Merge branch 'iris-clasification' of s434734/AiForklift into master

magazine/static/magazine/forklift.js

@@ -39,7 +39,6 @@ class Forklift {
   }
 
   setVelocity() {
-        debugger;
     this.direction = this.sub(sections[this.currentTarget], this.positoin);
     this.velocity = this.direction.setMag(this.speed);
   }
@@ -49,8 +48,7 @@ class Forklift {
     if (
       Math.abs(this.positoin.x - sections[this.currentTarget].x) <=
         this.speed &&
-            Math.abs(this.positoin.y - sections[this.currentTarget].y) <=
+      Math.abs(this.positoin.y - sections[this.currentTarget].y) <= this.speed
-                this.speed
     ) {
       this.positoin = sections[this.currentTarget];
       this.nextTarget();

magazine/static/magazine/sketch.js

@@ -1,11 +1,10 @@
+const serverUrl = 'http://localhost:8000';
 let sections;
 let roads;
 let packageClaim;
 let going = false;
 let forklift;
 
-let target;
-
 // This runs once at start
 function setup() {
   createCanvas(600, 600).parent('canvas');
@@ -14,8 +13,11 @@ function setup() {
 
   createMagazineLayout();
 
-    select('#button').mousePressed(deliver);
+  select('#button').mousePressed(getIrisType);
-    target = select('#target');
+  sepalWidth = select('#sepalWidth');
+  sepalLength = select('#sepalLength');
+  petalWidth = select('#petalWidth');
+  petalLength = select('#petalLength');
   // Create a forklift instance
   forklift = new Forklift(sections[0].x, sections[0].y);
 }
@@ -63,14 +65,31 @@ function drawMagazine() {
   }
 }
 
-function deliver() {
+function getIrisType() {
+  let sw = select('#sepalWidth').value();
+  let sl = select('#sepalLength').value();
+  let pw = select('#petalWidth').value();
+  let pl = select('#petalLength').value();
+  let data = {
+    sepalWidth: sw,
+    sepalLength: sl,
+    petalWidth: pw,
+    petalLength: pl,
+  };
+  httpPost(serverUrl + '/classify', data, response => {
+    deliver(response);
+  });
+}
+
+function deliver(targetSection) {
   let data = {
     graph: magazineToGraph(),
     start_node: forklift.currentSection,
-        dest_node: int(target.value()),
+    dest_node: int(targetSection),
   };
+  console.log(data);
   httpPost(
-        'http://localhost:8000/shortestPath',
+    serverUrl + '/shortestPath',
     data,
     response => {
       path = response.split('').map(Number);

magazine/templates/magazine/index.html

@@ -42,27 +42,21 @@
         <div class="package">
             <h3 style="margin-top: 0;">Package description</h1>
             <label for="width">Sepal Width</label>
-            <input type="number" name="width">
+            <input type="number" id="sepalWidth">
             <label for="topWidth">Sepal Length</label>
-            <input type="number" name="topWidth">
+            <input type="number" id="sepalLength">
             <label for="botWidth">Petal Width</label>
-            <input type="number" name="botWidth">
+            <input type="number" id="petalWidth">
             <label for="height">Petal Length</label>
-            <input type="number" name="height">
+            <input type="number" id="petalLength">
-            <label for="target">Target</label>
-            <input type="number" id="target" name="target">
             <button id="button">Send Package</button>
         </div>
         <div id="canvas" style="margin: 10px;"></div>
         <div class="legend">
             <h3 style="margin-top: 0">Sections</h3>
-            <p>A - Cartons</p>
+            <p>1 - Setosa</p>
-            <p>B - Barrels</p>
+            <p>2 - Versicolor</p>
-            <p>C - Plastic boxes</p>
+            <p>3 - Viginica</p>
-            <p>D - ______</p>
-            <p>E - ______</p>
-            <p>F - ______</p>
-            <p>G - ______</p>
         </div>
     </div>
 </body>

magazine/views.py

@@ -1,10 +1,11 @@
+import math
+import json
 from django.shortcuts import render
 from django.http import HttpResponse
 from django.views.decorators.csrf import csrf_exempt
 
-import json
+import tensorflow as tf
-import math
+import numpy as np
-
 # Create your views here.
 
 
@@ -14,7 +15,21 @@ def index(request):
 
 @csrf_exempt
 def classify(request):
-    return HttpResponse(json.load(request))
+    loaded_request = json.load(request)
+    sw = loaded_request['sepalWidth']
+    sl = loaded_request['sepalLength']
+    pw = loaded_request['petalWidth']
+    pl = loaded_request['petalLength']
+
+    model = tf.keras.models.load_model('iris_model.h5')
+    output = model.predict(np.array([[sw, sl, pw, pl]]))
+    if output[0][0] > output[0][1] and output[0][0] > output[0][1]:
+        guess = 1
+    elif output[0][1] > output[0][0] and output[0][1] > output[0][2]:
+        guess = 2
+    else:
+        guess = 3
+    return HttpResponse(guess)
 
 
 @csrf_exempt
@@ -61,4 +76,6 @@ def shortestPath(request):
             current = predecessor[current]
         path[node] = p[::-1]
 
+    print(path)
+
     return HttpResponse(path[dest_node][1:])

train_model.py

@@ -0,0 +1,28 @@
+import numpy as np
+import tensorflow as tf
+
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+
+from tensorflow.keras import layers
+from tensorflow.keras.utils import to_categorical
+
+# Getting data
+data_set = load_iris()
+x = data_set['data']
+y = to_categorical(data_set['target'])
+
+train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2)
+
+# Building the model
+model = tf.keras.Sequential()
+
+model.add(layers.Dense(8, activation='relu', input_dim=4))
+model.add(layers.Dense(3, activation='sigmoid'))
+
+model.compile(optimizer='adam', loss='categorical_crossentropy',
+              metrics=['accuracy'])
+# Training the model
+model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=2000)
+
+model.save('iris_model.h5')