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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() |