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| from faster_whisper import WhisperModel | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| import os | |
| print("--- Preloading models for Docker image ---") | |
| # 1. Preload Whisper (Medium) | |
| # This downloads the model files to the cache directory | |
| print("Downloading Whisper model...") | |
| try: | |
| model = WhisperModel("medium", device="cpu", compute_type="int8") | |
| except Exception as e: | |
| print(f"Error downloading Whisper: {e}") | |
| # 2. Preload Summarization Model (BART) | |
| print("Downloading Summarization model...") | |
| try: | |
| model_name = "sshleifer/distilbart-cnn-12-6" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| except Exception as e: | |
| print(f"Error downloading BART: {e}") | |
| # 3. Preload Sentiment Model | |
| print("Downloading Sentiment model...") | |
| try: | |
| from transformers import pipeline | |
| sentiment_pipeline = pipeline( | |
| "sentiment-analysis", | |
| model="distilbert-base-uncased-finetuned-sst-2-english" | |
| ) | |
| except Exception as e: | |
| print(f"Error downloading Sentiment model: {e}") | |
| print("--- Finished preloading ---") | |