Update app.py

app.py

@@ -1,59 +1,28 @@
-import torch
-import torchvision
-import torch.nn as nn
-import torchvision.transforms as transforms
-
-model = torchvision.models.resnet50(pretrained=True)
-model.fc = nn.Linear(model.fc.in_features, 2)
-model.load_state_dict(torch.load("model.pth"))
-input_batch = input_batch.to('cpu')
-model.to('cpu')
-model.eval()
-
-transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
-
-categories = ['Fruta própria para o consumo', 'Fruta imprópria para o consumo']
-
 import gradio as gr
+import torch
+from torchvision.transforms import ToTensor
 from PIL import Image
 
+# Load your PyTorch model
+model = torch.load("model.pth")
 
-def inference(input_image):
-    preprocess = transforms.Compose([
-        transforms.Resize(256),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-    ])
-    input_tensor = preprocess(input_image)
-    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
-
-        
+# Define the function for image classification
+def classify_image(image):
+    image_tensor = ToTensor()(image).unsqueeze(0)
 
+    # Perform inference using your PyTorch model
     with torch.no_grad():
-        output = model(input_batch)
-    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
-    probabilities = torch.nn.functional.softmax(output[0], dim=0)
-
-
-    # Show top categories per image
-    top5_prob, top5_catid = torch.topk(probabilities, 2)
-    result = {}
-    for i in range(top5_prob.size(0)):
-        result[categories[top5_catid[i]]] = top5_prob[i].item()
-    return result
-
-inputs = gr.inputs.Image(type='pil')
-outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
+        model.eval()
+        outputs = model(image_tensor)
 
-title = "ResNet"
-description = "Gradio demo for ResNet, Deep residual networks pre-trained on ImageNet. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+    predicted_labels = outputs.argmax(dim=1).tolist()
+    return predicted_labels
 
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1512.03385' target='_blank'>Deep Residual Learning for Image Recognition</a> | <a href='https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py' target='_blank'>Github Repo</a></p>"
+# Define the Gradio interface
+inputs = gr.Image()
+outputs = gr.Label(num_top_classes=1)
 
+interface = gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs)
 
-gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False).launch()
+# Launch the interface
+interface.launch()