Upload app.py

app.py

@@ -0,0 +1,107 @@
+# Core Pkgs
+import streamlit as st
+from function import *
+# EDA Pkgs
+import pandas as pd
+import matplotlib.pyplot as plt
+from wordcloud import WordCloud
+# Utils
+from datetime import datetime
+warnings.filterwarnings("ignore")
+
+# page info setup
+menu_items = {
+	'Get help':'https://https://www.linkedin.com/in/ayomide-ishola-924014162/' ,
+	'Report a bug': 'https://www.linkedin.com/in/ayomide-ishola-924014162/',
+	'About': '''
+	## My Custom App
+
+	Some markdown to show in the About dialog.
+	'''
+}
+#page configuration
+st.set_page_config(page_title="Article Summerizer", page_icon="./favicon/favicon.ico",menu_items=menu_items)
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
+def main():
+        # This is used to hide the made with streamlit watermark
+        hide_streamlit_style = """
+                <style>
+                footer {visibility: hidden;}
+                </style>
+                """
+        st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+        # Article Summerizer heading
+        st.markdown("<h1 style = 'color:green; align:center; font-size: 40px;'> Article Summerizer</h1>", unsafe_allow_html=True)
+        
+        # control for Model Settings
+        st.sidebar.markdown("<h4 style = 'color:green; align:center; font-size: 20px;'> Model Settings</h1>", unsafe_allow_html=True)
+        max_length= st.sidebar.slider("Maximum length of the generated text is  500 tokens",min_value=100,max_value=500)
+        min_length= st.sidebar.slider("Minimum length of the generated text",min_value=30)
+        model_type = st.sidebar.selectbox("Model type", options=["Bart","T5", "Bart + T5"])
+        
+        # This function is used to upload a .txt, .pdf, .docx file for summarization
+        upload_doc = st.file_uploader("Upload a .txt, .pdf, .docx file for summarization")
+        
+        st.markdown("<h3 style='text-align: center; color: green;'>or</h3>",unsafe_allow_html=True)
+
+        #This function is used to Type your Message (text area)
+        plain_text = st.text_area("Type your Message",height=200)
+
+        # this is used to control the logic of the code
+        if upload_doc:
+            clean_text = preprocess_plain_text(extract_text_from_file(upload_doc))
+        else:
+            clean_text = preprocess_plain_text(plain_text)
+            
+        summarize = st.button("Summarize")        
+        
+        # called on toggle button [summarize]
+        if summarize:
+            if model_type == "Bart":
+                text_to_summarize = clean_text
+
+                with st.spinner(
+                    text="Loading Bart Model and Extracting summary. This might take a few seconds depending on the length of your text..."):
+                    summarizer_model = bart()
+                    summarized_text = summarizer_model(text_to_summarize, max_length=max_length ,min_length=min_length)
+                    summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text])
+            
+            elif model_type == "T5":
+                text_to_summarize = clean_text
+
+                with st.spinner(
+                    text="Loading T5 Model and Extracting summary. This might take a few seconds depending on the length of your text..."):
+                    summarizer_model = t5()
+                    summarized_text = summarizer_model(text_to_summarize, max_length=max_length, min_length=min_length)
+                    summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text]) 
+
+            elif model_type == "Bart + T5":
+                text_to_summarize = clean_text
+
+                with st.spinner(
+                    text="Loading Bart + T5 Models and Extracting summary. This might take a few seconds depending on the length of your text..."):
+                    summarizer_model = bart_t5()
+                    summarized_text = summarizer_model(text_to_summarize, max_length=max_length, min_length=min_length)
+                    summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text]) 
+
+            res_col1 ,res_col2 = st.columns(2)
+            with res_col1:
+                st.subheader("Generated Text Visualization")
+                # Create and generate a word cloud image:
+                wordcloud = WordCloud().generate(summarized_text)
+                # Display the generated image:
+                plt.imshow(wordcloud, interpolation='bilinear')
+                plt.axis("off")
+                plt.show()
+                st.pyplot()
+                summary_downloader(summarized_text)   
+                
+            with res_col2:
+                st.subheader("Summarized Text Output")
+                st.success("Summarized Text")
+                st.write(summarized_text)
+
+if __name__ == '__main__':
+	main()