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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()
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