Update streamlit_app.py

.github/workflows/azure-webapps-python.yml

@@ -0,0 +1,86 @@
+# This workflow will build and push a Python application to an Azure Web App when a commit is pushed to your default branch.
+#
+# This workflow assumes you have already created the target Azure App Service web app.
+# For instructions see https://docs.microsoft.com/en-us/azure/app-service/quickstart-python?tabs=bash&pivots=python-framework-flask
+#
+# To configure this workflow:
+#
+# 1. Download the Publish Profile for your Azure Web App. You can download this file from the Overview page of your Web App in the Azure Portal.
+#    For more information: https://docs.microsoft.com/en-us/azure/app-service/deploy-github-actions?tabs=applevel#generate-deployment-credentials
+#
+# 2. Create a secret in your repository named AZURE_WEBAPP_PUBLISH_PROFILE, paste the publish profile contents as the value of the secret.
+#    For instructions on obtaining the publish profile see: https://docs.microsoft.com/azure/app-service/deploy-github-actions#configure-the-github-secret
+#
+# 3. Change the value for the AZURE_WEBAPP_NAME. Optionally, change the PYTHON_VERSION environment variables below.
+#
+# For more information on GitHub Actions for Azure: https://github.com/Azure/Actions
+# For more information on the Azure Web Apps Deploy action: https://github.com/Azure/webapps-deploy
+# For more samples to get started with GitHub Action workflows to deploy to Azure: https://github.com/Azure/actions-workflow-samples
+
+name: Build and deploy Python app to Azure Web App
+
+env:
+  AZURE_WEBAPP_NAME: your-app-name  # set this to the name of your Azure Web App
+  PYTHON_VERSION: '3.8'              # set this to the Python version to use
+
+on:
+  push:
+    branches: [ "master" ]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python version
+        uses: actions/[email protected]
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          cache: 'pip'
+
+      - name: Create and start virtual environment
+        run: |
+          python -m venv venv
+          source venv/bin/activate
+
+      - name: Install dependencies
+        run: pip install -r requirements.txt
+
+      # Optional: Add step to run tests here (PyTest, Django test suites, etc.)
+
+      - name: Upload artifact for deployment jobs
+        uses: actions/upload-artifact@v3
+        with:
+          name: python-app
+          path: |
+            .
+            !venv/
+
+  deploy:
+    permissions:
+      contents: none
+    runs-on: ubuntu-latest
+    needs: build
+    environment:
+      name: 'Development'
+      url: ${{ steps.deploy-to-webapp.outputs.webapp-url }}
+
+    steps:
+      - name: Download artifact from build job
+        uses: actions/download-artifact@v3
+        with:
+          name: python-app
+          path: .
+
+      - name: 'Deploy to Azure Web App'
+        id: deploy-to-webapp
+        uses: azure/webapps-deploy@v2
+        with:
+          app-name: ${{ env.AZURE_WEBAPP_NAME }}
+          publish-profile: ${{ secrets.AZURE_WEBAPP_PUBLISH_PROFILE }}

azure-pipelines-1.yml

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+trigger:
+- master
+
+pool:
+  vmImage: 'ubuntu-latest'
+
+steps:
+- task: UsePythonVersion@0
+  inputs:
+    versionSpec: '3.x'  # Use the latest Python 3 version
+    addToPath: true
+
+- script: |
+    python -m pip install --upgrade pip
+    pip install -r requirements.txt
+  displayName: 'Install dependencies'
+
+- script: |
+    pytest
+  displayName: 'Run tests'
+
+- script: |
+   streamlit_app.py  # Replace app.py with the name of your Streamlit application file
+  displayName: 'Build Streamlit application'
+
+# Add deployment steps here to deploy your Streamlit application to your hosting platform

azure-pipelines.yml

@@ -0,0 +1,36 @@
+# Python package
+# Create and test a Python package on multiple Python versions.
+# Add steps that analyze code, save the dist with the build record, publish to a PyPI-compatible index, and more:
+# https://docs.microsoft.com/azure/devops/pipelines/languages/python
+
+trigger:
+- master
+
+pool:
+  vmImage: ubuntu-latest
+strategy:
+  matrix:
+    Python27:
+      python.version: '2.7'
+    Python35:
+      python.version: '3.5'
+    Python36:
+      python.version: '3.6'
+    Python37:
+      python.version: '3.7'
+
+steps:
+- task: UsePythonVersion@0
+  inputs:
+    versionSpec: '$(python.version)'
+  displayName: 'Use Python $(python.version)'
+
+- script: |
+    python -m pip install --upgrade pip
+    pip install -r requirements.txt
+  displayName: 'Install dependencies'
+
+- script: |
+    pip install pytest pytest-azurepipelines
+    pytest
+  displayName: 'pytest'

requirements.txt

@@ -1,3 +1,5 @@
 altair
 pandas
 streamlit
+openpyxl
+matplotlib

streamlit_app.py

@@ -1,40 +1,92 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Function to load student data from Excel sheet
+def load_student_data():
+    # Read student data from Excel sheet
+    student_data = pd.read_excel("student_data.xlsx")
+    return student_data
+
+# Function to filter students by course eligibility
+def filter_students_by_course(course):
+    student_df = load_student_data()
+    if course == "Science":
+        eligible_students = student_df[student_df["Marks"] >= 80]
+    elif course == "Economics":
+        eligible_students = student_df[student_df["Marks"] >= 75]
+    elif course == "Humanities":
+        eligible_students = student_df[student_df["Marks"] >= 70]
+    else:
+        eligible_students = pd.DataFrame(columns=["Name", "Marks"])  # Return empty DataFrame if course is not recognized
+    return eligible_students
+
+# Streamlit UI
+st.title('Education System')
+
+# Sidebar navigation
+page = st.sidebar.radio("Navigation", ('Home', 'Add Student', 'Admission Checker', 'Course Eligibility', 'Data Visualization'))
+
+if page == 'Home':
+    st.subheader('Welcome to the Education System!')
+    st.write("This platform allows you to manage student data and check admission eligibility.")
+    st.write("To get started, you can:")
+    st.write("- Click on 'Manage Students' to view student data.")
+
+    if st.button("Manage Students"):
+        student_df = load_student_data()
+        st.write(student_df)  # Display student data
+
+def add_student(name, marks):
+    student_df = load_student_data()
+    if name in student_df["Name"].values:
+        st.warning("Student already exists in the database.")
+    elif marks >= 75:
+        # Create a new DataFrame with the new student data
+        new_student = pd.DataFrame({"Name": [name], "Marks": [marks]})
+        # Append the new student DataFrame to the existing student DataFrame
+        student_df = pd.concat([student_df, new_student], ignore_index=True)
+        # Write the updated DataFrame to the Excel sheet (if needed)
+        # student_df.to_excel("students.xlsx", index=False)
+        st.success(f"{name} added successfully!")
+    else:
+        st.warning("Student not added. Marks should be 75 or greater for admission.")
+    return student_df  # Return the updated DataFrame
+
+if page == 'Admission Checker':
+    st.subheader('Admission Checker')
+    name = st.text_input("Enter student name to check admission:")
+
+    if st.button('Check Admission'):  # Button click to check admission
+        student_df = load_student_data()
+        if name in student_df["Name"].values:
+            st.write("Student found.")
+        else:
+            st.write("Student not found.")
+
+elif page == 'Course Eligibility':
+    st.subheader('Course Eligibility')
+    course = st.selectbox("Select a course:", ["Science", "Economics", "Humanities"])
+
+    st.write(f"Students eligible for {course} course:")
+    eligible_students = filter_students_by_course(course)
+    st.write(eligible_students)
+
+elif page == 'Data Visualization':
+    st.subheader('Data Visualization')
+
+    # Load student data
+    student_df = load_student_data()
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    # Check if the "Marks" column exists
+    if "Marks" not in student_df.columns:
+        st.error("The 'Marks' column is missing in the student data.")
+    else:
+        # Histogram of student marks distribution
+        st.write("Histogram of Student Marks Distribution")
+        fig, ax = plt.subplots()
+        ax.hist(student_df["Marks"], bins=10, color='skyblue', edgecolor='black')
+        ax.set_xlabel('Marks')
+        ax.set_ylabel('Frequency')
+        ax.set_title('Distribution of Student Marks')
+        st.pyplot(fig)