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app.py

@@ -1,27 +1,52 @@
 import gradio as gr
 from collections import Counter
 import re
+from textblob import TextBlob
+import textstat
+from transformers import pipeline
+from langdetect import detect
+
+# Load a summarization model
+summarizer = pipeline("summarization")
 
 def word_and_char_counter(text):
-    # Clean and split text into words
+    # Clean and split text into words and sentences
     words = re.findall(r'\w+', text.lower())  # Remove punctuation and make lowercase
+    sentences = re.split(r'[.!?]+', text)  # Split text into sentences
+    num_sentences = len(sentences) - 1  # Adjusting for the last split being empty if text ends with a punctuation
     num_words = len(words)
     num_chars = len("".join(words))  # Count characters excluding spaces and punctuation
 
     # Count the frequency of each word
-    word_freq = Counter(words).most_common(3)
+    word_freq = Counter(words)
+    most_common_words = word_freq.most_common(3)
+    unique_words = [word for word, count in word_freq.items() if count == 1]
+
+    # Analyze sentiment
+    sentiment = TextBlob(text).sentiment
+
+    # Calculate Flesch Reading Ease score
+    reading_ease = textstat.flesch_reading_ease(text)
+
+    # Detect language of the text
+    language = detect(text)
+
+    # Generate summary of the text using the transformer model
+    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)[0]['summary_text']
 
-    # Format the word frequency results
-    freq_results = ', '.join([f"'{word}': {count}" for word, count in word_freq])
-    return f"{num_words} words, {num_chars} characters. Most common words: {freq_results}"
+    # Format the results
+    freq_results = ', '.join([f"'{word}': {count}" for word, count in most_common_words])
+    unique_words_result = ', '.join(unique_words)
+    sentiment_text = 'Positive' if sentiment.polarity > 0 else 'Negative' if sentiment.polarity < 0 else 'Neutral'
+    return f"Language: {language}. {num_sentences} sentences, {num_words} words, {num_chars} characters. Most common words: {freq_results}. Unique words: {unique_words_result}. Sentiment: {sentiment_text}. Readability (Flesch Reading Ease): {reading_ease:.2f}. Summary: {summary}"
 
 # Define your interface
 interface = gr.Interface(
     fn=word_and_char_counter,
     inputs=gr.Textbox(lines=4, placeholder="Type something here..."),
     outputs="text",
-    title="Word and Character Counter",
-    description="Counts the words and characters in your text and shows the most common words."
+    title="Comprehensive Text Analysis with Language Detection",
+    description="This app provides detailed analysis including language detection, sentence, word, and character counts, lists the most common and unique words, analyzes sentiment, calculates readability, and provides a concise summary of the text."
 )
 
 # Launch the app