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import gradio as gr
from utils import load_specific_model, inference
from datetime import datetime
import pytz
# current_model = None # Initialize the current model as None
MODEL_NAMES = ["EfficientNet-B3", "EfficientNet-B4", "vgg19", "resnet50", "dinov2_vits14"]
# 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 predict(inp_image, inp_dropdown):
if inp_dropdown is None:
raise gr.Error("No model selected!")
if inp_image is None:
raise gr.Error("No image uploaded!")
if inp_dropdown not in MODEL_NAMES:
raise gr.Error("Invalid model selected!")
# Get the current time in UTC
utc_now = datetime.now(pytz.utc)
# Convert UTC time to German time zone
german_timezone = pytz.timezone('Europe/Berlin')
german_time = utc_now.astimezone(german_timezone)
# Format and print the German time and date
formatted_time = german_time.strftime('%Y-%m-%d %H:%M:%S %Z')
print('Current time and date:', formatted_time)
print(f"\nInput: {inp_dropdown}\n")
current_model = load_specific_model(inp_dropdown)
confidences = inference(current_model, inp_image)
print(f"\nConfidences: {confidences}\n")
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(
MODEL_NAMES,
value="EfficientNet-B3",
label="Model",
info="Select a model to use.",
scale=1,
)
predict_button = gr.Button(value="Predict", label="Predict", info="Click to make a prediction.", scale=1)
predict_button.click(fn=predict, inputs=[user_image, model_dropdown], 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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