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import torch
import gradio as gr
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig
# List of summarization models
model_names = [
"google/bigbird-pegasus-large-arxiv",
"facebook/bart-large-cnn",
"google/t5-v1_1-large",
"sshleifer/distilbart-cnn-12-6",
"allenai/led-base-16384",
"google/pegasus-xsum",
"togethercomputer/LLaMA-2-7B-32K"
]
# Placeholder for the summarizer pipeline, tokenizer, and maximum tokens
summarizer = None
tokenizer = None
max_tokens = None
# Function to load the selected model
def load_model(model_name):
global summarizer, tokenizer, max_tokens
try:
# Load the summarization pipeline with the selected model
summarizer = pipeline("summarization", model=model_name, torch_dtype=torch.bfloat16)
# Load the tokenizer for the selected model
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Load the configuration for the selected model
config = AutoConfig.from_pretrained(model_name)
# Determine the maximum tokens based on available configuration attributes
if hasattr(config, 'max_position_embeddings'):
max_tokens = config.max_position_embeddings
elif hasattr(config, 'n_positions'):
max_tokens = config.n_positions
elif hasattr(config, 'd_model'):
max_tokens = config.d_model # for T5 models, d_model is a rough proxy
else:
max_tokens = "Unknown"
return f"Model {model_name} loaded successfully! Max tokens: {max_tokens}"
except Exception as e:
return f"Failed to load model {model_name}. Error: {str(e)}"
# Function to summarize the input text
def summarize_text(input, min_length, max_length):
if summarizer is None:
return "No model loaded!"
# Tokenize the input text and check the number of tokens
input_tokens = tokenizer.encode(input, return_tensors="pt")
num_tokens = input_tokens.shape[1]
if num_tokens > max_tokens:
# Return an error message if the input text exceeds the maximum token limit
return f"Error: The input text has {num_tokens} tokens, which exceeds the maximum allowed {max_tokens} tokens. Please enter shorter text."
# Calculate minimum and maximum summary length based on the percentages
min_summary_length = int(num_tokens * (min_length / 100))
max_summary_length = int(num_tokens * (max_length / 100))
# Summarize the input text using the loaded model with specified lengths
output = summarizer(input, min_length=min_summary_length, max_length=max_summary_length)
return output[0]['summary_text']
# Gradio Interface
with gr.Blocks() as demo:
with gr.Row():
# Dropdown menu for selecting the model
model_dropdown = gr.Dropdown(choices=model_names, label="Choose a model", value="sshleifer/distilbart-cnn-12-6")
# Button to load the selected model
load_button = gr.Button("Load Model")
# Textbox to display the load status
load_message = gr.Textbox(label="Load Status", interactive=False)
# Slider for minimum summary length
min_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Minimum Summary Length (%)", value=10)
# Slider for maximum summary length
max_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Maximum Summary Length (%)", value=20)
# Textbox for inputting the text to be summarized
input_text = gr.Textbox(label="Input text to summarize", lines=6)
# Button to trigger the summarization
summarize_button = gr.Button("Summarize Text")
# Textbox to display the summarized text
output_text = gr.Textbox(label="Summarized text", lines=4)
# Define the actions for the load button and summarize button
load_button.click(fn=load_model, inputs=model_dropdown, outputs=load_message)
summarize_button.click(fn=summarize_text, inputs=[input_text, min_length_slider, max_length_slider],
outputs=output_text)
# Launch the Gradio interface
demo.launch()
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