Update app.py

app.py

@@ -1,53 +1,11 @@
 import streamlit as st
-import pandas as pd
-import numpy as np
-from annotated_text import annotated_text
+from transformers import pipeline
 
+pipe = pipeline('sentiment-analysis')
 
-annotated_text(
-    "This ",
-    ("is", "verb", "#8ef"),
-    " some ",
-    ("annotated", "adj", "#faa"),
-    ("text", "noun", "#afa"),
-    " for those of ",
-    ("you", "pronoun", "#fea"),
-    " who ",
-    ("like", "verb", "#8ef"),
-    " this sort of ",
-    ("thing", "noun", "#afa"),
-    "."
-)
+st.title("Sentiment Analysis")
+text = st.text_area('Enter some text in the box below!')
 
-st.title('Uber pickups in NYC')
-
-DATE_COLUMN = 'date/time'
-DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
-            'streamlit-demo-data/uber-raw-data-sep14.csv.gz')
-
-@st.cache
-def load_data(nrows):
-    data = pd.read_csv(DATA_URL, nrows=nrows)
-    lowercase = lambda x: str(x).lower()
-    data.rename(lowercase, axis='columns', inplace=True)
-    data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
-    return data
-
-data_load_state = st.text('Loading data...')
-data = load_data(10000)
-data_load_state.text("Done! (using st.cache)")
-
-if st.checkbox('Show raw data'):
-    st.subheader('Raw data')
-    st.write(data)
-
-st.subheader('Number of pickups by hour')
-hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0]
-st.bar_chart(hist_values)
-
-# Some number in the range 0-23
-hour_to_filter = st.slider('hour', 0, 23, 17)
-filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]
-
-st.subheader('Map of all pickups at %s:00' % hour_to_filter)
-st.map(filtered_data)
+if text:
+    out = pipe(text)
+    st.json(out)