Update app.py

app.py

@@ -1,11 +1,36 @@
 import streamlit as st
-from transformers import pipeline
+import pandas as pd
+import numpy as np
 
-pipe = pipeline('sentiment-analysis')
+st.title('Uber pickups in NYC')
 
-st.title("Sentiment Analysis")
-text = st.text_area('Enter some text in the box below!')
+DATE_COLUMN = 'date/time'
+DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
+            'streamlit-demo-data/uber-raw-data-sep14.csv.gz')
 
-if text:
-    out = pipe(text)
-    st.json(out)
+@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)