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import gradio as gr
from weight_detector import WeightDetector
import tempfile
import os
from PIL import Image
import requests
from io import BytesIO
# Initialize detector
detector = WeightDetector()
def process_input(image_source: str, image_upload=None, image_url: str = "") -> tuple:
"""
Process image from different sources (upload, webcam, or URL)
Returns:
tuple: (detected_weight, detection_metadata, annotated_image)
"""
temp_img_path = None
try:
# Handle different input types
if image_source == "upload" and image_upload is not None:
img = image_upload
elif image_source == "url" and image_url:
response = requests.get(image_url)
img = Image.open(BytesIO(response.content))
else:
return None, "No valid image provided", None
# Save to temp file for processing
with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f:
temp_img_path = f.name
img.save(f.name)
# Detect weight
weight, metadata, annotated_img = detector.detect_weight(temp_img_path)
# Format result message
if weight is not None:
message = f"✅ Detected weight: {weight}g"
if len(metadata) > 1:
message += f" (from {len(metadata)} possible values)"
else:
message = "❌ No weight value detected"
return weight, message, annotated_img
except Exception as e:
return None, f"Error: {str(e)}", None
finally:
if temp_img_path and os.path.exists(temp_img_path):
os.unlink(temp_img_path)
# Gradio interface
with gr.Blocks(title="Auto Weight Logger") as demo:
gr.Markdown("""
# Auto Weight Logger
Capture or upload an image of a weight measurement to automatically detect and log the value.
""")
with gr.Row():
with gr.Column():
image_source = gr.Radio(
["upload", "url"],
label="Image Source",
value="upload"
)
image_upload = gr.Image(
sources=["upload"],
type="pil",
label="Upload Image"
)
image_url = gr.Textbox(
label="Image URL",
visible=False
)
submit_btn = gr.Button("Detect Weight")
with gr.Column():
weight_value = gr.Number(
label="Detected Weight (g)",
interactive=False
)
result_message = gr.Textbox(
label="Detection Result",
interactive=False
)
annotated_image = gr.Image(
label="Annotated Image",
interactive=False
)
# Show/hide URL input based on selection
def toggle_url_visibility(source):
return gr.Textbox(visible=source == "url")
image_source.change(
toggle_url_visibility,
inputs=image_source,
outputs=image_url
)
# Process submission
submit_btn.click(
process_input,
inputs=[image_source, image_upload, image_url],
outputs=[weight_value, result_message, annotated_image]
)
# For Hugging Face Spaces
demo.launch() |