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import gradio as gr
from transformers import pipeline
from sentence_transformers import SentenceTransformer, util
import math
# Translation models
translation_models = {
'Vietnamese': "Helsinki-NLP/opus-mt-en-vi",
'Japanese': "Helsinki-NLP/opus-mt-en-jap",
'Thai': "Helsinki-NLP/opus-mt-en-tha",
'Spanish': "Helsinki-NLP/opus-mt-en-es"
}
# Initialize summarization pipeline with a specified model
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
# Initialize translation pipeline
def get_translator(language):
model_name = translation_models.get(language)
if model_name:
return pipeline("translation", model=model_name)
return None
# Helper function to generate bullet points
def generate_bullet_points(text):
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
sentences = text.split('. ')
embeddings = model.encode(sentences, convert_to_tensor=True)
clusters = util.community_detection(embeddings, threshold=0.75)
bullet_points = []
for cluster in clusters:
cluster_sentences = [sentences[idx] for idx in cluster]
main_sentence = cluster_sentences[0] if cluster_sentences else ""
bullet_points.append(main_sentence.strip())
return "\n".join(f"- {point}" for point in bullet_points)
# Helper function to split text into chunks
def split_text(text, max_tokens=1024):
sentences = text.split('. ')
chunks = []
current_chunk = ""
current_tokens = 0
for sentence in sentences:
sentence_tokens = len(sentence.split())
if current_tokens + sentence_tokens > max_tokens:
chunks.append(current_chunk.strip())
current_chunk = sentence
current_tokens = sentence_tokens
else:
current_chunk += sentence + ". "
current_tokens += sentence_tokens
if current_chunk:
chunks.append(current_chunk.strip())
return chunks
# Helper function to summarize text
def summarize_text(text):
chunks = split_text(text)
summaries = [summarizer(chunk, max_length=150, min_length=40, do_sample=False)[0]['summary_text'] for chunk in chunks]
return " ".join(summaries)
# Helper function to translate text
def translate_text(text, language):
translator = get_translator(language)
if translator:
translated_text = translator(text)[0]['translation_text']
return translated_text
return text
def process_text(input_text, language):
summary = summarize_text(input_text)
bullet_points = generate_bullet_points(summary)
translated_text = translate_text(bullet_points, language)
return bullet_points, translated_text
# Create Gradio interface
iface = gr.Interface(
fn=process_text,
inputs=[
gr.Textbox(label="Input Text", placeholder="Paste your text here..."),
gr.Dropdown(choices=["Vietnamese", "Japanese", "Thai", "Spanish"], label="Translate to", value="Vietnamese")
],
outputs=[
gr.Textbox(label="Bullet Points"),
gr.Textbox(label="Translated Bullet Points")
],
title="Text to Bullet Points and Translation",
description="Paste any text, and the program will summarize it into bullet points. Optionally, translate the bullet points into Vietnamese, Japanese, Thai, or Spanish."
)
iface.launch()
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