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Upload app.py
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app.py
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@@ -4,7 +4,6 @@ import spacy
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import nltk
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import tensorflow as tf
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import streamlit as st
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import streamlit.components.v1 as components
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from streamlit_extras.add_vertical_space import add_vertical_space
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from bs4 import BeautifulSoup
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from gensim.models import Word2Vec
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@@ -38,27 +37,6 @@ def streamlit_config():
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add_vertical_space(4)
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def text_color_change(input_file):
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# Add style to change the text color to white
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styled_html = f"""
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<!DOCTYPE html>
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<html>
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<head>
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<style>
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body {{
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color: white;
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}}
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</style>
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</head>
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<body>
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{input_file}
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</body>
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</html>
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"""
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return styled_html
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def text_extract_from_html(html_file):
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# Read the uploaded HTML file
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@@ -149,7 +127,13 @@ def prediction(input_file):
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# Find the Confidence
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confidence = round(np.max(prediction)*100, 2)
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@@ -169,19 +153,6 @@ input_file = st.file_uploader('Upload an HTML file', type='html')
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if input_file is not None:
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add_vertical_space(1)
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html_content = input_file.read().decode('utf-8')
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html_content = text_color_change(html_content)
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components.html(html=html_content, width=700, height=300, scrolling=True)
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predicted_class, confidence = prediction(input_file)
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add_vertical_space(1)
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st.markdown(f'<h4 style="text-align: center; color: orange;">{confidence}% Match Found</h4>',
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unsafe_allow_html=True)
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add_vertical_space(1)
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st.markdown(f'<h3 style="text-align: center; color: green;">{predicted_class}</h3>',
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unsafe_allow_html=True)
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import nltk
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import tensorflow as tf
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import streamlit as st
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from streamlit_extras.add_vertical_space import add_vertical_space
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from bs4 import BeautifulSoup
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from gensim.models import Word2Vec
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add_vertical_space(4)
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def text_extract_from_html(html_file):
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# Read the uploaded HTML file
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# Find the Confidence
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confidence = round(np.max(prediction)*100, 2)
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add_vertical_space(2)
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st.markdown(f'<h4 style="text-align: center; color: orange;">{confidence}% Match Found</h4>',
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unsafe_allow_html=True)
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add_vertical_space(1)
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st.markdown(f'<h3 style="text-align: center; color: green;">{predicted_class}</h3>',
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unsafe_allow_html=True)
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if input_file is not None:
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prediction(input_file)
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