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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,33 +1,37 @@
 import gradio as gr
-import torch
+# import torch
+import tensorflow as tf
 import numpy as np
 from PIL import Image
-from torchvision.transforms import Compose, Resize, ToTensor, Normalize
+# from torchvision.transforms import Compose, Resize, ToTensor, Normalize
 
 
 
 print('loading model..')
-model = torch.load('model.pth')
-model.eval()
+model = tf.keras.models.load_model('model.keras')
 print('loaded.')
 
 
-transform = Compose([
-    Resize((300,300)),
-    ToTensor(),
-    Normalize(mean=[0.485, 0.456, 0.406],
-                        std=[0.229, 0.224, 0.225]),
-])
+# transform = Compose([
+#     Resize((300,300)),
+#     ToTensor(),
+#     Normalize(mean=[0.485, 0.456, 0.406],
+#                         std=[0.229, 0.224, 0.225]),
+# ])
 
 def predict(img):
-    labels = list(range(10))
     if isinstance(img, np.ndarray):
         img = Image.fromarray(img.astype('uint8'), 'RGB')
-    img = transform(img)
-    img = img.unsqueeze(0)
+    img = img.resize((300,300))
+    img = np.array(img)
+    img = np.expand_dims(img,axis=0)
+    labels = list(range(10))
+    # img = transform(img)
+    # img = img.unsqueeze(0)
 
-    with torch.inference_mode():
-        prediction = torch.softmax(model(img),dim=1)[0]
+    # with torch.inference_mode():
+    #     prediction = torch.softmax(model(img),dim=1)[0]
+    prediction = model(img)[0]
 
 
     result = { num:float(prob.numpy()) for num, prob in enumerate(prediction)}

model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0aed96125de150e9b07b705f2879cb465516acb8532bb8a57b85569b4210d7a
+size 20058690