Streamlit

.streamlit/config.toml

@@ -0,0 +1,2 @@
+[server]
+maxUploadSize = 4000

README.md

@@ -1 +1,6 @@
-# email-2-faq
+# email-2-faq
+
+
+### How to run web app
+1. cd email2faq/streamlit_web_app
+2. streamlit run app.py

email2faq/data/asset/sample_email_dataset_raw.csv

@@ -317,7 +317,6 @@ X-Folder: \Steven_Kean_Nov2001_1\Notes Folders\All documents
 X-Origin: KEAN-S
 X-FileName: skean.nsf
 
-
 FYI. what are you upto? is everythimg ok? What are the different types of laptops available?
 What are the specifications of each type of laptop?
 What is the battery life of each laptop model?

email2faq/streamlit_web_app/app.py

@@ -0,0 +1,257 @@
+from pathlib import Path
+import pathlib
+import base64
+import re
+import os
+import streamlit as st
+import warnings
+warnings.filterwarnings('ignore')
+import numpy as np
+import pandas as pd
+import sys
+
+temp = pathlib.Path(__file__).parent.resolve()
+path1 = os.path.dirname(temp)
+
+sys.path.insert(1, path1)
+
+import fgen
+
+path2 = os.path.join(temp, 'user_data')
+
+
+# set page layout
+st.set_page_config(
+    page_title="Email-2-FAQ",
+    page_icon=":email:",
+    layout="centered",
+    initial_sidebar_state="auto",
+)
+
+# Markdown cannot be directly rendered from local machine using st.markdown , we need to use st.image for that and these funcitons below
+# parse the markdown file and find the markdown way of mentioning images ![]<> and convert it into html and then it is seen.
+def markdown_images(markdown):
+    # example image markdown:
+    # ![Test image](images/test.png "Alternate text")
+    images = re.findall(
+        r'(!\[(?P<image_title>[^\]]+)\]\((?P<image_path>[^\)"\s]+)\s*([^\)]*)\))', markdown)
+    return images
+
+
+def img_to_bytes(img_path):
+    img_bytes = Path(img_path).read_bytes()
+    encoded = base64.b64encode(img_bytes).decode()
+    return encoded
+
+
+def img_to_html(img_path, img_alt):
+    img_format = img_path.split(".")[-1]
+    img_html = f'<img src="data:image/{img_format.lower()};base64,{img_to_bytes(img_path)}" alt="{img_alt}" style="max-width: 100%;">'
+
+    return img_html
+
+
+def markdown_insert_images(markdown):
+    images = markdown_images(markdown)
+
+    for image in images:
+        image_markdown = image[0]
+        image_alt = image[1]
+        image_path = image[2]
+        if os.path.exists(image_path):
+            markdown = markdown.replace(
+                image_markdown, img_to_html(image_path, image_alt))
+    return markdown
+
+
+def get_file_content_as_string(path):
+    with open(path, 'r') as f:
+        readme_text = f.read()
+    readme_text = markdown_insert_images(readme_text)
+    return readme_text
+
+
+# function to show the developer information
+def about():
+    st.sidebar.markdown("# A B O U T")
+    st.sidebar.markdown("""
+
+### Mentors
+
+- Aryan Amit Barsainyan
+- Ashish Bharath
+- Amandeep Singh
+
+### Members
+
+- Bharadwaja M Chittrapragada
+- Karan Kumar Bhagat
+- Mohammad Aadil Shabier
+- Tejashree Chaudhari
+
+### [GitHub Repo](https://github.com/IEEE-NITK/email-2-faq)
+
+## Acknowledgements
+
+Special thanks to Pranav DV, Rakshit P, Nishant Nayak and the seniors for guiding and reviewing us during the project.
+    """)
+
+
+
+# This will be run the app page
+def run_app():
+
+    st.title('Email-2-FAQ :email:')
+    st.caption('A web-app to generate FAQs from emails')
+
+    instruction = st.markdown('''
+##### How to Use:question:
+
+1. Upload the Emails(_in csv format_) to the upload section in the page.
+
+##### Results (__FGen Framework__):email:
+
+1. __QC__(_Query Classfier_)  : The First text box preprocesses the emails and outputs the queries from all the emails uploaded.
+2. __FGG__ (_FAQ Group Generator_) : The second text box clusters similar quiries into groups.
+3. __FG__  (_FAQ Generator Subsytem_) : The third and final text box shows Frequently asked questions from the groups generated. 
+
+_Terms:_\n
+`Threshold:` The minimum threshold to form clusters(threshild corresponds to the amount of similarity required to consider to 2 sentences in the same cluster) i.e if the threshold is high, it means that clusters will have only a few really similar sentences and this will result in more number of clusters.\n
+`Frequency:` The minimum frequency of repetition for the question to be considered in as a Frequent question.\n
+
+''')
+
+    # further instructions for Side Bar
+    warning_text = st.sidebar.info(
+        'Go to "Project Report" to read more about the app')
+
+    # display the developer information
+    about()
+
+
+
+    # define the fucntion predictions
+    def predict(filepath,thres,freq):
+        pred = fgen.generate_faq_fgen(filepath,thres,freq)
+        # pred = {'valid_queries': ["what are you upto", "is everything ok", "what are different types of laptops available", "what are specifications of each type of laptop"], 'query_clusters': [['is everything ok', 'what will be good specs of the gaming laptop'], ['what are different types of laptops available', 'what is warranty period of laptops']], 'valid_faq': ['what is best gaming laptop?', 'what is the best laptop to buy?']}
+        return pred
+
+
+    # for aesthetic purposes
+    st.write("-"*34)
+
+
+    # display the upload image interface
+    uploaded_file = st.file_uploader(
+        "Choose an file", type=["csv"])
+
+
+    # check for errors in the file
+    if uploaded_file is not None:
+        try:
+            text = uploaded_file.read().decode("utf-8")
+            # st.write(text)
+            filename = 'input.csv'
+            filepath = os.path.join(path2, filename)
+            print(filepath, file=sys.stderr) 
+            col1, col2 = st.columns([2,3])
+            with col1:
+                st.write(' ')
+
+            with col2:
+                gen_faq = st.button(
+                label="Generate FAQ",
+                type="primary")
+
+            thres = st.slider("Threshold",min_value = 0.01, max_value = 0.99,value = 0.35)
+            freq = st.slider("Frequency",min_value = 1,value = 1)
+            if(gen_faq):
+                with st.spinner(text=" Getting the predictions.."):
+                    with open(filepath,'w') as f:
+                        f.write(text)
+                    pred = predict(filepath,thres,freq)
+                qc_col, fgg_col, faq_col = st.columns(3)
+                with qc_col:
+                        st.markdown("#### Queries")
+                        st.caption("Queries extracted from the emails")
+                        st.markdown("---")
+                        n = 1
+                        for i in pred["valid_queries"]:
+                            st.write(f"{n}) {i}")
+                            n+=1
+                        st.markdown("---")
+
+                with fgg_col:
+                    st.markdown("#### Query Clusters")
+                    st.caption("Groups of similar queries")
+                    st.markdown("---")
+                    n = 1
+                    for cluster in pred["query_clusters"]:
+                        st.markdown(f"##### Cluster {n}")
+                        for query in cluster:
+                            st.markdown(i)
+                        n+=1
+                    st.markdown("---")
+
+                with faq_col:       
+                    st.markdown("### FAQ")
+                    st.caption("Frequently asked questions")
+                    st.markdown("---")
+                    n = 1
+                    for i in pred["valid_faq"]:
+                        st.write(f"{n}) {i}")
+                        n+=1
+                    st.markdown("---")
+
+
+
+
+        # display the error message of the format is not a csv format
+        except Exception as e:
+            print(e)
+            st.error('Please upload the data in .csv format!')
+
+
+
+# defining each page
+# This will be the landing page: we will start with project report
+def readme():
+
+    success_text = st.sidebar.success('To continue, select "WebApp" ')
+    # This is for the  home page
+    st.title('Email-2-FAQ')
+    st.caption('A web-app to generate FAQs from emails')
+    Main_image = st.image('https://static.wixstatic.com/media/0fc399_1053e45158ab4724a1579d7790fdee5d~mv2.gif')
+    readme_text = st.markdown(get_file_content_as_string(
+        'report/report.md'), unsafe_allow_html=True)
+
+    about()
+
+
+
+
+# This is for the side menu for selecting the sections of the app
+st.sidebar.markdown('# M E N U')
+page_bg_img = '''
+<style>
+.stApp{
+background-image: url("https://images.unsplash.com/photo-1497091071254-cc9b2ba7c48a?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1174&q=80");
+background-size: cover;
+
+}
+</style>
+'''
+
+st.markdown(page_bg_img, unsafe_allow_html=True)
+
+page_names_to_funcs = {
+    "WebApp": run_app,
+    "Project Report": readme,
+}
+
+
+selected_page = st.sidebar.selectbox(
+    "Choose the app mode", page_names_to_funcs.keys())
+page_names_to_funcs[selected_page]()
+
+