import streamlit as st
import tensorflow as tf
import numpy as np
import cv2
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
from io import BytesIO
from PIL import Image
import requests
# os module ki ab yahan directly zaroorat nahi hai, kyunki cache_dir specify nahi ho raha.

# IMPORTANT: Ensure your `requirements.txt` includes tensorflow==2.10.0 or 2.15.0.
# The `get_config` error often stems from incompatible TensorFlow versions.

# Download model from Hugging Face
repo_id = "Hammad712/closed_eye_detection"
filename = "Closed_Eye_Detection_98.h5"

try:
    # Download the model to a local temporary file
    # Removing cache_dir as Hugging Face handles caching internally
    model_path = hf_hub_download(repo_id=repo_id, filename=filename)
    # Load the downloaded model
    model = load_model(model_path)
    st.success("Model loaded successfully!")
except Exception as e:
    st.error(f"Error loading model: {e}. Please ensure correct TensorFlow version in requirements.txt.")
    st.stop() # Stop the app if there's an error

# Set image dimensions
img_height, img_width = 150, 150

# Custom CSS
def set_css(style):
    """
    Function to apply custom CSS to the webpage.
    """
    st.markdown(f"<style>{style}</style>", unsafe_allow_html=True)

combined_css = """
    /* Main background and text colors */
    .main, .sidebar .sidebar-content { background-color: #1c1c1c; color: #f0f2f6; }
    /* Styling for the main content block */
    .block-container { padding: 1rem 2rem; background-color: #333; border-radius: 10px; box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.5); margin-bottom: 20px; }
    /* Button styling */
    .stButton>button, .stDownloadButton>button {
        background: linear-gradient(135deg, #ff7e5f, #feb47b); /* Colorful gradient */
        color: white;
        border: none;
        padding: 10px 24px;
        text-align: center;
        text-decoration: none;
        display: inline-block;
        font-size: 16px;
        margin: 4px 2px;
        cursor: pointer;
        border-radius: 5px;
        transition: all 0.3s ease; /* Smooth transition */
    }
    .stButton>button:hover, .stDownloadButton>button:hover {
        opacity: 0.9;
        transform: translateY(-2px); /* Lift slightly on hover */
    }
    /* Spinner color */
    .stSpinner { color: #4CAF50; }
    /* Title styling */
    .title {
        font-size: 3rem;
        font-weight: bold;
        display: flex;
        align-items: center;
        justify-content: center;
        text-shadow: 2px 2px 5px rgba(0,0,0,0.7); /* Shadow on title */
        margin-bottom: 20px;
    }
    /* Colorful text (gradient) */
    .colorful-text {
        background: -webkit-linear-gradient(135deg, #ff7e5f, #feb47b);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
    }
    /* Black-white text */
    .black-white-text {
        color: white; /* White color will look better */
        margin-left: 10px; /* Give a little spacing */
    }
    /* Input field styling */
    .small-input .stTextInput>div>input {
        height: 2.5rem;
        font-size: 1rem;
        border-radius: 8px;
        border: 1px solid #ff7e5f; /* Border matching gradient color */
        background-color: #444;
        color: #f0f2f6;
        padding: 0 10px;
    }
    /* File uploader styling */
    .small-file-uploader .stFileUploader>div>div {
        height: 2.5rem;
        font-size: 1rem;
    }
    /* Custom text (subtitle) */
    .custom-text {
        font-size: 1.2rem;
        color: #feb47b;
        text-align: center;
        margin-top: -10px; /* Bring closer to title */
        margin-bottom: 30px; /* More space at bottom */
    }
    /* Style for expander title */
    .stExpander {
        border: 1px solid #feb47b;
        border-radius: 10px;
        background-color: #282828;
    }
    .stExpander div[data-baseweb="button"] {
        color: #feb47b !important;
        font-weight: bold;
    }
    .stExpander > div > div > div > div > p {
        color: #f0f2f6 !important;
    }
"""

# Streamlit application
st.set_page_config(layout="centered", page_title="Eye Detection") # Center layout, add a page title
set_css(combined_css)

# Title and subtitle
st.markdown('<div class="title"><span class="colorful-text">آँख</span> <span class="black-white-text">डिटेक्शन मॉडल</span></div>', unsafe_allow_html=True)
st.markdown('<div class="custom-text">यह पहचानने के लिए एक छवि अपलोड करें या URL प्रदान करें कि आँखें खुली हैं या बंद।</div>', unsafe_allow_html=True)

# Input for image URL or path
with st.expander("इनपुट विकल्प", expanded=True):
    url = st.text_input("छवि URL दर्ज करें", "")
    uploaded_file = st.file_uploader("या एक छवि अपलोड करें", type=["jpg", "jpeg", "png"])

def load_image_from_url(url):
    """
    Function to load an image from a URL.
    """
    if not url:
        return None
    try:
        response = requests.get(url, timeout=10) # Add a 10-second timeout
        response.raise_for_status() # Raise an exception for HTTP errors
        img = Image.open(BytesIO(response.content)).convert('RGB')
        return np.array(img)
    except requests.exceptions.Timeout:
        st.error("Error: Timeout while downloading image from URL.")
        return None
    except requests.exceptions.RequestException as e:
        st.error(f"Error fetching image from URL: {e}")
        return None
    except Exception as e:
        st.error(f"Error processing image from URL: {e}")
        return None

if uploaded_file is not None or url:
    image = None
    if uploaded_file is not None:
        # Read the uploaded image
        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
        image = cv2.imdecode(file_bytes, 1) # OpenCV reads in BGR format
        st.session_state['current_image'] = image # Save image to session state
    elif url:
        # Read the image from URL
        with st.spinner('URL से छवि लोड हो रही है...'):
            image = load_image_from_url(url)
        if image is not None:
            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # PIL gives RGB, OpenCV wants BGR
            st.session_state['current_image'] = image # Save image to session state
        else:
            st.warning("No valid image loaded from URL.")
            st.session_state['current_image'] = None

    if st.session_state.get('current_image') is not None:
        image_to_predict = st.session_state['current_image']
        # Resize and preprocess the image
        resized_image = cv2.resize(image_to_predict, (img_height, img_width))
        # Reshape for model input (batch_size, height, width, channels)
        input_image = resized_image.reshape((1, img_height, img_width, 3)) / 255.0

        # Perform inference
        with st.spinner('अनुमान लगाया जा रहा है...'):
            predictions = model.predict(input_image)
        prediction = predictions[0][0] # Get the single scalar value

        def get_label(prediction_value):
            """
            Get the label based on the prediction value.
            """
            return "खुली आँख" if prediction_value >= 0.5 else "बंद आँख"

        label = get_label(prediction)

        # Display the image and prediction
        st.image(image_to_predict, channels="BGR", caption='अपलोड की गई छवि' if uploaded_file is not None else 'URL से छवि', use_column_width=True)
        
        # Predicted result in bold and larger font
        st.markdown(f"### **अनुमान:** `{prediction:.4f}`")
        st.markdown(f"### **लेबल:** <span class='{'colorful-text' if label == 'खुली आँख' else 'black-white-text'}' style='font-size: 2rem;'>{label}</span>", unsafe_allow_html=True)
        
        # Download button for the uploaded image (optional)
        img_byte_arr = BytesIO()
        # Convert from OpenCV (BGR) to PIL (RGB) so PIL can save correctly
        img_pil = Image.fromarray(cv2.cvtColor(image_to_predict, cv2.COLOR_BGR2RGB))
        img_pil.save(img_byte_arr, format='JPEG')
        img_byte_arr = img_byte_arr.getvalue()

        st.download_button(
            label="छवि डाउनलोड करें",
            data=img_byte_arr,
            file_name="processed_eye_image.jpg",
            mime="image/jpeg"
        )
    else:
        st.info("कृपया आगे बढ़ने के लिए एक छवि अपलोड करें या URL प्रदान करें।")