src/streamlit_app.py

import streamlit as st
import numpy as np
import cv2
from paddleocr import PaddleOCR
from PIL import Image
import re
from datetime import datetime
import pytz

ocr = PaddleOCR(use_angle_cls=True, lang='en')

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)

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

st.set_page_config(page_title="Auto Weight Logger", layout="centered")
st.title("📦 Auto Weight Logger")
st.write("Upload or click image of weight display. App will read the weight.")

uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
camera_image = st.camera_input("Or Capture Image")

input_image = uploaded_file or camera_image

if input_image:
    image = Image.open(input_image)
    st.image(image, caption="Original", use_column_width=True)

    processed = preprocess_image(image)
    st.image(processed, caption="Processed", use_column_width=True)

    weight = extract_weight_text(processed)

    if weight:
        time_now = datetime.now(pytz.timezone('Asia/Kolkata')).strftime('%Y-%m-%d %H:%M:%S')
        st.success(f"✅ Weight Detected: {weight} kg")
        st.info(f"⏱️ Captured At: {time_now}")
    else:
        st.error("❌ Weight not detected. Try clearer image.")