Upload app.py

app.py

@@ -0,0 +1,54 @@
+import streamlit
+import pandas as pd
+import torch
+from transformers import pipeline
+import streamlit as st
+
+def app():
+    st.title("Text Summarization  🤓")
+
+    st.markdown("This is a Web application that Summarizes Text 😎")
+    upload_file = st.file_uploader('Upload a file containing Text data')
+    button = st.button("Summarize")
+
+    st.cache(allow_output_mutation=True)
+    def facebook_bart_model():
+        summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+        return summarizer
+    summarizer= facebook_bart_model()
+
+    def text_summarizer(text):
+        a = summarizer(text, max_length=150, min_length=30, do_sample=False)
+        return a[0]['summary_text']
+
+
+    # Check to see if a file has been uploaded
+    if upload_file is not None and button:
+        st.success("Summarizing Text, Please wait...")
+        # If it has then do the following:
+
+        # Read the file to a dataframe using pandas
+        df = pd.read_csv(upload_file)
+
+        # Create a section for the dataframe header
+
+        df1 = df.copy()
+        df1['summarized_text'] = df1['Dialog'].apply(text_summarizer)
+
+        df2 = df1[['Name','summarized_text']]
+        st.write(df2.head(5))
+
+        @st.cache
+        def convert_df(dataframe):
+            return dataframe.to_csv().encode('utf-8')
+
+        csv = convert_df(df2)
+        st.download_button(label="Download CSV", data=csv, file_name='summarized_output.csv', mime='text/csv')
+
+
+
+
+
+
+if __name__ == "__main__":
+    app()