import gradio as gr
import numpy as np
from PIL import Image
import cv2
import re
from paddleocr import PaddleOCR
from datetime import datetime

# Initialize OCR model once
ocr = PaddleOCR(use_angle_cls=True, lang='en')

# Preprocessing: Convert to grayscale and threshold
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)

# OCR detection + regex filtering
def extract_weight_text(image):
    results = ocr.ocr(np.array(image), cls=True)
    for line in results[0]:
        text = line[1][0]
        match = re.search(r"\d+\.\d+", text)
        if match:
            return match.group()
    return None

# Main function
def detect_weight(image):
    if image is None:
        return "No image uploaded.", "", None
    pre_img = preprocess_image(image)
    weight = extract_weight_text(pre_img)
    if weight:
        return f"Detected Weight: {weight} kg", datetime.now().strftime("Captured At: %Y-%m-%d %H:%M:%S"), pre_img
    else:
        return "Weight Not Detected", "", pre_img

# Gradio UI
interface = 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")
    ],
    title="Auto Weight Logger",
    description="Upload or click image of digital scale. It will detect and show the weight (kg).",
)

interface.launch()