added sample file

.ipynb_checkpoints/app-checkpoint.py

@@ -12,14 +12,21 @@ if st.button('Click for predictions!'):
         
         result = get_single_prediction(feedback)
     
-    st.success(f'Your text has been predicted to fall under the following topics: {result[:-1]}. \n The sentiment of this text is {result[-1]}.')
+    st.success(f'Your text has been predicted to fall under the following topics: {result[:-1]}. The sentiment of this text is {result[-1]}.')
 
 st.write("\n")    
-st.subheader('Or... Upload a csv file if you have many texts')
+st.subheader('Or... Upload a csv file if you have a file instead.')
 st.write("\n")
 
 uploaded_file = st.file_uploader("Please upload a csv file with only 1 column of texts.")
 
+st.download_button(
+     label="Download sample file here",
+     data=sample_file,
+     file_name='sample_results.csv',
+     mime='text/csv',
+ )
+
 if uploaded_file is not None:
     
     with st.spinner('Generating predictions...'):

.ipynb_checkpoints/utils-checkpoint.py

@@ -18,6 +18,8 @@ labels = [
        'facilities', 'location', 'price'
 ]
 
+sample_file = pd.read_csv('sample.csv')
+
 def get_sentiment_label_facebook(list_of_sent_dicts):
     if list_of_sent_dicts['labels'][0] == 'negative':
         return 'negative'

app.py

@@ -12,14 +12,21 @@ if st.button('Click for predictions!'):
         
         result = get_single_prediction(feedback)
     
-    st.success(f'Your text has been predicted to fall under the following topics: {result[:-1]}. \n The sentiment of this text is {result[-1]}.')
+    st.success(f'Your text has been predicted to fall under the following topics: {result[:-1]}. The sentiment of this text is {result[-1]}.')
 
 st.write("\n")    
-st.subheader('Or... Upload a csv file if you have many texts')
+st.subheader('Or... Upload a csv file if you have a file instead.')
 st.write("\n")
 
 uploaded_file = st.file_uploader("Please upload a csv file with only 1 column of texts.")
 
+st.download_button(
+     label="Download sample file here",
+     data=sample_file,
+     file_name='sample_results.csv',
+     mime='text/csv',
+ )
+
 if uploaded_file is not None:
     
     with st.spinner('Generating predictions...'):

sample.csv

@@ -0,0 +1,2 @@
+sequence
+The website was user friendly and the agent provided good solutions

tester.ipynb

@@ -1,49 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "48c76726-b0a4-43e6-9f07-0199e0248d5e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "bd2034e6-1187-4887-9ca7-8b9c0b5c9331",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1ac414f1-37dd-4642-867c-5520a16c1c86",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

utils.py

@@ -18,6 +18,8 @@ labels = [
        'facilities', 'location', 'price'
 ]
 
+sample_file = pd.read_csv('sample.csv')
+
 def get_sentiment_label_facebook(list_of_sent_dicts):
     if list_of_sent_dicts['labels'][0] == 'negative':
         return 'negative'