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| import gradio as gr | |
| from optimum.onnxruntime import ORTModelForSeq2SeqLM | |
| from transformers import pipeline | |
| import logging | |
| logger = logging.Logger() | |
| logger.setLevel(logging.INFO) | |
| # File handler (for logging to file) | |
| file_handler = logging.FileHandler("sc_summ.log", encoding='utf-8') | |
| file_handler.setLevel(logging.INFO) | |
| file_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') | |
| file_handler.setFormatter(file_formatter) | |
| logger.addHandler(file_handler) | |
| def get_summarizer(): | |
| model = ORTModelForSeq2SeqLM.from_pretrained( | |
| "shorecode/t5-efficient-tiny-summarizer-general-purpose-v3" | |
| ) | |
| return pipeline( | |
| "summarization", | |
| model=model, | |
| tokenizer="shorecode/t5-efficient-tiny-summarizer-general-purpose-v3", | |
| ) | |
| def summarize(src): | |
| try: | |
| summary = SUMMARY_MODEL( | |
| src, | |
| max_new_tokens=4000, | |
| min_length=20, | |
| no_repeat_ngram_size=2, | |
| num_beams=3, | |
| ) | |
| return summary | |
| except Exception as e: | |
| logger.error(f"An error occured while summarizing: {e}") | |
| return "An error occurred" | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| demo = gr.Interface(fn=summarize, inputs="text", outputs="text") | |
| demo.launch() |