Create backup.app.py

backup.app.py

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+import streamlit as st
+from collections import Counter
+import plotly.express as px
+import numpy as np
+
+def get_word_score(word):
+    # This function returns a score based on the length of the word
+    # Modify this function as per your requirements
+    score = len(word)**2
+    return score
+
+def get_word_frequency(text):
+    # This function returns the word frequency of the given text
+    words = text.split()
+    word_frequency = Counter(words)
+    return word_frequency
+
+# Load the markdown file
+with open('Setup.md', 'r') as file:
+    text = file.read()
+
+
+# Display the parsed markdown
+st.markdown(text, unsafe_allow_html=True)
+
+# Get the word frequency of the markdown text
+word_frequency = get_word_frequency(text)
+
+# Get the top words and their frequency
+top_words = word_frequency.most_common(10)
+top_words_dict = dict(top_words)
+
+# Create a Plotly bar chart to display the top words and their frequency
+fig = px.bar(x=list(top_words_dict.keys()), y=list(top_words_dict.values()), labels={'x':'Word', 'y':'Frequency'})
+st.plotly_chart(fig)
+
+# Calculate the scores for each word based on their length
+word_scores = {word:get_word_score(word) for word in word_frequency}
+top_word_scores = dict(sorted(word_scores.items(), key=lambda item: item[1], reverse=True)[:10])
+
+# Create a Plotly bar chart to display the top words and their scores
+fig = px.bar(x=list(top_word_scores.keys()), y=list(top_word_scores.values()), labels={'x':'Word', 'y':'Score'})
+st.plotly_chart(fig)