file(file_url, filepath) | |
| def ensure_gpt2_files_exist(): | |
| ensure_folder_and_files_exist(GPT2_FOLDER, GPT2_FILES_URLS) | |
| def ensure_translation_files_exist(): | |
| ensure_folder_and_files_exist(TRANSLATION_FOLDER, TRANSLATION_MODEL_FILES_URLS) | |
| ensure_single_file_exists(TRANSLATION_FOLDER, TRANSLATION_SPM_URL, TRANSLATION_SPM) | |
| def ensure_codegen_files_exist(): | |
| ensure_folder_and_files_exist(CODEGEN_FOLDER, CODEGEN_FILES_URLS) | |
| ensure_single_file_exists(CODEGEN_FOLDER, CODEGEN_SPM_URL, CODEGEN_SPM) | |
| def ensure_tts_files_exist(): | |
| ensure_folder_and_files_exist(TTS_FOLDER, TTS_FILES_URLS) | |
| def ensure_stt_files_exist(): | |
| ensure_folder_and_files_exist(STT_FOLDER, STT_FILES_URLS) | |
| def ensure_sentiment_files_exist(): | |
| ensure_folder_and_files_exist(SENTIMENT_FOLDER, SENTIMENT_FILES_URLS) | |
| def ensure_imagegen_files_exist(): | |
| ensure_folder_and_files_exist(IMAGEGEN_FOLDER, IMAGEGEN_FILES_URLS) | |
| def ensure_lipsync_files_exist(): | |
| ensure_folder_and_files_exist(LIPSYNC_FOLDER, LIPSYNC_FILES_URLS) | |
| def ensure_wav2lip_files_exist(): | |
| ensure_folder_and_files_exist(WAV2LIP_FOLDER, WAV2LIP_FILES_URLS) | |
| def ensure_musicgen_files_exist(): | |
| ensure_folder_and_files_exist(MUSICGEN_FOLDER, MUSICGEN_FILES_URLS) | |
| def ensure_text_to_video_files_exist(): | |
| ensure_folder_and_files_exist(TEXT_TO_VIDEO_FOLDER, TEXT_TO_VIDEO_FILES_URLS) | |
| def ensure_summarization_files_exist(): | |
| ensure_folder_and_files_exist(SUMMARIZATION_FOLDER, SUMMARIZATION_FILES_URLS) | |
| def ensure_image_to_3d_files_exist(): | |
| ensure_folder_and_files_exist(IMAGE_TO_3D_FOLDER, IMAGE_TO_3D_FILES_URLS) | |
| def ensure_all_model_files_exist(): # Define the function here, before it's called | |
| ensure_gpt2_files_exist() | |
| ensure_translation_files_exist() | |
| ensure_codegen_files_exist() | |
| ensure_tts_files_exist() | |
| ensure_stt_files_exist() | |
| ensure_sentiment_files_exist() | |
| ensure_imagegen_files_exist() | |
| ensure_lipsync_files_exist() | |
| ensure_wav2lip_files_exist() | |
| ensure_musicgen_files_exist() | |
| ensure_text_to_video_files_exist() | |
| ensure_summarization_files_exist() | |
| ensure_image_to_3d_files_exist() | |
| async def html_handler(): | |
| return html_code | |
| async def generate_stream_api(): | |
| text_input = request.args.get("text") | |
| temperature = float(request.args.get("temp", 0.7)) | |
| top_k = int(request.args.get("top_k", 40)) | |
| top_p = float(request.args.get("top_p", 0.0)) | |
| reppenalty = float(request.args.get("reppenalty", 1.2)) | |
| return Response(generate_stream_generator(text_input, temperature, top_k, top_p, reppenalty), mimetype='text/event-stream') | |
| def tts_api(): | |
| data = request.get_json() | |
| text = data.get('text') | |
| if not text: | |
| return jsonify({"error": "Text is required"}), 400 | |
| output_file = text_to_speech(text) | |
| if output_file == "Error generating speech.": | |
| return jsonify({"error": "TTS generation failed"}), 500 | |
| return send_file(output_file, mimetype="audio/wav", as_attachment=True, download_name="output.wav") | |
| def stt_api(): | |
| if 'audio' not in request.files: | |
| return jsonify({"error": "Audio file is required"}), 400 | |
| audio_file = request.files['audio'] | |
| temp_audio_path = f"temp_audio_{uuid.uuid4()}.wav" | |
| audio_file.save(temp_audio_path) | |
| output_file = speech_to_text(temp_audio_path) | |
| os.remove(temp_audio_path) | |
| if output_file == "Error transcribing audio.": | |
| return jsonify({"error": "STT failed"}), 500 | |
| return send_file(output_file, mimetype="text/plain", as_attachment=True, download_name="output.txt") | |
| def sentiment_api(): | |
| data = request.get_json() | |
| text = data.get('text') | |
| if not text: | |
| return jsonify({"error": "Text is required"}), 400 | |
| output_file = analyze_sentiment(text) | |
| if output_file == "Sentiment model not initialized.": | |
| return jsonify({"error": "Sentiment analysis failed"}), 500 | |
| return jsonify(output_file) | |
| def imagegen_api(): | |
| data = request.get_json() | |
| prompt = data.get('prompt') | |
| if not prompt: | |
| return jsonify({"error": "Prompt is required"}), 400 | |
| output_file = generate_image(prompt) | |
| if output_file == "Error generating image.": | |
| return jsonify({"error": "Image generation failed"}), 500 | |
| image_io = BytesIO() | |
| output_file.save(image_io, 'PNG') | |
| image_io.seek(0) | |
| return send_file(image_io, mimetype='image/png', as_attachment=True, download_name="output.png") | |
| def musicgen_api(): | |
| data = request.get_json() | |
| prompt = data.get('prompt') | |
| if not prompt: | |
| return jsonify({"error": "Prompt is required"}), 400 | |
| output_file = generate_music(prompt) | |
| if output_file == "Error generating music.": | |
| return jsonify({"error": "Music generation failed"}), 500 | |
| return send_file(output_file, mimetype="audio/wav", as_attachment=True, download_name="output.wav") | |
| def translation_api(): | |
| data = request.get_json() | |
| text = data.get('text') | |
| target_lang = data.get('target_lang', 'es') | |
| source_lang = data.get('source_lang', 'en') | |
| if not text: | |
| return jsonify({"error": "Text is required"}), 400 | |
| output_file = perform_translation(text, target_language_code=f'{target_lang}_XX', source_language_code=f'{source_lang}_XX') | |
| if output_file == "Error during translation.": | |
| return jsonify({"error": "Translation failed"}), 500 | |
| return send_file(output_file, mimetype="text/plain", as_attachment=True, download_name="output_translation.txt") | |
| def codegen_api(): | |
| data = request.get_json() | |
| prompt = data.get('prompt') | |
| if not prompt: | |
| return jsonify({"error": "Prompt is required"}), 400 | |
| output_file = generate_code(prompt) | |
| if output_file == "Error generating code.": | |
| return jsonify({"error": "Code generation failed"}), 500 | |
| return send_file(output_file, mimetype="text/x-python", as_attachment=True, download_name="output.py") | |
| def text_to_video_api(): | |
| data = request.get_json() | |
| prompt = data.get('prompt') | |
| if not prompt: | |
| return jsonify({"error": "Prompt is required"}), 400 | |
| output_file = text_to_video(prompt) | |
| if output_file == "Error generating video representation.": | |
| return jsonify({"error": "Text to video failed"}), 500 | |
| return send_file(output_file, mimetype="application/octet-stream", as_attachment=True, download_name="output_video_representation.pt") | |
| def summarization_api(): | |
| data = request.get_json() | |
| text = data.get('text') | |
| if not text: | |
| return jsonify({"error": "Text is required"}), 400 | |
| output_file = summarize_text(text) | |
| if output_file == "Error during summarization.": | |
| return jsonify({"error": "Summarization failed"}), 500 | |
| return send_file(output_file, mimetype="text/plain", as_attachment=True, download_name="output_summary.txt") | |
| def image_to_3d_api(): | |
| if 'image' not in request.files: | |
| return jsonify({"error": "Image file is required"}), 400 | |
| image_file = request.files['image'] | |
| temp_image_path = f"temp_image_{uuid.uuid4()}.png" | |
| image_file.save(temp_image_path) | |
| output_file = image_to_3d(temp_image_path) | |
| os.remove(temp_image_path) | |
| if output_file == "Error converting image to 3D.": | |
| return jsonify({"error": "Image to 3D failed"}), 500 | |
| return send_file(output_file, mimetype="model/obj", as_attachment=True, download_name="output_3d.obj") | |
| async def main(): | |
| global background_threads, response_queue | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
| response_queue = queue.Queue() | |
| ensure_all_model_files_exist() | |
| initialize_model() | |
| await initialize_sklearn() | |
| initialize_tts_model() | |
| initialize_stt_model() | |
| initialize_sentiment_model() | |
| initialize_imagegen_model() | |
| ensure_lipsync_files_exist() | |
| ensure_wav2lip_files_exist() | |
| initialize_musicgen_model() | |
| initialize_translation_model() | |
| initialize_codegen_model() | |
| initialize_text_to_video_model() | |
| initialize_summarization_model() | |
| initialize_image_to_3d_model() | |
| background_threads.append(threading.Thread(target=generate_and_queue_text, args=('en',), daemon=True)) | |
| background_threads.append(threading.Thread(target=generate_and_queue_text, args=('es',), daemon=True)) | |
| background_threads.append(threading.Thread(target=background_training, daemon=True)) | |
| for thread in background_threads: | |
| thread.start() | |
| asyncio.create_task(background_reasoning_queue()) | |
| app.run(host="127.0.0.1", port=7860, debug=False) | |
| if __name__ == '__main__': | |
| asyncio.run(main()) |