import os

# Force installation of compatible versions
os.system("pip install --force-reinstall numpy==1.23.5 opencv-python-headless==4.8.0.74")
import cv2

import streamlit as st
from PIL import Image
import numpy as np
import cv2

# Ensure OpenCV installation
try:
    import cv2
except ModuleNotFoundError:
    os.system("pip install opencv-python-headless==4.8.0.74")
    import cv2

# Streamlit App Title
st.title("Image Enhancer App")

# File Uploader
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Load image using PIL and convert to OpenCV format
    image = Image.open(uploaded_file)
    image_np = np.array(image)

    # Display original image
    st.subheader("Original Image")
    st.image(image, caption="Uploaded Image", use_container_width=True)

    # Enhance Options
    st.subheader("Enhancement Options")
    enhancement = st.selectbox("Choose an enhancement type:", [
        "Brightness Adjustment",
        "Contrast Enhancement",
        "Sharpening",
        "Denoising",
        "Edge Detection",
        "Apply All Enhancements"
    ])

    enhanced_image = image_np

    if enhancement == "Brightness Adjustment":
        brightness = st.slider("Adjust Brightness", -100, 100, 0)
        enhanced_image = cv2.convertScaleAbs(enhanced_image, beta=brightness)
    elif enhancement == "Contrast Enhancement":
        contrast = st.slider("Adjust Contrast", 1.0, 3.0, 1.0)
        alpha = contrast  # Simple contrast control
        beta = 0          # Simple brightness control
        enhanced_image = cv2.convertScaleAbs(enhanced_image, alpha=alpha, beta=beta)
    elif enhancement == "Sharpening":
        kernel = np.array([[0, -1, 0],
                           [-1, 5, -1],
                           [0, -1, 0]])
        enhanced_image = cv2.filter2D(enhanced_image, -1, kernel)
    elif enhancement == "Denoising":
        h = st.slider("Denoising Strength", 1, 20, 10)
        enhanced_image = cv2.fastNlMeansDenoisingColored(enhanced_image, None, h, h, 7, 21)
    elif enhancement == "Edge Detection":
        threshold1 = st.slider("Threshold 1", 50, 200, 100)
        threshold2 = st.slider("Threshold 2", 50, 300, 200)
        gray_image = cv2.cvtColor(enhanced_image, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray_image, threshold1, threshold2)
        enhanced_image = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
    elif enhancement == "Apply All Enhancements":
        # Apply all filters sequentially
        brightness = 50
        contrast = 1.5
        h = 10
        kernel = np.array([[0, -1, 0],
                           [-1, 5, -1],
                           [0, -1, 0]])
        gray_image = cv2.cvtColor(enhanced_image, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray_image, 100, 200)
        enhanced_image = cv2.convertScaleAbs(enhanced_image, beta=brightness)
        enhanced_image = cv2.convertScaleAbs(enhanced_image, alpha=contrast, beta=0)
        enhanced_image = cv2.filter2D(enhanced_image, -1, kernel)
        enhanced_image = cv2.fastNlMeansDenoisingColored(enhanced_image, None, h, h, 7, 21)
        edges = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)

    # Display Enhanced Image
    st.subheader("Enhanced Image")
    st.image(enhanced_image, caption="Enhanced Image", use_container_width=True)

    # Download Option
    st.subheader("Download Enhanced Image")
    result_image = Image.fromarray(enhanced_image)
    st.download_button(
        label="Download Image",
        data=result_image.tobytes(),
        file_name="enhanced_image.png",
        mime="image/png"
    )