Added the ML Model with traning datasets

Machine_Learning/Courier Project.postman_collection.json

+{
+	"info": {
+		"_postman_id": "9edb0908-3d79-4e8e-adc0-09f22cb25cef",
+		"name": "Courier Project",
+		"schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json",
+		"_exporter_id": "11353339",
+		"_collection_link": "https://solar-shadow-692034.postman.co/workspace/New-Team-Workspace~652dfa11-221c-40ef-91b1-49d23f421655/collection/11353339-9edb0908-3d79-4e8e-adc0-09f22cb25cef?action=share&source=collection_link&creator=11353339"
+	},
+	"item": [
+		{
+			"name": "defects",
+			"request": {
+				"method": "POST",
+				"header": [],
+				"body": {
+					"mode": "formdata",
+					"formdata": [
+						{
+							"key": "image",
+							"type": "file",
+							"src": "/C:/Projects/private-projects/SLIIT/Group/PP2 Progressing/courier service defect analysis/data/train/qualified/20230625_185800.jpg"
+						}
+					]
+				},
+				"url": {
+					"raw": "http://127.0.0.1:5000/defects",
+					"protocol": "http",
+					"host": [
+						"127",
+						"0",
+						"0",
+						"1"
+					],
+					"port": "5000",
+					"path": [
+						"defects"
+					]
+				}
+			},
+			"response": []
+		},
+		{
+			"name": "maps",
+			"request": {
+				"method": "POST",
+				"header": [],
+				"body": {
+					"mode": "formdata",
+					"formdata": [
+						{
+							"key": "pickup",
+							"value": "(6.9271, 79.8612)",
+							"type": "text"
+						},
+						{
+							"key": "delivery",
+							"value": "(7.0840, 80.0098)",
+							"type": "text"
+						}
+					]
+				},
+				"url": {
+					"raw": "http://127.0.0.1:5000/maps",
+					"protocol": "http",
+					"host": [
+						"127",
+						"0",
+						"0",
+						"1"
+					],
+					"port": "5000",
+					"path": [
+						"maps"
+					]
+				}
+			},
+			"response": []
+		}
+	]
+}
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Machine_Learning/app.py

+import os
+import folium
+from folium.plugins import MarkerCluster
+import requests
+import numpy as np
+import pandas as pd
+from flask import Flask, render_template, request
+from models.route_optimization import load_model, visualize_route
+
+app = Flask(__name__)
+model = load_model('models/route-optimization.h5')
+
+@app.route("/maps/map-inference.html", methods=["GET"])
+def file():
+    return render_template('maps', 'map-inference.html')
+
+@app.route('/visualize', methods=['POST'])
+def visualize():
+    pickup = request.form['pickup']
+    delivery = request.form['delivery']
+
+    X = pd.DataFrame([pickup, delivery]).values.reshape(1, -1)
+    Y = model.predict(X)
+
+    if np.argmax(Y[0]) == 0:
+        route_result = 'Route Optimized'
+    else:
+        route_result = 'Straight Route'
+
+    visualize_route(pickup, delivery)
+
+    return render_template('/maps/map-inference.html', pickup=pickup, delivery=delivery, route_result=route_result)
+
+if __name__ == '__main__':
+    app.run(debug=True)
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