feat: closes #22

logic/models/run.go

@@ -7,15 +7,27 @@ import (
 	"net/http"
 	"os"
 	"path"
+	"strconv"
 
 	. "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/utils"
 	. "git.andr3h3nriqu3s.com/andr3/fyp/logic/utils"
 
 	tf "github.com/galeone/tensorflow/tensorflow/go"
+	"github.com/galeone/tensorflow/tensorflow/go/op"
 	tg "github.com/galeone/tfgo"
 	"github.com/galeone/tfgo/image"
 )
 
+func ReadPNG(scope *op.Scope, imagePath string, channels int64) *image.Image {
+	scope = tg.NewScope(scope)
+	contents := op.ReadFile(scope.SubScope("ReadFile"), op.Const(scope.SubScope("filename"), imagePath))
+	output := op.DecodePng(scope.SubScope("DecodePng"), contents, op.DecodePngChannels(channels))
+	output = op.ExpandDims(scope.SubScope("ExpandDims"), output, op.Const(scope.SubScope("axis"), []int32{0}))
+	image := &image.Image{
+		Tensor: tg.NewTensor(scope, output)}
+	return image.Scale(0, 255)
+}
+
 func handleRun(handle *Handle) {
 	handle.Post("/models/run", func(w http.ResponseWriter, r *http.Request, c *Context) *Error {
 		if !CheckAuthLevel(1, w, r, c) {
@@ -98,10 +110,10 @@ func handleRun(handle *Handle) {
 		img_file.Write(file)
 
 		root := tg.NewRoot()
-		tf_img := image.Read(root, path.Join(run_path, "img.png"), 3)
-        
-        batch := tg.Batchify(root, []tf.Output{tf_img.Value()})
-        exec_results := tg.Exec(root, []tf.Output{batch}, nil, &tf.SessionOptions{})
+
+		tf_img := ReadPNG(root, path.Join(run_path, "img.png"), 3)
+
+        exec_results := tg.Exec(root, []tf.Output{tf_img.Value()}, nil, &tf.SessionOptions{})
         inputImage, err:= tf.NewTensor(exec_results[0].Value())
         if err != nil {
             return Error500(err)
@@ -115,8 +127,23 @@ func handleRun(handle *Handle) {
                 tf_model.Op("serving_default_rescaling_input", 0): inputImage,
         })
 
-        predictions := results[0]
-        fmt.Println(predictions.Value())
+        var vmax float32 = 0.0
+        vi := 0
+        var predictions = results[0].Value().([][]float32)[0]
+
+        for i, v := range predictions {
+            if v > vmax {
+                vi = i
+                vmax = v
+            }
+        }
+
+        os.RemoveAll(run_path)
+
+        LoadDefineTemplate(w, "/models/edit.html", "run-model-card", c.AddMap(AnyMap{
+            "Model": model,
+            "Result": strconv.Itoa(vi),
+        }))
 		return nil
 	})
 }

logic/models/train/train.go

@@ -156,9 +156,8 @@ func trainDefinition(handle *Handle, model_id string, definition_id string) (acc
 	if err != nil {
 		return
 	}
-
+    
     os.RemoveAll(run_path)
-
 	return
 }
 

views/models/edit.html

@@ -289,7 +289,12 @@
 {{ end }}
 
 {{ define "run-model-card" }}
-    <form hx-headers='{"REQUEST-TYPE": "html"}' enctype="multipart/form-data" hx-post="/models/run">
+    <form hx-headers='{"REQUEST-TYPE": "html"}' enctype="multipart/form-data" hx-post="/models/run" hx-swap="outerHTML">
+        {{ if .Result }}
+            <div>
+                Img Class: {{.Result}}
+            </div>
+        {{ end }}
         <input type="hidden" name="id" value={{.Model.Id}} />
         <fieldset class="file-upload" >
             <label for="file">Image</label>

views/py/python_model_template.py

@@ -11,8 +11,9 @@ dataset = keras.utils.image_dataset_from_directory(
   "{{ .DataDir }}",
   color_mode="rgb",
   validation_split=0.2,
-  label_mode='int',
+  label_mode='categorical',
   seed=seed,
+  shuffle=True,
   subset="training",
   image_size=({{ .Size }}),
   batch_size=batch_size)
@@ -21,8 +22,9 @@ dataset_validation = keras.utils.image_dataset_from_directory(
   "{{ .DataDir }}",
   color_mode="rgb",
   validation_split=0.2,
-  label_mode='int',
+  label_mode='categorical',
   seed=seed,
+  shuffle=True,
   subset="validation",
   image_size=({{ .Size }}),
   batch_size=batch_size)
@@ -42,11 +44,11 @@ model = keras.Sequential([
 ])
 
 model.compile(
-    loss=losses.SparseCategoricalCrossentropy(),
+    loss=losses.CategoricalCrossentropy(),
     optimizer=tf.keras.optimizers.Adam(),
     metrics=['accuracy'])
 
-his = model.fit(dataset, validation_data= dataset_validation, epochs=70)
+his = model.fit(dataset, validation_data= dataset_validation, epochs=50)
 
 acc = his.history["accuracy"]