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import gradio as gr
import re
import textstat
from transformers import pipeline
from langdetect import detect
# Load a summarization model
summarizer = pipeline("summarization")
def text_analysis_and_summarization(text):
# Analyze text: word count, character count, language detection, and readability
words = re.findall(r'\w+', text.lower())
sentences = re.split(r'[.!?]+', text)
num_sentences = len(sentences) - 1
num_words = len(words)
num_chars = len("".join(words))
reading_ease = textstat.flesch_reading_ease(text)
language = detect(text)
# Summarize text using the transformer model
summary = summarizer(text, max_length=130, min_length=30, do_sample=False)[0]['summary_text']
# Format the results
return {
"Language": language,
"Sentences": num_sentences,
"Words": num_words,
"Characters": num_chars,
"Readability (Flesch Reading Ease)": reading_ease,
"Summary": summary
}
# Define an interface for text analysis and summarization
text_analysis_and_summarization_interface = gr.Interface(fn=text_analysis_and_summarization,
inputs=gr.Textbox(lines=4, placeholder="Type something here..."),
outputs=gr.JSON(label="Text Analysis and Summarization"))
# Launch the interface
if __name__ == "__main__":
text_analysis_and_summarization_interface.launch()
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