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import gradio as gr
import cv2
import numpy as np
from PIL import Image
import logging
from ocr_engine import extract_weight_from_image
from datetime import datetime
import pytz
import sys
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[logging.StreamHandler(sys.stdout)])
def process_image(img):
try:
# Convert Gradio image (PIL) to process
if img is None:
return "No image provided", 0.0, "", None
# Resize if > 5MB
img_bytes = img.tobytes()
size_mb = len(img_bytes) / (1024 * 1024)
if size_mb > 5:
scale = 0.9
while size_mb > 5:
w, h = img.size
img = img.resize((int(w * scale), int(h * scale)), Image.Resampling.LANCZOS)
img_bytes = img.tobytes()
size_mb = len(img_bytes) / (1024 * 1024)
scale *= 0.9
logging.info(f"Resized image to {size_mb:.2f} MB")
# Extract weight
weight, confidence, unit = extract_weight_from_image(img)
# Return results
return f"{weight} {unit} (Confidence: {confidence:.2f}%)", f"Processed at {datetime.now(pytz.timezone('Asia/Kolkata')).strftime('%d-%m-%Y %I:%M:%S %p IST')}", img
except Exception as e:
logging.error(f"Error in process_image: {str(e)}")
return f"Error: {str(e)}", "", None
# Gradio interface
with gr.Blocks(title="Auto Weight Logger") as demo:
gr.Markdown("""
# 📷 Auto Weight Logger — OCR-Based Smart Scale Reader
This app detects weight from uploaded or captured images of digital balance displays. Optimized for 7-segment displays and various formats, it extracts numeric weights with high accuracy.
""")
with gr.Row():
with gr.Column():
image_input = gr.Image(source="upload", tool="select", type="pil", label="Upload Weight Display Image")
webcam_input = gr.Image(source="webcam", type="pil", label="Or Capture with Webcam")
submit_btn = gr.Button("Detect Weight")
with gr.Column():
output_text = gr.Textbox(label="Detected Weight", interactive=False)
timestamp_text = gr.Textbox(label="Processed At", interactive=False)
output_image = gr.Image(label="Processed Image")
submit_btn.click(
fn=process_image,
inputs=[image_input],
outputs=[output_text, timestamp_text, output_image]
)
webcam_input.change(
fn=process_image,
inputs=[webcam_input],
outputs=[output_text, timestamp_text, output_image]
)
demo.launch() |