Update app.py

app.py

@@ -1,53 +1,152 @@
-import torch
-from torchvision import transforms
-from PIL import Image
-from torchvision import models
-import gradio.inputs as gi
-import gradio.outputs as go
+
+
+
 import gradio as gr
+from transformers import pipeline
+
+# Load the model pipeline
+pipe = pipeline("image-classification", "dima806/medicinal_plants_image_detection")
+
+# Define the image classification function
+def image_classifier(image):
+    # Perform image classification
+    outputs = pipe(image)
+    results = {}
+    for result in outputs:
+        results[result['label']] = result['score']
+    return results
+
+# Define app title and description with HTML formatting
+title = "<h1 style='text-align: center; color: #4CAF50;'>Image Classification</h1>"
+description = "<p style='text-align: center; font-size: 18px;'>This application serves to classify skin lesion images based on their skin cancer type. Trained using Vision Transformer (ViT), it has achieved a validation accuracy of 86%.</p>"
+
+# Define custom CSS styles for the Gradio app
+custom_css = """
+            .gradio-interface {
+                max-width: 600px;
+                margin: auto;
+                border-radius: 10px;
+                box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1);
+            }
+            .title-container {
+                padding: 20px;
+                background-color: #f0f0f0;
+                border-top-left-radius: 10px;
+                border-top-right-radius: 10px;
+            }
+            .description-container {
+                padding: 20px;
+            }
+            """
+
+# Launch the Gradio interface with custom HTML and CSS
+demo = gr.Interface(fn=image_classifier, inputs=gr.Image(type="pil"), outputs="label", title=title, description=description, 
+                     theme="gstaff/sketch", css=custom_css,
+                   )
+demo.launch()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# import torch
+# from torchvision import transforms
+# from PIL import Image
+# from torchvision import models
+# import gradio.inputs as gi
+# import gradio.outputs as go
+# import gradio as gr
+
+# # Define the ResNet50 model
+# class ResNet50(torch.nn.Module):
+#     def __init__(self):
+#         super(ResNet50, self).__init__()
+#         self.resnet = models.resnet50(pretrained=True)
+#         for param in self.resnet.parameters():
+#             param.requires_grad = False
+#         self.resnet.fc = torch.nn.Sequential(
+#             torch.nn.Linear(2048, 2)
+#         )
+
+#     def forward(self, x):
+#         x = self.resnet(x)
+#         return x
+
+# # Load the pre-trained model
+# model = ResNet50()
+# model.load_state_dict(torch.load('best_modelv2.pth', map_location=torch.device('cpu')))
+# model.eval()
+
+# # Define transform for input images
+# data_transforms = transforms.Compose([
+#     transforms.Resize((224, 224)),
+#     transforms.ToTensor(),
+#     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+# ])
+
+# # Function to predict image label
+# def predict_image_label(image):
+#     # Preprocess the image
+#     image = data_transforms(image).unsqueeze(0)
+
+#     # Make prediction
+#     with torch.no_grad():
+#         output = model(image)
+#         _, predicted = torch.max(output, 1)
+
+#     label = 'Leaf' if predicted.item() == 0 else 'Plant'
+#     return label
+
+# # Create Gradio interface
+# # image = gi.Image(shape=(224, 224))
+# label = go.Label(num_top_classes=2)
 
-# Define the ResNet50 model
-class ResNet50(torch.nn.Module):
-    def __init__(self):
-        super(ResNet50, self).__init__()
-        self.resnet = models.resnet50(pretrained=True)
-        for param in self.resnet.parameters():
-            param.requires_grad = False
-        self.resnet.fc = torch.nn.Sequential(
-            torch.nn.Linear(2048, 2)
-        )
-
-    def forward(self, x):
-        x = self.resnet(x)
-        return x
-
-# Load the pre-trained model
-model = ResNet50()
-model.load_state_dict(torch.load('best_modelv2.pth', map_location=torch.device('cpu')))
-model.eval()
-
-# Define transform for input images
-data_transforms = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
-
-# Function to predict image label
-def predict_image_label(image):
-    # Preprocess the image
-    image = data_transforms(image).unsqueeze(0)
-
-    # Make prediction
-    with torch.no_grad():
-        output = model(image)
-        _, predicted = torch.max(output, 1)
-
-    label = 'Leaf' if predicted.item() == 0 else 'Plant'
-    return label
-
-# Create Gradio interface
-# image = gi.Image(shape=(224, 224))
-label = go.Label(num_top_classes=2)
-
-gr.Interface(fn=predict_image_label,inputs="image", outputs=label, title="Leaf or Plant Classifier").launch()
+# gr.Interface(fn=predict_image_label,inputs="image", outputs=label, title="Leaf or Plant Classifier").launch()