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app.py

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+import torch
+import torch.nn as nn
+import torchvision
+import gradio as gr
+from PIL import Image
+from torchvision import transforms
+
+agirliklar=torchvision.models.EfficientNet_B2_Weights.DEFAULT
+eff_don=agirliklar.transforms()
+
+model=torchvision.models.efficientnet_b2(weights=agirliklar)
+model.classifier=nn.Sequential(nn.Linear(1408,100),nn.ReLU(),nn.Linear(100,5))
+model.load_state_dict(torch.load('model.pth'))
+
+class_names=['beş', 'bir', 'dört', 'iki', 'üç']
+
+def predict(img):
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+   # img=Image.open(img)
+    # Transform the target image and add a batch dimension
+    img = eff_don(img).unsqueeze(0)
+
+    # Put model into evaluation mode and turn on inference mode
+    model.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(model(img), dim=1)
+
+    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+
+    # Return the prediction dictionary and prediction time
+    return pred_labels_and_probs
+
+
+# Create title, description and article strings
+title = "El işaretleri"
+description = "Birden beşe kadar olan sayilarin el işaretlerini anlar"
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type="pil"), # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=5, label="Predictions")], # what are the outputs?
+                            # gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                   # examples=example_list,
+                    title=title,
+                    description=description)
+
+# Launch the demo!
+demo.launch(debug=False, # print errors locally?
+            share=True) # generate a publically shareable URL?

model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f210cb7da722afb244a7de3e2c7f65b86af5db517155b456b6e3fc0a577ea40
+size 31791154

requirements.txt

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+torch
+gradio
+torchvision
+pillow