Upload 6 files

app.py

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+import gradio as gr
+import torch
+import torchvision
+from model import create_model
+from timeit import default_timer as timer
+
+model,transform=create_model()
+model.load_state_dict(torch.load("deneme_modeli.pth"))
+class_names = ["pizza","steak","sushi"]
+def predict(model,image):
+    start=timer()
+    image=transform(image.to("cpu")).unsqueeze(0)
+    model=model.to("cpu")
+    pred=model(image)
+    pred = {class_names[i]:torch.softmax(pred)[0][i] for i in range(3)}
+    td=timer()-start
+    return pred,td
+
+inputs = gr.Image(type="pil", label = "Resim")
+outputs = [gr.Label(num_top_classes=3,label="Tahminler"),gr.Number(label="Süre")]
+demo=gr.Interface(fn = predict,
+                 inputs=inputs,
+                 outputs=outputs,
+                 examples=["examples/673127.jpg","examples/690177.jpg"],
+                 title="Yeni Model")
+
+demo.launch()

deneme_modeli.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73d59290c2a2e612d28a90fdd5eb7a72a7d44d8582efb5b018a9cd7af7311915
+size 31276858

model.py

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+from torch import nn
+import torchvision
+import torch
+def create_model(num_classes:int=3)
+    weights = torchvision.models.EfficientNet_B2_Weights.IMAGENET1K_V1
+    model=torchvision.models.efficientnet_b2(weights=weights)
+    transform=weights.transforms()
+    
+    for param in model.parameters():
+        param.requires_grad=False
+    
+    model.classifier=nn.Sequential(nn.Dropout(p=0.3, inplace=True),
+                                         nn.Linear(in_features=1408, out_features=num_classes, bias=True))
+    return model,transform

requirements.txt

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+torch==2.0.1
+torchvision==0.15.2
+gradio==3.35.2