Update app.py

app.py

@@ -172,6 +172,56 @@ def on_select(instruction1, instruction2, instruction3, evt: gr.SelectData):
 #----------------#
 # Grammar metrics
 import re
+from textstat import textstat
+
+#def pre_process_text(text):
+#    sentences_list = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
+#    # Split the elements of the list by newline characters
+#    split_sentences = []
+#    for sentence in sentences_list:
+#        split_sentences.extend(re.split(r'\n+', sentence))
+#    # Remove empty elements
+#    cleaned_sentences = [sentence for sentence in split_sentences if sentence.strip()]
+#    sentences_number = len(cleaned_sentences)
+#    return cleaned_sentences, sentences_number
+
+# Function to clean the sentences list and return words only
+#def extract_words(sentences):
+#    words = []
+#    for sentence in sentences:
+#        # Extract words using regex, ignoring special characters
+#        words.extend(re.findall(r'\b\w+\b', sentence))
+#    return words
+
+#def count_syllables(word):
+#    return len(re.findall(r'[aeiouyAEIOUY]', word))
+
+#def flesch_kincaid_grade_level(text):
+#    sentences, sentences_count = pre_process_text(text)
+#    words = extract_words(sentences)
+#    syllables = sum([count_syllables(word) for word in text.split()])
+#
+#    if sentences_count == 0 or words == 0:
+#        return float('nan')  # Return NaN to indicate an error
+#    return 0.39 * (words / sentences_count) + 11.8 * (syllables / words) - 15.59
+
+#def flesch_reading_ease(text):
+#    sentences, sentences_count = pre_process_text(text)
+#    words = extract_words(sentences)
+#    syllables = sum([count_syllables(word) for word in words])
+#
+#    if sentences_count == 0 or words == 0:
+#        return float('nan')  # Return NaN to indicate an error
+#   return 206.835 - 1.015 * (words / sentences_count) - 84.6 * (syllables / words)
+
+#def gunning_fog_index(text):
+#    sentences, sentences_count = pre_process_text(text)
+#    words = extract_words(sentences)
+#    complex_words = len([word for word in words if count_syllables(word) >= 3])
+#
+#    if sentences_count == 0 or words == 0:
+#        return float('nan')  # Return NaN to indicate an error
+#    return 0.4 * ((words / sentences_count) + 100 * (complex_words / words))
 
 def pre_process_text(text):
     sentences_list = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
@@ -181,47 +231,21 @@ def pre_process_text(text):
         split_sentences.extend(re.split(r'\n+', sentence))
     # Remove empty elements
     cleaned_sentences = [sentence for sentence in split_sentences if sentence.strip()]
-    sentences_number = len(cleaned_sentences)
-    return cleaned_sentences, sentences_number
-
-# Function to clean the sentences list and return words only
-def extract_words(sentences):
-    words = []
-    for sentence in sentences:
-        # Extract words using regex, ignoring special characters
-        words.extend(re.findall(r'\b\w+\b', sentence))
-    return words
-
-def count_syllables(word):
-    return len(re.findall(r'[aeiouyAEIOUY]', word))
+    string_sentences = (" ".join(cleaned_sentences))
+    return string_sentences
 
 def flesch_kincaid_grade_level(text):
-    sentences, sentences_count = pre_process_text(text)
-    words = extract_words(sentences)
-    syllables = sum([count_syllables(word) for word in text.split()])
-
-    if sentences_count == 0 or words == 0:
-        return float('nan')  # Return NaN to indicate an error
-    return 0.39 * (words / sentences_count) + 11.8 * (syllables / words) - 15.59
+    sentences = pre_process_text(text)
+    return textstat.flesch_kincaid_grade(sentences)
 
 def flesch_reading_ease(text):
-    sentences, sentences_count = pre_process_text(text)
-    words = extract_words(sentences)
-    syllables = sum([count_syllables(word) for word in words])
-
-    if sentences_count == 0 or words == 0:
-        return float('nan')  # Return NaN to indicate an error
-    return 206.835 - 1.015 * (words / sentences_count) - 84.6 * (syllables / words)
+    sentences = pre_process_text(text)
+    return textstat.flesch_reading_ease(sentences)
 
 def gunning_fog_index(text):
-    sentences, sentences_count = pre_process_text(text)
-    words = extract_words(sentences)
-    complex_words = len([word for word in words if count_syllables(word) >= 3])
-
-    if sentences_count == 0 or words == 0:
-        return float('nan')  # Return NaN to indicate an error
-    return 0.4 * ((words / sentences_count) + 100 * (complex_words / words))
-
+    sentences = pre_process_text(text)
+    return textstat.gunning_fog(sentences)
+    
 def calculate_readability_metrics(text):
     fk_grade_level = flesch_kincaid_grade_level(text)
     fk_reading_ease = flesch_reading_ease(text)