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import gradio as gr
from utils import load_specific_model, inference
import markdown
current_model = None # Initialize the current model as None
# Define a set of example images
example_images = [
("Beispielbild Glas", "src/examples/Glas.jpg"),
("Beispielbild Organic", "src/examples/Organic.jpg"),
("Beispielbild Papier", "src/examples/Papier.jpg"),
("Beispielbild Restmüll", "src/examples/Restmuell.jpg"),
("Beispielbild Wertstoff", "src/examples/Wertstoff.jpg")
]
def load_model(model_name):
global current_model
if model_name is None:
raise gr.Error("No model selected!")
if current_model is not None:
current_model = None
current_model = load_specific_model(model_name)
current_model.eval()
def predict(inp):
global current_model
if current_model is None:
raise gr.Error("No model loaded!")
confidences = inference(current_model, inp)
return confidences
with gr.Blocks() as demo:
with open('src/app_template.md', 'r') as f:
markdown_string = f.read()
header = gr.Markdown(markdown_string)
with gr.Row(variant="panel", equal_height=True):
user_image = gr.Image(
type="pil",
label="Upload Your Own Image",
info="You can also upload your own image for prediction.",
scale=2,
height=350,
)
with gr.Column():
output = gr.Label(
num_top_classes=3,
label="Output",
info="Top three predicted classes and their confidences.",
scale=2,
)
model_dropdown = gr.Dropdown(
["EfficientNet-B3", "EfficientNet-B4", "vgg19", "resnet50", "dinov2_vits14"],
label="Model",
info="Select a model to use.",
scale=1,
)
model_dropdown.change(load_model, model_dropdown, show_progress=True, queue=True)
predict_button = gr.Button(label="Predict", info="Click to make a prediction.", scale=1)
predict_button.click(fn=predict, inputs=user_image, outputs=output, queue=True)
gr.Markdown("## Example Images")
gr.Markdown("You can just drag and drop these images into the image uploader above!")
with gr.Row():
for name, image_path in example_images:
example_image = gr.Image(
value=image_path,
label=name,
type="pil",
height=220,
interactive=False,
)
if __name__ == "__main__":
demo.queue()
demo.launch()
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