Upload 6 files

Dockerfile

@@ -0,0 +1,20 @@
+#Base Image to use
+FROM python:3.7.9
+
+#Expose port 8080
+EXPOSE 8080
+
+#Copy Requirements.txt file into app directory
+COPY requirements.txt app/requirements.txt
+
+#install all requirements in requirements.txt
+RUN pip install -r app/requirements.txt
+
+#Copy all files in current directory into app directory
+COPY . /app
+
+#Change Working Directory to app directory
+WORKDIR /app
+
+#Run the application on port 8080
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8080", "--server.address=0.0.0.0"]

app.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+
+
+@author: hamzafarooq@ MABA CLASS
+"""
+
+import streamlit as st
+import pandas as pd
+
+import plotly.express as px
+
+
+
+
+
+st.title("Uma")
+st.markdown("This is a demo Streamlit app.")
+st.markdown("My name is UN, hello world!..")
+st.markdown("This is v2")
+
+@st.cache(persist=True)
+def load_data():
+    df = pd.read_csv("https://datahub.io/machine-learning/iris/r/iris.csv")
+    return(df)
+
+
+
+def run():
+    st.subheader("Iris Data Loaded into a Pandas Dataframe.")
+
+    df = load_data()
+
+
+
+    disp_head = st.sidebar.radio('Select DataFrame Display Option:',('Head', 'All'),index=0)
+
+
+
+    #Multi-Select
+    #sel_plot_cols = st.sidebar.multiselect("Select Columns For Scatter Plot",df.columns.to_list()[0:4],df.columns.to_list()[0:2])
+
+    #Select Box
+    #x_plot = st.sidebar.selectbox("Select X-axis Column For Scatter Plot",df.columns.to_list()[0:4],index=0)
+    #y_plot = st.sidebar.selectbox("Select Y-axis Column For Scatter Plot",df.columns.to_list()[0:4],index=1)
+
+
+    if disp_head=="Head":
+        st.dataframe(df.head())
+    else:
+        st.dataframe(df)
+    #st.table(df)
+    #st.write(df)
+
+
+    #Scatter Plot
+    fig = px.scatter(df, x=df["sepallength"], y=df["sepalwidth"], color="class",
+                 size='petallength', hover_data=['petalwidth'])
+
+    fig.update_layout({
+                'plot_bgcolor': 'rgba(0, 0, 0, 0)'})
+
+    fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray')
+    fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray')
+
+    st.write("\n")
+    st.subheader("Scatter Plot")
+    st.plotly_chart(fig, use_container_width=True)
+
+
+    #Add images
+    #images = ["<image_url>"]
+    #st.image(images, width=600,use_container_width=True, caption=["Iris Flower"])
+
+
+
+
+
+if __name__ == '__main__':
+    run()

app.yaml

@@ -0,0 +1,3 @@
+service: default
+runtime: custom
+env: flex

apps.py

@@ -0,0 +1,140 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+import plotly.graph_objects as go
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+
+
+iris_data = load_iris()
+# separate the data into features and target
+features = pd.DataFrame(
+    iris_data.data, columns=iris_data.feature_names
+)
+target = pd.Series(iris_data.target)
+
+# split the data into train and test
+x_train, x_test, y_train, y_test = train_test_split(
+    features, target, test_size=0.2, stratify=target
+)
+
+
+class StreamlitApp:
+
+    def __init__(self):
+        self.model = RandomForestClassifier()
+
+    def train_data(self):
+        self.model.fit(x_train, y_train)
+        return self.model
+
+    def construct_sidebar(self):
+
+        cols = [col for col in features.columns]
+
+        st.sidebar.markdown(
+            '<p class="header-style">Iris Data Classification</p>',
+            unsafe_allow_html=True
+        )
+        sepal_length = st.sidebar.selectbox(
+            f"Select {cols[0]}",
+            sorted(features[cols[0]].unique())
+        )
+
+        sepal_width = st.sidebar.selectbox(
+            f"Select {cols[1]}",
+            sorted(features[cols[1]].unique())
+        )
+
+        petal_length = st.sidebar.selectbox(
+            f"Select {cols[2]}",
+            sorted(features[cols[2]].unique())
+        )
+
+        petal_width = st.sidebar.selectbox(
+            f"Select {cols[3]}",
+            sorted(features[cols[3]].unique())
+        )
+        values = [sepal_length, sepal_width, petal_length, petal_width]
+
+        return values
+
+    def plot_pie_chart(self, probabilities):
+        fig = go.Figure(
+            data=[go.Pie(
+                    labels=list(iris_data.target_names),
+                    values=probabilities[0]
+            )]
+        )
+        fig = fig.update_traces(
+            hoverinfo='label+percent',
+            textinfo='value',
+            textfont_size=15
+        )
+        return fig
+
+    def construct_app(self):
+
+        self.train_data()
+        values = self.construct_sidebar()
+
+        values_to_predict = np.array(values).reshape(1, -1)
+
+        prediction = self.model.predict(values_to_predict)
+        prediction_str = iris_data.target_names[prediction[0]]
+        probabilities = self.model.predict_proba(values_to_predict)
+
+        st.markdown(
+            """
+            <style>
+            .header-style {
+                font-size:25px;
+                font-family:sans-serif;
+            }
+            </style>
+            """,
+            unsafe_allow_html=True
+        )
+
+        st.markdown(
+            """
+            <style>
+            .font-style {
+                font-size:20px;
+                font-family:sans-serif;
+            }
+            </style>
+            """,
+            unsafe_allow_html=True
+        )
+        st.markdown(
+            '<p class="header-style"> Iris Data Predictions </p>',
+            unsafe_allow_html=True
+        )
+
+        column_1, column_2 = st.columns(2)
+        column_1.markdown(
+            f'<p class="font-style" >Prediction </p>',
+            unsafe_allow_html=True
+        )
+        column_1.write(f"{prediction_str}")
+
+        column_2.markdown(
+            '<p class="font-style" >Probability </p>',
+            unsafe_allow_html=True
+        )
+        column_2.write(f"{probabilities[0][prediction[0]]}")
+
+        fig = self.plot_pie_chart(probabilities)
+        st.markdown(
+            '<p class="font-style" >Probability Distribution</p>',
+            unsafe_allow_html=True
+        )
+        st.plotly_chart(fig, use_container_width=True)
+
+        return self
+
+
+sa = StreamlitApp()
+sa.construct_app()

requirements.txt

@@ -0,0 +1,4 @@
+pandas
+streamlit
+plotly
+sklearn