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import gradio as gr
from transformers import pipeline
# Initialize the classification pipeline with your private model and token
pipe = pipeline(
task="image-classification",
model="scfive/ESPR-Weapon-Classification_App", # Replace with your model repository identifier
)
# Define custom CSS to style the header, footer, and main container
custom_css = """
/* Header styling */
#header {
background-color: #003366;
color: #ffffff;
padding: 20px;
text-align: center;
font-family: 'Arial', sans-serif;
}
#header img {
max-width: 100px;
vertical-align: middle;
margin-right: 15px;
}
/* Footer styling */
#footer {
background-color: #f0f4f8;
color: #003366;
padding: 10px;
text-align: center;
font-family: 'Arial', sans-serif;
font-size: 0.9em;
}
/* Main container styling */
.main-container {
padding: 20px;
margin: auto;
max-width: 800px;
}
/* Button styling */
button {
font-size: 1em;
padding: 10px 20px;
}
"""
# Define the image classification function
def classify_image(image_path):
result = pipe(image_path)
# Return the label from the first result
return result[0]['label']
# Build the Gradio Blocks layout
with gr.Blocks(css=custom_css) as demo:
# Header: includes a logo and the app name
gr.HTML("""
<div id="header">
<img src="https://via.placeholder.com/100" alt="Logo"/>
<span style="font-size: 2em; font-weight: bold;">Weapon Classifier</span>
</div>
""")
# Main content area with an image input and text output
with gr.Column(elem_classes="main-container"):
gr.HTML("<h2>Upload an image to classify the weapon</h2>")
with gr.Row():
image_input = gr.Image(type="filepath", label="Weapon Image")
output_text = gr.Textbox(label="Classification Result")
btn = gr.Button("Classify")
btn.click(fn=classify_image, inputs=image_input, outputs=output_text)
# Footer: explains the app is for game use only
gr.HTML("""
<div id="footer">
This application is intended for game use only. © 2025 My Company.
</div>
""")
# Launch the app
demo.launch()
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