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| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from paddleocr import PaddleOCR | |
| from datetime import datetime | |
| import re | |
| from PIL import Image | |
| # Initialize PaddleOCR (only once) | |
| ocr = PaddleOCR(use_angle_cls=True, lang='en') # Use English OCR model | |
| def detect_weight(image): | |
| if image is None: | |
| return "No image uploaded", "N/A", None | |
| # Convert PIL Image to OpenCV format (NumPy array) | |
| image = image.convert("RGB") | |
| image_np = np.array(image) | |
| # Preprocess: Grayscale + contrast enhancement (optional) | |
| gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) | |
| gray_eq = cv2.equalizeHist(gray) | |
| processed_image = cv2.cvtColor(gray_eq, cv2.COLOR_GRAY2RGB) # convert back to RGB for PaddleOCR | |
| # Run OCR | |
| result = ocr.ocr(processed_image, cls=True) | |
| best_match = None | |
| best_conf = 0 | |
| # Search for a decimal number like 25.52 | |
| for line in result: | |
| for box in line: | |
| text, conf = box[1] | |
| match = re.search(r"\d+\.\d+", text) | |
| if match and conf > best_conf: | |
| best_match = match.group() | |
| best_conf = conf | |
| if best_match: | |
| now = datetime.now().strftime("%Y-%m-%d %H:%M:%S") | |
| return f"Weight: {best_match} kg (Confidence: {round(best_conf * 100, 2)}%)", now, image | |
| else: | |
| return "No weight detected kg (Confidence: 0.0%)", "N/A", image | |
| # Gradio UI | |
| gr.Interface( | |
| fn=detect_weight, | |
| inputs=gr.Image(type="pil", label="Upload or Capture Weight Image"), | |
| outputs=[ | |
| gr.Text(label="Detected Weight"), | |
| gr.Text(label="Captured At (IST)"), | |
| gr.Image(label="Snapshot") | |
| ], | |
| title="Auto Weight Logger", | |
| description="Upload or capture a digital scale image. This app detects the weight automatically using AI." | |
| ).launch() | |