manish-jsx / flaskproject

Machine Learning Project Portfolio

Flask Data Visualization App

This is a simple Flask web application that reads data from a CSV file and displays it in a table format on the homepage. Additionally, it provides a bar graph visualization of the data on a separate page using Plotly.

Screenshots

Project Structure

myproject/
├── README.md
├── app/
│   ├── __init__.py
│   ├── main.py
│   ├── templates/
│   │   ├── base.html
│   │   ├── graph.html
│   │   └── index.html
│   └── wsgi.py
├── data.csv
├── graph.csv
├── requirements.txt
├── screenshots/
│   ├── data_graph.png
│   └── data_table.png
└── static/
    └── css/
        └── styles.css

Requirements

• Python 3.7+
• Flask
• pandas
• plotly

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/myproject.git
   cd myproject

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the dependencies:

   pip install -r requirements.txt

Usage

1. Run the Flask application:

   gunicorn app.wsgi:app

2. Open your web browser and navigate to:

   http://127.0.0.1:5000/
   

Application Features

Homepage

• Displays the CSV data in a table format.
• Provides a link to view the data visualization.

Graph Page

• Displays a bar graph visualization of the data using Plotly.

Data Files

The application reads data from data.csv and graph.csv. Make sure your CSV files are structured correctly. Here's an example structure for graph.csv:

Country,Score
USA,85
Canada,90
UK,78
Australia,88
India,92
Germany,75
Japan,80
China,95
France,82
Brazil,87

Templates

• base.html: The base template that includes the navbar, hero section, and footer.
• index.html: Inherits from base.html and displays the data table.
• graph.html: Inherits from base.html and displays the data graph.

Static Files

• styles.css: Custom styles for the application.

Deployment on Render

To deploy this application on Render, follow these steps:

1. Create a requirements.txt file:

   pip freeze > requirements.txt

2. Create a render.yaml file in the root of your project:

   services:
   - type: web
       name: my-flask-app
       env: python
       region: oregon
       plan: free
       buildCommand: pip install -r requirements.txt
       startCommand: gunicorn app.wsgi:app
   
   

3. Push your code to a GitHub repository.

4. Create a new Web Service on Render:

   • Go to Render.
   • Click on the "New" button and select "Blueprint".
   • Connect your GitHub repository.
   • Use the settings from your render.yaml file.

5. Deploy the service.

6. Access your deployed application.

Updated Code Files

app/main.py

from flask import Flask, render_template
import pandas as pd
import plotly.express as px
import os

app = Flask(__name__)

port = int(os.environ.get('PORT', 10000))  # Adjusted the default port

@app.route('/')
def index():
    # Load CSV data
    data = pd.read_csv('data.csv')
    # Convert to HTML table
    data_html = data.to_html(classes='table table-striped', index=False)
    return render_template('index.html', data=data_html)

@app.route('/graph')
def graph():
    # Load CSV data
    data = pd.read_csv('graph.csv')
    
    # Create a plotly figure
    fig = px.bar(data, x='Country', y='Score', title='Sample Data Visualization')
    graph_html = fig.to_html(full_html=False)
    
    return render_template('graph.html', graph=graph_html)

if __name__ == '__main__':
    app.run()

app/wsgi.py

from app.main import app

if __name__ == "__main__":
    app.run()

app/__init__.py

# This file is intentionally left blank.