Create app.py

app.py

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+import torch
+from torchvision import models, transforms
+from PIL import Image
+
+
+labels = {
+  0: "bluebell",
+  1: "buttercup",
+  2: "colts_foot",
+  3: "corn_poppy",
+  4: "cowslip",
+  5: "crocus",
+  6: "daffodil",
+  7: "daisy",
+  8: "dandelion",
+  9: "foxglove",
+  10: "fritillary",
+  11: "geranium",
+  12: "hibiscus",
+  13: "iris",
+  14: "lily_valley",
+  15: "pansy",
+  16: "petunia",
+  17: "rose",
+  18: "snowdrop",
+  19: "sunflower",
+  20: "tigerlily",
+  21: "tulip",
+  22: "wallflower",
+  23: "water_lily",
+  24: "wild_tulip",
+  25: "windflower"
+}
+
+# Load the trained ResNet-152 model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model structure
+model = models.resnet152()
+num_classes = 26  # Update with your dataset's number of classes
+model.fc = torch.nn.Linear(model.fc.in_features, num_classes)
+
+# Load trained weights
+model.load_state_dict(torch.load('trained_model.pth', map_location=device))
+model = model.to(device)
+model.eval()  # Set to evaluation mode
+
+# Preprocessing pipeline for incoming images
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),  # ResNet default input size
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+def predict_image(image_path):
+    # Load and preprocess the image
+    image = Image.open(image_path).convert("RGB")
+    input_tensor = preprocess(image).unsqueeze(0).to(device)
+
+    # Predict
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        _, predicted_class = torch.max(outputs, 1)
+
+    return predicted_class.item()  # Return class index
+
+import gradio as gr
+
+def get_class_name(class_index):
+    return labels[class_index]
+# Function to predict from an uploaded image
+def classify_image(image):
+    predicted_class = predict_image(image)  # Use the function from above
+    return f"Predicted Class: {predicted_class} : {get_class_name(predicted_class)}"
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="filepath"),  # Accept image uploads
+    outputs="text",
+    title="Image Classification with ResNet-152",
+    description="Upload an image to classify it into one of 26 classes."
+)
+
+# Launch the app
+interface.launch()