streamlit testing

Author: ansjin

streamlit/Dockerfile

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+FROM continuumio/miniconda3:latest
+
+MAINTAINER Anshul Jindal "anshul.jindal@tum.de"
+
+RUN mkdir -p /home/streamlit
+WORKDIR /home/streamlit
+
+RUN conda install --yes \
+    numpy==1.16.3 \
+    pandas==0.24.2 \
+    && conda clean -afy
+
+RUN pip install streamlit 
+RUN pip install xlrd 
+
+COPY . /home/streamlit 
+
+EXPOSE 8501
+
+CMD [ "streamlit", "run", "web.py" ]

streamlit/m_1.xlsx

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+import streamlit as st
+import pandas as pd
+import numpy as np
+
+st.title('Machine Resources Utilization')
+st.sidebar.title('Machine Resources Utilization')
+DATE_COLUMN = 'date/time'
+
+@st.cache
+def load_data():
+    data = pd.read_excel('m_1.xlsx')
+    data.drop(['machine_id', 'mem_gps', 'mkpi'], axis=1, inplace=True)
+    lowercase = lambda x: str(x).lower()
+    data.rename(lowercase, axis='columns', inplace=True)
+    data[DATE_COLUMN] = pd.to_datetime(data['timestamp'])
+    return data
+
+
+data = load_data()
+hour_data_group = data.groupby(data[DATE_COLUMN].dt.hour)
+
+st.sidebar.markdown('Interact with the data here')
+option = st.sidebar.selectbox(
+    'Mean or Mean results to get ',
+     ['mean', 'median'])
+
+'You have selected: ', option
+
+# median stats
+if option == 'median':
+    med_data = pd.DataFrame(data.groupby(data[DATE_COLUMN].dt.hour).median(),
+                            columns=['cpu_util_percent', 'mem_util_percent', 'net_in', 'net_out', 'disk_io_percent'])
+    med_data.reset_index(inplace=True)
+    med_data.drop([DATE_COLUMN], axis=1, inplace=True)
+    st.subheader('Median Resources Utilization per hour of the day in the whole week')
+    if st.sidebar.checkbox('Show raw data'):
+        st.subheader('Raw data')
+        med_data
+    st.line_chart(med_data)
+    st.subheader('Median Resources Utilization per minute for the selected hour of the day in the whole week')
+    # Some number in the range 0-23
+    hour_to_filter = st.sidebar.slider('hour', 0, 23, 17)
+    hour_data = hour_data_group.get_group(hour_to_filter)
+    hour_data
+    hour_median_data = pd.DataFrame(hour_data.groupby(hour_data[DATE_COLUMN].dt.minute).median(),
+                                    columns=['cpu_util_percent', 'mem_util_percent',
+                                             'net_in', 'net_out', 'disk_io_percent'])
+    hour_median_data.reset_index(inplace=True)
+    hour_median_data.drop([DATE_COLUMN], axis=1, inplace=True)
+    st.line_chart(hour_median_data)
+
+
+elif option == 'mean':
+    mean_data = pd.DataFrame(data.groupby(data[DATE_COLUMN].dt.hour).mean(),
+                             columns=['cpu_util_percent', 'mem_util_percent', 'net_in', 'net_out', 'disk_io_percent'])
+    mean_data.reset_index(inplace=True)
+    mean_data.drop([DATE_COLUMN], axis=1, inplace=True)
+    st.subheader('Mean Resources Utilization per hour of the day in the whole week')
+    if st.sidebar.checkbox('Show raw data'):
+        st.subheader('Raw data')
+        mean_data
+    st.line_chart(mean_data)
+    st.subheader('Mean Resources Utilization per minute for the selected hour of the day in the whole week')
+    # Some number in the range 0-23
+    hour_to_filter = st.sidebar.slider('hour', 0, 23, 17)
+    hour_data = hour_data_group.get_group(hour_to_filter)
+    hour_data
+    hour_mean_data = pd.DataFrame(hour_data.groupby(hour_data[DATE_COLUMN].dt.minute).mean(),
+                                  columns=['cpu_util_percent', 'mem_util_percent',
+                                           'net_in', 'net_out', 'disk_io_percent'])
+    hour_mean_data.reset_index(inplace=True)
+    hour_mean_data.drop([DATE_COLUMN], axis=1, inplace=True)
+    st.line_chart(hour_mean_data)
+
+
+
+
+
+
+
+