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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()
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