Upload function.py

function.py

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+# Core Pkgs
+import streamlit as st
+from transformers import pipeline
+from PyPDF2 import PdfFileReader
+import docx2txt
+import base64
+import  re
+import sqlite3
+import time
+from io import StringIO
+import warnings
+warnings.filterwarnings("ignore")
+
+time_str = time.strftime("%Y%m%d-%H%M%S")
+# Loading function the model pipeline from huggingface model
+@st.cache(allow_output_mutation=True)
+def bart():
+    ''' Loading bart model using pipeline api '''
+    summarizer = pipeline('summarization',model='facebook/bart-large-cnn')
+    return summarizer
+    
+def t5():
+    ''' Loading t5 model using pipeline api '''
+    summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base")
+    return summarizer
+
+# def pegasus():
+#     ''' Loading pegasus model using pipeline api '''
+#     summarizer = pipeline('summarization',model='google/pegasus-xsum')
+#     return summarizer
+
+def preprocess_plain_text(x):
+
+    x = x.encode("ascii", "ignore").decode()  # unicode
+    x = re.sub(r"https*\S+", " ", x)  # url
+    x = re.sub(r"@\S+", " ", x)  # mentions
+    x = re.sub(r"#\S+", " ", x)  # hastags
+    x = re.sub(r"\s{2,}", " ", x)  # over spaces
+    x = re.sub("[^.,!?A-Za-z0-9]+", " ", x)  # special charachters except .,!?
+
+    return x
+
+def extract_pdf(file):
+    
+    '''Extract text from PDF file'''
+    
+    pdfReader = PdfFileReader(file)
+    count = pdfReader.numPages
+    all_text = ""
+    for i in range(count):
+        page = pdfReader.getPage(i)
+        all_text += page.extractText()
+
+    return all_text
+
+
+def extract_text_from_file(file):
+
+    '''Extract text from uploaded file'''
+
+    # read text file
+    if file.type == "text/plain":
+        # To convert to a string based IO:
+        stringio = StringIO(file.getvalue().decode("utf-8"))
+
+        # To read file as string:
+        file_text = stringio.read()
+
+    # read pdf file
+    elif file.type == "application/pdf":
+        file_text = extract_pdf(file)
+
+    # read docx file
+    elif (
+        file.type
+        == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+    ):
+        file_text = docx2txt.process(file)
+
+    return file_text
+
+def summary_downloader(raw_text):
+    
+	b64 = base64.b64encode(raw_text.encode()).decode()
+	new_filename = "new_text_file_{}_.txt".format(time_str)
+	st.markdown("#### Download Summary as a File ###")
+	href = f'<a href="data:file/txt;base64,{b64}" download="{new_filename}">Click to Download!!</a>'
+	st.markdown(href,unsafe_allow_html=True)
+
+
+# Storage in A Database
+conn = sqlite3.connect('summarizer_database.db',check_same_thread=False)
+c = conn.cursor() 
+    # Create Fxn From SQL
+def create_table():
+	c.execute('CREATE TABLE IF NOT EXISTS TextTable(text_to_summarize TEXT,summarized_text TEXT,postdate DATE)')
+
+
+def add_data(text_to_summarize,summarized_text,postdate):
+    c.execute('INSERT INTO TextTable(text_to_summarize,summarized_text,postdate) VALUES (?,?,?)',(text_to_summarize,summarized_text,postdate))
+    conn.commit()
+
+def view_all_data():
+	c.execute("SELECT * FROM TextTable")
+	data = c.fetchall()
+	return data