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import gradio as gr
from utils.ner_helpers import is_llm_model
from typing import Dict, List, Any
from tasks.summarization import text_summarization
def summarization_ui():
"""Summarization UI component"""
# Define models
SUMMARY_MODELS = [
"gemini-2.0-flash" # Only allow gemini-2.0-flash for now
# "gpt-4",
# "claude-2",
# "facebook/bart-large-cnn",
# "t5-small",
# "qwen/Qwen2.5-3B-Instruct"
]
DEFAULT_MODEL = "gemini-2.0-flash"
def summarize(text, model, summary_length, custom_instructions):
"""Process text for summarization"""
if not text.strip():
return "No text provided"
use_llm = is_llm_model(model)
result = text_summarization(
text=text,
model=model,
summary_length=summary_length,
use_llm=use_llm
)
# Lưu ý: custom_instructions sẽ được sử dụng trong tương lai khi API hỗ trợ
return result
# UI Components
with gr.Row():
with gr.Column():
input_text = gr.Textbox(
label="Input Text",
lines=8,
placeholder="Enter text to summarize...",
elem_id="summary-input-text"
)
summary_length = gr.Radio(
["Short", "Medium", "Long"],
value="Medium",
label="Summary Length",
elem_id="summary-length-radio"
)
model = gr.Dropdown(
SUMMARY_MODELS,
value=DEFAULT_MODEL,
label="Model",
interactive=True,
elem_id="summary-model-dropdown"
)
custom_instructions = gr.Textbox(
label="Custom Instructions (optional)",
lines=2,
placeholder="Add any custom instructions for the model...",
elem_id="summary-custom-instructions"
)
btn = gr.Button("Summarize", variant="primary", elem_id="summary-btn")
with gr.Column():
output = gr.Textbox(
label="Summary",
lines=10,
elem_id="summary-output"
)
# with gr.Accordion("About Summarization", open=False):
# gr.Markdown("""
# ## Text Summarization
# Text summarization condenses a document while preserving key information. This tool offers:
# - **Length control**: Choose between short, medium, or long summaries
# - **Multiple models**: Select from LLMs (like Gemini and GPT) or traditional models
# - **Custom instructions**: Tailor the summarization to your specific needs
# ### How it works
# - **LLM models** process your text using natural language understanding
# - **Traditional models** use extractive or abstractive techniques to identify and condense key information
# For best results with long texts, try different summary lengths to find the right balance between brevity and detail.
# """)
# Event handlers
btn.click(
summarize,
inputs=[input_text, model, summary_length, custom_instructions],
outputs=output
)
return None
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