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()