Delete intro.py

intro.py

@@ -1,79 +0,0 @@
-import streamlit as st
-from textblob import TextBlob
-from collections import Counter
-
-import re
-import spacy
-
-# ... (Rest of the code for Title, Introduction, and Basic Libraries) ...
-
-# Sample Project 2: Word Frequency Analysis
-st.subheader("2. Word Frequency Analysis")
-
-st.write("""
-Word frequency analysis helps us understand which words are most commonly used in a given text. 
-""")
-
-# Input for Word Frequency
-word_input = st.text_area("Enter text for word frequency analysis:", "Natural language processing is a branch of artificial intelligence. NLP is a fascinating field.")
-
-def word_frequency(text):
-    """Calculates word frequencies in the given text.
-    Args:
-        text: The input text.
-    Returns:
-        A list of tuples, where each tuple contains a word and its frequency.
-    """
-    # Preprocess the text:
-    # - Convert to lowercase
-    # - Remove punctuation 
-    # - Remove stop words (optional)
-    text = text.lower()
-    text = re.sub(r"[^\w\s]", "", text) 
-    # Uncomment the line below if you want to remove stop words
-    # from nltk.corpus import stopwords
-    # stop_words = set(stopwords.words('english'))
-    # words = [word for word in text.split() if word not in stop_words] 
-    words = text.split() 
-
-    # Count word frequencies
-    word_counts = Counter(words) 
-    return word_counts.most_common(10)
-
-# Display the word frequency result
-if word_input:
-    word_counts = word_frequency(word_input)
-    st.write("Top 10 most frequent words:")
-    for word, count in word_counts:
-        st.write(f"- {word}: {count}")
-
-# Sample Project 3: Named Entity Recognition (NER) with spaCy
-st.subheader("3. Named Entity Recognition (NER) with spaCy")
-
-st.write("""
-Named Entity Recognition (NER) identifies and classifies named entities like people, organizations, locations, dates, etc.
-""")
-
-# Load spaCy model
-nlp = spacy.load("en_core_web_sm") 
-
-# Input for NER
-ner_input = st.text_area("Enter text for Named Entity Recognition:", "Apple Inc. is looking to buy a startup in San Francisco.")
-
-def extract_entities(text):
-    """Extracts named entities from the given text using spaCy.
-    Args:
-        text: The input text.
-    Returns:
-        A list of tuples, where each tuple contains the entity text and its label.
-    """
-    doc = nlp(text)
-    entities = [(ent.text, ent.label_) for ent in doc.ents]
-    return entities
-
-# Display the NER result
-if ner_input:
-    entities = extract_entities(ner_input)
-    st.write("Named Entities found:")
-    for entity in entities:
-        st.write(f"- {entity[0]} ({entity[1]})")