FarmPure for the Formers

FarmPure is a web application designed to connect local farmers with consumers, providing a platform for selling and purchasing fresh, natural produce directly from the farm. The project includes a multi-page website for user and farmer interactions, a database schema, and a machine learning model for predicting optimal product prices.

Project Structure

The project is organized into the following key components:

• HTML Files: The front-end of the application, consisting of multiple pages for user and farmer portals.
• CSS & JavaScript: A stylesheet for consistent design and a script for specific functionalities like geolocation.
• Backend & Database: A Flask-based Python server for routing and a MySQL database schema (farmpure.sql) for managing user, farmer, and product data.
• Pricing Model: Python scripts for a machine learning model that analyzes market data to suggest optimal product prices for farmers.

Getting Started

To run this project locally, you need to set up a few things:

1. File Organization: Ensure your files are arranged in the correct folder structure:

   FarmPure/
   ├── app.py
   ├── index.html
   ├── login_signup.html
   ├── user_login.html
   ├── user_signup.html
   ├── farmer_login.html
   ├── farmer_signup.html
   ├── u_homepage.html
   ├── f_homepage.html
   ├── home.html
   ├── gps.html
   ├── style.css
   ├── your_script.js
   ├── farmpure.sql
   ├── image/
   │   ├── background1.jpg
   │   ├── logo.png
   │   └── ... (other images)
   └── graph/
       ├── dataset.py
       ├── dummy_data_with_predicted_prices.csv
       ├── evaluate_model.py
       └── plot_graph.py
   

2. Run the Backend Server: The provided app.py file uses the Flask framework to handle routing. To start the server, navigate to the FarmPure directory in your terminal and run:

   pip install Flask
   python app.py

   Once the server is running, you can access the website by opening your web browser and navigating to http://127.0.0.1:5000.

3. Database Setup (Optional): If you want to set up the database, you can use the farmpure.sql file. You will need a MySQL server installed and configured.

4. Pricing Model (Optional): The Python scripts in the graph folder are for data analysis. You can run them independently to see the pricing model in action. You will need to install the required libraries:

   pip install pandas scikit-learn matplotlib

   Then, you can run each script to generate the data, plots, and evaluation metrics:

   python graph/dataset.py
   python graph/evaluate_model.py
   python graph/plot_graph.py

Key Features

• Role-Based Access: Separate login and signup flows for Farmers and Users.
• Intuitive Navigation: Easy-to-use navigation bars on the user and farmer homepages.
• Geolocation: The gps.html page demonstrates how to get the user's current location using a JavaScript API.
• Data-Driven Insights: The pricing model scripts show how the platform could provide valuable data to farmers to help them set optimal prices.