Added sketch_recognition

app.py

@@ -1,7 +1,28 @@
 import gradio as gr
+import tensorflow as tf
+import urllib.request
 
-def greet(name):
-    return "Hello " + name + "!!"
+# mlp_model = tf.keras.models.load_model(
+#   "models/sketch_recognition/mlp.h5")
+cnn_model = tf.keras.models.load_model(
+  "models/sketch_recognition/cnn.h5")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+labels = urllib.request.urlopen("https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/categories.txt")
+labels = labels.read()
+labels = labels.decode('utf-8').split("\n")[:-1]
+
+
+def predict(img):
+  img = tf.math.divide(img, 255)
+  preds = cnn_model.predict(img.reshape(-1, 28, 28, 1))[0]
+  return {label: float(pred) for label, pred in zip(labels, preds)}
+
+output = gr.outputs.Label(num_top_classes=3)
+
+title="Sketch Recognition"
+description="This Convolution Neural Network was trained on Google's " \
+            "QuickDraw dataset with 345 classes. Try it by drawing a " \
+            "lightbulb, radio, or anything you can think of!"
+thumbnail="https://github.com/gradio-app/machine-learning-experiments/raw/master/lightbulb.png?raw=true"
+gr.Interface(predict, "sketchpad", output, live=True, title=title, analytics_enabled=False,
+             description=description, thumbnail=thumbnail, capture_session=True).launch()

models/sketch_recognition/cnn.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25b551a31a9ca980637231fef74428a4f7b2dcea199cbe3226fed89629741075
+size 2029392