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| import gradio as gr | |
| from paddleocr import PaddleOCR | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| import re | |
| from datetime import datetime | |
| from pytz import timezone | |
| ocr = PaddleOCR(use_angle_cls=False, lang='en') # cls removed | |
| def detect_weight(image): | |
| try: | |
| if image is None: | |
| return "No image uploaded", "N/A", None | |
| image = image.convert("RGB") | |
| image_np = np.array(image) | |
| gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) | |
| enhanced = cv2.equalizeHist(gray) | |
| rgb_image = cv2.cvtColor(enhanced, cv2.COLOR_GRAY2RGB) | |
| result = ocr.ocr(rgb_image) | |
| best_match = None | |
| best_conf = 0 | |
| for line in result: | |
| for box in line: | |
| if len(box) > 1 and isinstance(box[1], tuple): | |
| 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_ist = datetime.now(timezone('Asia/Kolkata')).strftime("%Y-%m-%d %H:%M:%S IST") | |
| return f"Weight: {best_match} kg (Confidence: {round(best_conf * 100, 2)}%)", now_ist, image | |
| else: | |
| return "No weight detected kg (Confidence: 0.0%)", "N/A", image | |
| except Exception as e: | |
| return f"Error: {str(e)}", "Error", None | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## Auto Weight Logger\nUpload or capture a digital scale image. This app detects the weight automatically using AI.") | |
| with gr.Row(): | |
| image_input = gr.Image(type="pil", label="Upload or Capture Weight Image", tool="editor", sources=["upload", "camera"]) | |
| with gr.Column(): | |
| output_text = gr.Textbox(label="Detected Weight") | |
| output_time = gr.Textbox(label="Captured At (IST)") | |
| snapshot_output = gr.Image(label="📷 Snapshot") | |
| image_input.change(fn=detect_weight, inputs=image_input, outputs=[output_text, output_time, snapshot_output]) | |
| demo.launch() | |