add app.py

app.py

@@ -1,7 +1,46 @@
 import gradio as gr
+import torch
+from model import *
 
-def greet(name):
-    return "Hello " + name + "!!"
+from PIL import Image
+import torchvision.transforms as transforms
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+
+title = "Fashion Image Recognition"
+inputs = gr.components.Image()
+outputs = gr.components.Label()
+examples = "examples"
+
+model = torch.load("model/fashion.mnist.base.pt", map_location=torch.device("cpu"))
+
+# Images need to be transformed to an approximated `fashion MNIST` dataset format
+# see https://arxiv.org/abs/1708.07747
+transform = transforms.Compose(
+    [
+        transforms.Resize((28, 28)),
+        transforms.Grayscale(),
+        transforms.ToTensor(),
+        transforms.Normalize((0.5,), (0.5,)),  # Normalization
+        transforms.Lambda(lambda x: 1.0 - x),  # Invert colors
+        transforms.Lambda(lambda x: x[0]),
+        transforms.Lambda(lambda x: x.unsqueeze(0)),
+    ]
+)
+
+
+def predict(img):
+    img = transform(Image.fromarray(img))
+    predictions = model.predictions(img)
+    return predictions
+
+
+with gr.Blocks() as demo:
+    with gr.Tab("Garment Prediction"):
+        gr.Interface(
+            fn=predict,
+            inputs=inputs,
+            outputs=outputs,
+            examples=examples,
+        ).queue(default_concurrency_limit=5)
+
+demo.launch()