Upload 3 files

app.py

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+import torch
+import torch.nn as nn
+from PIL import Image
+from torchvision import transforms
+import torchvision
+import gradio as gr
+
+weights = torchvision.models.GoogLeNet_Weights.DEFAULT
+transfer_model_transformer = weights.transforms()
+
+transfer_model = torchvision.models.googlenet(weights=weights)
+transfer_model.classifier = nn.Sequential(nn.Dropout(p=0.2), nn.Linear(in_features=1024, out_features=512),nn.ReLU(),
+                                            nn.Linear(in_features=512, out_features=256),nn.ReLU(),
+                                            nn.Linear(in_features=256, out_features=128),nn.ReLU(),
+                                            nn.Linear(in_features=128, out_features=64),nn.ReLU(),
+                                            nn.Linear(in_features=64, out_features=32),nn.ReLU(),
+                                            nn.Linear(in_features=32, out_features=16),nn.ReLU(),
+                                            nn.Linear(in_features=16, out_features=8),nn.ReLU(),
+                                            nn.Linear(in_features=8, out_features=4),nn.ReLU(),
+                                            nn.Linear(in_features=4, out_features=2))
+transfer_model.load_state_dict(torch.load("best_model_transfer.pth"))
+
+class_names=['Tere','Roka']
+
+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 = transfer_model_transformer(img).unsqueeze(0)
+
+    # Put model into evaluation mode and turn on inference mode
+    transfer_model.eval()
+    transfer_model.to("cpu")
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(transfer_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 = "CRESS ARUGULA DISTINCTIVE"
+description = "An artificial intelligence application that recognizes whether the photo uploaded to the system is cress or arugula."
+
+# 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=len(class_names), 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?

best_model_transfer.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0550657fe32036d8fd70443167cdd954bc04231b15aa1ed9e6e8df0de3a2410f
+size 29488774

requirements.txt

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+torch
+gradio
+torchvision
+pillow