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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| # Supported summarization models | |
| model_choices = { | |
| # 🥇 High Accuracy Models | |
| "Pegasus (google/pegasus-xsum)": "google/pegasus-xsum", | |
| "BigBird-Pegasus (google/bigbird-pegasus-large-arxiv)": "google/bigbird-pegasus-large-arxiv", | |
| "LongT5 Large (google/long-t5-tglobal-large)": "google/long-t5-tglobal-large", | |
| "BART Large CNN (facebook/bart-large-cnn)": "facebook/bart-large-cnn", | |
| "ProphetNet (microsoft/prophetnet-large-uncased-cnndm)": "microsoft/prophetnet-large-uncased-cnndm", | |
| "LED (allenai/led-base-16384)": "allenai/led-base-16384", | |
| "T5 Large (t5-large)": "t5-large", | |
| "Flan-T5 Large (google/flan-t5-large)": "google/flan-t5-large", | |
| # ⚖️ Balanced (Speed vs Accuracy) | |
| "DistilBART CNN (sshleifer/distilbart-cnn-12-6)": "sshleifer/distilbart-cnn-12-6", | |
| "DistilBART XSum (mrm8488/distilbart-xsum-12-6)": "mrm8488/distilbart-xsum-12-6", | |
| "T5 Base (t5-base)": "t5-base", | |
| "Flan-T5 Base (google/flan-t5-base)": "google/flan-t5-base", | |
| "BART CNN SamSum (philschmid/bart-large-cnn-samsum)": "philschmid/bart-large-cnn-samsum", | |
| "T5 SamSum (knkarthick/pegasus-samsum)": "knkarthick/pegasus-samsum", | |
| "LongT5 Base (google/long-t5-tglobal-base)": "google/long-t5-tglobal-base", | |
| # ⚡ Lighter / Faster Models | |
| "T5 Small (t5-small)": "t5-small", | |
| "MBART (facebook/mbart-large-cc25)": "facebook/mbart-large-cc25", | |
| "MarianMT (Helsinki-NLP/opus-mt-en-ro)": "Helsinki-NLP/opus-mt-en-ro", # not trained for summarization, just as placeholder | |
| "Falcon Instruct (tiiuae/falcon-7b-instruct)": "tiiuae/falcon-7b-instruct", # general-purpose, not summarization-specific | |
| "BART ELI5 (yjernite/bart_eli5)": "yjernite/bart_eli5" # trained for explain-like-I'm-5 | |
| } | |
| # Cache for loaded models/tokenizers | |
| model_cache = {} | |
| def load_model(model_name): | |
| if model_name not in model_cache: | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| model_cache[model_name] = (tokenizer, model) | |
| return model_cache[model_name] | |
| # Summarization function | |
| def summarize_text(input_text, model_label): | |
| if not input_text.strip(): | |
| return "Please enter some text." | |
| model_name = model_choices[model_label] | |
| tokenizer, model = load_model(model_name) | |
| if "t5" in model_name.lower(): | |
| input_text = "summarize: " + input_text | |
| inputs = tokenizer(input_text, return_tensors="pt", truncation=True) | |
| summary_ids = model.generate( | |
| inputs["input_ids"], | |
| max_length=20, # Approximate for 65 characters | |
| min_length=5, | |
| do_sample=False | |
| ) | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return summary[:65] # Ensure character limit | |
| # Gradio UI | |
| iface = gr.Interface( | |
| fn=summarize_text, | |
| inputs=[ | |
| gr.Textbox(lines=6, label="Enter text to summarize"), | |
| gr.Dropdown(choices=list(model_choices.keys()), label="Choose summarization model", value="Pegasus (google/pegasus-xsum)") | |
| ], | |
| outputs=gr.Textbox(lines=2, label="Summary (max 65 characters)"), | |
| title="Short Text Summarizer", | |
| description="Summarizes input text to under 65 characters using a selected model." | |
| ) | |
| iface.launch() | |