import streamlit as st
from PIL import Image, UnidentifiedImageError
import io
import uuid
import urllib.parse
from datetime import datetime
import pytz
from ocr_engine import extract_weight_from_image  # Ensure this function returns (weight, confidence)

# Set up page
st.set_page_config(page_title="⚖️ Auto Weight Logger", layout="centered")
st.title("⚖️ Auto Weight Logger")

# Session state
if "camera_key" not in st.session_state:
    st.session_state.camera_key = str(uuid.uuid4())
if "captured_time" not in st.session_state:
    st.session_state.captured_time = ""
if "image_bytes" not in st.session_state:
    st.session_state.image_bytes = None

# Get IST time
def get_current_ist_time():
    ist = pytz.timezone("Asia/Kolkata")
    return datetime.now(ist).strftime("%Y-%m-%d %I:%M:%S %p")

# Input method
input_mode = st.radio("📸 Select Input Method", ["Camera", "Upload"], horizontal=True)

# Retake button
if st.button("🔁 Clear / Retake"):
    st.session_state.camera_key = str(uuid.uuid4())
    st.session_state.captured_time = ""
    st.session_state.image_bytes = None
    st.rerun()

# Capture or Upload
if input_mode == "Camera":
    cam_photo = st.camera_input("📷 Capture Weight Display", key=st.session_state.camera_key)
    if cam_photo:
        st.session_state.image_bytes = cam_photo.getvalue()
        st.session_state.captured_time = get_current_ist_time()

elif input_mode == "Upload":
    uploaded_file = st.file_uploader("📁 Upload an image (JPG/PNG)", type=["jpg", "jpeg", "png"])
    if uploaded_file:
        st.session_state.image_bytes = uploaded_file.read()
        st.session_state.captured_time = get_current_ist_time()

# Process and Display (if image is available)
if st.session_state.image_bytes:
    try:
        image = Image.open(io.BytesIO(st.session_state.image_bytes))

        # 1. Show captured time
        st.markdown(f"### 🕒 Captured At (IST): `{st.session_state.captured_time}`")

        # 2. Show image
        st.markdown("### 🖼️ Snapshot Image")
        st.image(image, use_column_width=True)

        # 3. OCR processing
        if len(st.session_state.image_bytes) > 5 * 1024 * 1024:
            st.error("❌ Image too large (>5MB). Please upload a smaller image.")
            st.stop()

        with st.spinner("🔍 Extracting weight using OCR..."):
            weight, confidence = extract_weight_from_image(image)

        # 4. Display OCR result
        st.markdown("### ⚖️ Captured Weight & OCR Confidence")
        if not weight or confidence < 80:
            st.error(f"⚠️ Low OCR Confidence ({int(confidence)}%). Please retake or upload a clearer image.")
            st.markdown("**Detected Weight:** Not reliable")
        else:
            st.success(f"✅ Detected Weight: `{weight} g`")
            st.markdown(f"**Confidence:** `{int(confidence)}%`")

        # 5. Send to Salesforce
        device_id = "BAL-001"
        image_url = ""  # optional

        salesforce_url = (
            "https://autoweightlogger-dev-ed.my.salesforce-sites.com/"
            f"weight_logger_page?WeightInput={urllib.parse.quote(str(weight))}"
            f"&DeviceID={urllib.parse.quote(device_id)}&ImageURL={urllib.parse.quote(image_url)}"
        )

        st.markdown("### ✅ Send to Salesforce")
        st.markdown(
            f"""
            <a href="{salesforce_url}" target="_blank">
                <button style="
                    background-color: #4CAF50;
                    border: none;
                    color: white;
                    padding: 12px 24px;
                    text-align: center;
                    text-decoration: none;
                    display: inline-block;
                    font-size: 16px;
                    border-radius: 8px;
                    cursor: pointer;
                ">📤 Log to Salesforce</button>
            </a>
            """, unsafe_allow_html=True
        )

    except UnidentifiedImageError:
        st.error("❌ Unable to process image. Please upload a valid JPG or PNG.")
    except Exception as e:
        st.error("❌ Unexpected error.")
        st.exception(e)