Modified README file

README.md

@@ -80,13 +80,7 @@ Or using `requirements.txt`:
 
 ```pip install -r requirements.txt```
 
-Run ```pip install -e .``` to install the project in editable mode.
-
-Run Jupyter Notebook
-
-```jupyter notebook```
-
-Open `dry_bean.ipynb` and execute the cells to preprocess the data and train the model
+Run ```pip install -e .``` to install the required packages and project in editable mode.
 
 ## Components
 
@@ -108,7 +102,25 @@ To train the model, run the `model_trainer.py` script:
 
 ```python src/components/model_trainer.py```
 
+Or use the Makefile by typing ```make``` in your terminal
+
 This will train the model and save the results to the `artifacts` directory.
 
+If you are using windows you can install ```make``` by running this code in your terminal
+
+```Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.WebClient]::new().DownloadString('https://community.chocolatey.org/install.ps1') | Invoke-Expression```
+
+then install make using choco
+
+```choco install make```
+
+To run app.py to predict your bean, use this code command
+
+#### On windows
+``` waitress-serve --listen=0.0.0.0:9696 app:app ```
+
+#### On Linux
+```gunicorn --bind=0.0.0.0:9696 app:app```
+
 ## Contributing
 Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

notebook/dry_bean.ipynb

@@ -1,5 +1,46 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The raw dataset for this study was obtained from the UCI Machine Learning Repository via an API call using ```ucimlrepo``` (fetch_ucirepo). \n",
+    "\n",
+    "It included grains with 16 features in total, comprising 12 dimensions and 4 distinct shapes. The study employed three well-known \n",
+    "\n",
+    "classifiers: Logistic Regression (LR), Random Forest (RF), and \n",
+    "\n",
+    "Extreme Gradient Boosting (XGB), all with balanced classes.\n",
+    "\n",
+    "This dataset has a shape of 13,611 data entries and 17 columns\n",
+    "\n",
+    "1. **Area (A)**: The area of a bean zone and the number of pixels within its boundaries.\n",
+    "\n",
+    "2. **Perimeter (P)**: Bean circumference is defined as the length of its border.\n",
+    "3. **Major axis length (L)**: The distance between the ends of the longest line that can be drawn from a bean.\n",
+    "4. **Minor axis length (l)**: The longest line that can be drawn from the bean while standing perpendicular to the main axis.\n",
+    "5. **Aspect ratio (K)**: Defines the relationship between L and l.\n",
+    "6. **Eccentricity (Ec)**: Eccentricity of the ellipse having the same moments as the region.\n",
+    "7. **Convex area (C)**: Number of pixels in the smallest convex polygon that can contain the area of a bean seed.\n",
+    "8. **Equivalent diameter (Ed)**: The diameter of a circle having the same area as a bean seed area.\n",
+    "9. **Extent (Ex)**: The ratio of the pixels in the bounding box to the bean area.\n",
+    "10. **Solidity (S)**: Also known as convexity. The ratio of the pixels in the convex shell to those found in beans.\n",
+    "11. **Roundness (R)**: Calculated with the following formula: \\( \\frac{4\\pi A}{P^2} \\)\n",
+    "12. **Compactness (CO)**: Measures the roundness of an object: \\( \\frac{Ed}{L} \\)\n",
+    "13. **ShapeFactor1 (SF1)**\n",
+    "14. **ShapeFactor2 (SF2)**\n",
+    "15. **ShapeFactor3 (SF3)**\n",
+    "16. **ShapeFactor4 (SF4)**\n",
+    "17. **Class**: Seker, Barbunya, Bombay, Cali, Dermosan, Horoz, and Sira"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -97,7 +138,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_csv('data/dry_bean.csv')"
+    "# df = pd.read_csv('data/dry_bean.csv')"
    ]
   },
   {

static/css/home.css

@@ -14,11 +14,12 @@ body {
 }
 
 .login {
-    width: 400px;
+    width: 100%;
     padding: 20px;
     border: 1px solid #ccc;
     border-radius: 10px;
     box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+    max-width: 700px;
 }
 
 .heading {
@@ -88,3 +89,16 @@ body {
 .dropdown-item:hover {
     background-color: #f0f0f0;
 }
+
+.two-grid{
+    display: flex;
+    flex-direction: row;
+    justify-content: space-between;
+    gap: 10px;
+}
+.two-grid-child{
+    width: 48%;
+    display: flex;
+    flex-direction: column;
+    gap: 20px;
+}

static/js/index.js

@@ -31,25 +31,44 @@ const dropDownChild = document.createElement('div');
         console.log("input value",input.value)
         areaDropDown.style.display = 'none'; // Hide dropdown after selection
     });
-    
+
     dropDown.appendChild(dropDownChild);
 });}
 
-const areaValues = [1, 2, 3, 4, 5, 6];
+const areaValues = [71236, 39318, 34045];
 
 const areaInput = document.getElementById("area-input");
 const areaDropDown = document.getElementById("area-dropdown");
 
 populateDropdown(areaInput, areaDropDown, areaValues)
 
 
-const perimeterValues = [1, 2, 3, 4, 5, 6, 7];
+const perimeterValues = [1055.392, 746.921, 681.927];
 const perimeterInput = document.getElementById("perimeter-input");
 const perimeterDropDown = document.getElementById("perimeter-dropdown");
 
 populateDropdown(perimeterInput, perimeterDropDown, perimeterValues)
 
+const majoraxislengthValues = [345.968599, 279.815434, 252.167650];
+const majoraxislengthInput = document.getElementById("majoraxislength-input");
+const majoraxislengthDropDown = document.getElementById("majoraxislength-dropdown");
+
+populateDropdown(majoraxislengthInput, majoraxislengthDropDown, majoraxislengthValues)
+
+const minoraxislengthValues = [264.740746, 179.451019, 172.362100];
+const minoraxislengthInput = document.getElementById("minoraxislength-input");
+const minoraxislengthDropDown = document.getElementById("minoraxislength-dropdown");
+
+populateDropdown(minoraxislengthInput, minoraxislengthDropDown, minoraxislengthValues)
 
+const aspectratioValues = [1.487857, 2.092897, 1.606084];
+const aspectratioInput = document.getElementById("aspectratio-input");
+const aspectratioDropDown = document.getElementById("aspectratio-dropdown");
 
+populateDropdown(aspectratioInput, aspectratioDropDown, aspectratioValues)
 
+const eccentricityValues = [0.740453, 0.878465, 0.782514];
+const eccentricityInput = document.getElementById("eccentricity-input");
+const eccentricityDropDown = document.getElementById("eccentricity-dropdown");
 
+populateDropdown(eccentricityInput, eccentricityDropDown, eccentricityValues)