added example default text

app.py

@@ -44,6 +44,8 @@ pos_tagger = load_pos_model()
 emotion_detector = load_emotion_model()
 ner = load_ner_model()
 
+example_text = "This is example text that contains both names of organizations like Hugging Face and cities like New York, all while portraying an upbeat attitude."
+
 with st.sidebar:
     page = option_menu(menu_title='Menu',
                        menu_icon="robot",
@@ -134,7 +136,7 @@ elif page == "Sentiment Analysis":
         """
     )
 
-    text = st.text_area("Paste text here", value="")
+    text = st.text_area("Paste text here", value=example_text)
 
     if st.button('🔥 Run!'):
         with st.spinner("Loading..."):
@@ -158,7 +160,7 @@ elif page == "Keyword Extraction":
         """
     )
 
-    text = st.text_area("Paste text here", value="")
+    text = st.text_area("Paste text here", value=example_text)
 
     max_keywords = st.slider('# of Keywords Max Limit', min_value=1, max_value=10, value=5, step=1)
 
@@ -191,7 +193,7 @@ elif page == "Part of Speech Tagging":
         """
     )
 
-    text = st.text_area("Paste text here", value="")
+    text = st.text_area("Paste text here", value=example_text)
 
     if st.button('🔥 Run!'):
         with st.spinner("Loading..."):
@@ -213,7 +215,7 @@ elif page == "Emotion Detection":
         """
     )
 
-    text = st.text_area("Paste text here", value="")
+    text = st.text_area("Paste text here", value=example_text)
 
     if st.button('🔥 Run!'):
         with st.spinner("Loading..."):
@@ -237,7 +239,7 @@ elif page == "Named Entity Recognition":
         """
     )
 
-    text = st.text_area("Paste text here", value="")
+    text = st.text_area("Paste text here", value=example_text)
 
     if st.button('🔥 Run!'):
         with st.spinner("Loading..."):

emotion_detection.py

@@ -5,6 +5,13 @@ import pandas as pd
 
 
 class EmotionDetection:
+    """ This class is an example
+
+    Attributes:
+        class_attribute (str): (class attribute) The class attribute
+        instance_attribute (str): The instance attribute
+    """
+
     def __init__(self):
         hub_location = 'cardiffnlp/twitter-roberta-base-emotion'
         self.tokenizer = AutoTokenizer.from_pretrained(hub_location)
@@ -12,7 +19,15 @@ class EmotionDetection:
         self.explainer = SequenceClassificationExplainer(self.model, self.tokenizer)
 
     def justify(self, text):
-        """"""
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
 
         word_attributions = self.explainer(text)
         html = self.explainer.visualize("example.html")
@@ -20,7 +35,15 @@ class EmotionDetection:
         return html
 
     def classify(self, text):
-        """"""
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
 
         tokens = self.tokenizer.encode_plus(text, add_special_tokens=False, return_tensors='pt')
         outputs = self.model(**tokens)
@@ -32,7 +55,15 @@ class EmotionDetection:
         return preds
 
     def run(self, text):
-        """"""
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
 
         preds = self.classify(text)
         html = self.justify(text)

keyword_extraction.py

@@ -6,11 +6,28 @@ import en_core_web_sm
 
 
 class KeywordExtractor:
+    """ This class is an example
+
+    Attributes:
+        class_attribute (str): (class attribute) The class attribute
+        instance_attribute (str): The instance attribute
+    """
+
     def __init__(self):
         self.nlp = en_core_web_sm.load()
         self.nlp.add_pipe("textrank")
 
     def get_keywords(self, text, max_keywords):
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
+
         doc = self.nlp(text)
 
         kws = [i.text for i in doc._.phrases[:max_keywords]]
@@ -18,6 +35,16 @@ class KeywordExtractor:
         return kws
 
     def get_keyword_indicies(self, string_list, text):
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
+
         out = []
         for s in string_list:
             indicies = [[m.start(), m.end()] for m in re.finditer(re.escape(s), text)]
@@ -26,6 +53,16 @@ class KeywordExtractor:
         return out
 
     def merge_overlapping_indicies(self, indicies):
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
+
         # Sort the array on the basis of start values of intervals.
         indicies.sort()
         stack = []
@@ -41,6 +78,16 @@ class KeywordExtractor:
         return stack
 
     def merge_until_finished(self, indicies):
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
+
         len_indicies = 0
         while True:
             merged = self.merge_overlapping_indicies(indicies)
@@ -51,9 +98,15 @@ class KeywordExtractor:
                 len_indicies = len(merged)
 
     def get_annotation(self, text, indicies, kws):
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
 
-        # Convert indicies to list
-        # kws = kws + [i.lower() for i in kws]
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
 
         arr = list(text)
         for idx in sorted(indicies, reverse=True):
@@ -71,6 +124,15 @@ class KeywordExtractor:
         return final_annotation
 
     def generate(self, text, max_keywords):
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
 
         kws = self.get_keywords(text, max_keywords)
 

named_entity_recognition.py

@@ -3,12 +3,29 @@ from transformers import pipeline
 
 
 class NamedEntityRecognition:
+    """ This class is an example
+
+    Attributes:
+        class_attribute (str): (class attribute) The class attribute
+        instance_attribute (str): The instance attribute
+    """
+
     def __init__(self):
         tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
         model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
         self.nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
 
     def get_annotation(self, preds, text):
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
+
         splits = [0]
         entities = {}
         for i in preds:
@@ -29,6 +46,16 @@ class NamedEntityRecognition:
         return final_annotation
 
     def classify(self, text):
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
+
         preds = self.nlp(text)
         ner_annotation = self.get_annotation(preds, text)
         return preds, ner_annotation

part_of_speech_tagging.py

@@ -5,10 +5,27 @@ nltk.download('averaged_perceptron_tagger')
 
 
 class POSTagging:
+    """ This class is an example
+
+    Attributes:
+        class_attribute (str): (class attribute) The class attribute
+        instance_attribute (str): The instance attribute
+    """
+
     def __init__(self):
        pass
 
     def classify(self, text):
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
+
         text = word_tokenize(text)
         preds = nltk.pos_tag(text)
         return preds

sentiment_analysis.py

@@ -5,6 +5,13 @@ import pandas as pd
 
 
 class SentimentAnalysis:
+    """ This class is an example
+
+    Attributes:
+        class_attribute (str): (class attribute) The class attribute
+        instance_attribute (str): The instance attribute
+    """
+
     def __init__(self):
         # Load Tokenizer & Model
         hub_location = 'cardiffnlp/twitter-roberta-base-sentiment'
@@ -23,7 +30,15 @@ class SentimentAnalysis:
         self.explainer = SequenceClassificationExplainer(self.model, self.tokenizer)
 
     def justify(self, text):
-        """"""
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
 
         word_attributions = self.explainer(text)
         html = self.explainer.visualize("example.html")
@@ -31,7 +46,15 @@ class SentimentAnalysis:
         return html
 
     def classify(self, text):
-        """"""
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
 
         tokens = self.tokenizer.encode_plus(text, add_special_tokens=False, return_tensors='pt')
         outputs = self.model(**tokens)
@@ -42,7 +65,15 @@ class SentimentAnalysis:
         return preds
 
     def run(self, text):
-        """"""
+        """
+        The function to add two Complex Numbers.
+
+        Parameters:
+            num (ComplexNumber): The complex number to be added.
+
+        Returns:
+            ComplexNumber: A complex number which contains the sum.
+        """
 
         preds = self.classify(text)
         html = self.justify(text)