Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,594 Bytes
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import gradio as gr
from PIL import Image
import os
from classifier import GarbageClassifier
from config import Config
# Check if running in Hugging Face Spaces environment
try:
import spaces
HF_SPACES = True
print("Running in Hugging Face Spaces environment")
except ImportError:
HF_SPACES = False
print("Running in local environment")
# Initialize classifier
config = Config()
classifier = GarbageClassifier(config)
# Load model at startup
print("Loading model...")
classifier.load_model()
print("Model loaded successfully!")
def classify_garbage_impl(image):
"""
Actual classification implementation
"""
if image is None:
return "Please upload an image", "No image provided"
try:
classification, full_response = classifier.classify_image(image)
return classification, full_response
except Exception as e:
return "Error", f"Classification failed: {str(e)}"
# Apply GPU decorator based on environment
if HF_SPACES:
classify_garbage = spaces.GPU(classify_garbage_impl)
print("GPU decorator applied for Hugging Face Spaces")
else:
classify_garbage = classify_garbage_impl
print("Running without GPU decorator")
def get_example_images():
"""Get example images if they exist"""
example_dir = "test_images"
examples = []
if os.path.exists(example_dir):
for file in os.listdir(example_dir):
if file.lower().endswith((".png", ".jpg", ".jpeg")):
examples.append(os.path.join(example_dir, file))
return examples[:3] # Limit to 3 examples
# Create Gradio interface
with gr.Blocks(title="Garbage Classification System") as demo:
gr.Markdown("# 🗂️ Garbage Classification System")
gr.Markdown(
"Upload an image to classify garbage into: Recyclable Waste, Food/Kitchen Waste, Hazardous Waste, or Other Waste"
)
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil", label="Upload Garbage Image")
classify_btn = gr.Button("Classify Garbage", variant="primary", size="lg")
with gr.Column():
classification_output = gr.Textbox(
label="Classification Result",
placeholder="Upload an image and click classify",
)
full_response_output = gr.Textbox(
label="Detailed Analysis",
placeholder="Detailed reasoning will appear here",
lines=10,
)
# Category information
with gr.Accordion("📋 Garbage Categories Information", open=False):
try:
category_info = classifier.get_categories_info()
for category, description in category_info.items():
gr.Markdown(f"**{category}**: {description}")
except Exception as e:
gr.Markdown(f"Categories information not available: {str(e)}")
# Examples section
examples = get_example_images()
if examples:
gr.Examples(examples=examples, inputs=image_input, label="Example Images")
# Event handlers
classify_btn.click(
fn=classify_garbage,
inputs=image_input,
outputs=[classification_output, full_response_output],
)
# Auto-classify on image upload
image_input.change(
fn=classify_garbage,
inputs=image_input,
outputs=[classification_output, full_response_output],
)
if __name__ == "__main__":
# Set appropriate launch parameters based on environment
if HF_SPACES:
demo.launch()
else:
demo.launch(share=True)
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