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| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import cv2 | |
| import re | |
| from paddleocr import PaddleOCR | |
| from datetime import datetime | |
| # Load OCR model | |
| ocr = PaddleOCR(use_angle_cls=True, lang='en') | |
| def preprocess_image(image): | |
| img = np.array(image.convert("RGB")) | |
| gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
| _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY) | |
| return Image.fromarray(thresh) | |
| def extract_weight_text(image): | |
| try: | |
| results = ocr.ocr(np.array(image), cls=True) | |
| texts = [] | |
| for line in results[0]: | |
| text = line[1][0] | |
| texts.append(text) | |
| match = re.search(r"\d+\.\d+", text) | |
| if match: | |
| return match.group(), texts | |
| return None, texts | |
| except Exception as e: | |
| return f"Error: {str(e)}", [] | |
| def detect_weight(image): | |
| if image is None: | |
| return "No image uploaded.", "", None, "" | |
| pre_img = preprocess_image(image) | |
| weight, texts = extract_weight_text(pre_img) | |
| if weight and "Error" not in weight: | |
| return f"Detected Weight: {weight} kg", datetime.now().strftime("Captured At: %Y-%m-%d %H:%M:%S"), pre_img, "\n".join(texts) | |
| else: | |
| return "Weight Not Detected", "", pre_img, "\n".join(texts) | |
| gr.Interface( | |
| fn=detect_weight, | |
| inputs=gr.Image(type="pil", label="Upload or Capture Image"), | |
| outputs=[ | |
| gr.Textbox(label="Weight"), | |
| gr.Textbox(label="Timestamp"), | |
| gr.Image(label="Preprocessed Image"), | |
| gr.Textbox(label="Detected Text (Debug)") | |
| ], | |
| title="Auto Weight Logger (Debug Mode)", | |
| description="Upload a clear image of a digital scale. This version shows detected text for debugging." | |
| ).launch() | |