app.py

import gradio as gr
import cv2
import numpy as np
from datetime import datetime
from diffusers import DiffusionPipeline
import random

#----------Start of theme----------
theme = gr.themes.Soft(
    primary_hue="zinc",
    secondary_hue="stone",
    font=[gr.themes.GoogleFont('Kavivanar'), gr.themes.GoogleFont('Kavivanar'), 'system-ui', 'sans-serif'],
    font_mono=[gr.themes.GoogleFont('Source Code Pro'), gr.themes.GoogleFont('Inconsolata'), gr.themes.GoogleFont('Inconsolata'), 'monospace'],
).set(
    body_background_fill='*primary_100',
    body_text_color='secondary_600',
    body_text_color_subdued='*primary_500',
    body_text_weight='500',
    background_fill_primary='*primary_100',
    background_fill_secondary='*secondary_200',
    color_accent='*primary_300',
    border_color_accent_subdued='*primary_400',
    border_color_primary='*primary_400',
    block_background_fill='*primary_300',
    block_border_width='*panel_border_width',
    block_info_text_color='*primary_700',
    block_info_text_size='*text_md',
    panel_background_fill='*primary_200',
    accordion_text_color='*primary_600',
    table_text_color='*primary_600',
    input_background_fill='*primary_50',
    input_background_fill_focus='*primary_100',
    button_primary_background_fill='*primary_500',
    button_primary_background_fill_hover='*primary_400',
    button_primary_text_color='*primary_50',
    button_primary_text_color_hover='*primary_100',
    button_cancel_background_fill='*primary_500',
    button_cancel_background_fill_hover='*primary_400'
)
#----------End of theme----------

def flip_image(x):
    return np.fliplr(x)

def basic_filter(image, filter_type):
    """Apply basic image filters"""
    if filter_type == "Gray Toning":
        return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    elif filter_type == "Sepia":
        sepia_filter = np.array([
            [0.272, 0.534, 0.131],
            [0.349, 0.686, 0.168],
            [0.393, 0.769, 0.189]
        ])
        return cv2.transform(image, sepia_filter)
    elif filter_type == "X-ray":
        # Improved X-ray effect
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        inverted = cv2.bitwise_not(gray)
        # Increase contrast
        clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
        enhanced = clahe.apply(inverted)
        # Sharpen
        kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
        sharpened = cv2.filter2D(enhanced, -1, kernel)
        return cv2.cvtColor(sharpened, cv2.COLOR_GRAY2BGR)
    elif filter_type == "Burn it":
        return cv2.GaussianBlur(image, (15, 15), 0)

def classic_filter(image, filter_type):
    """Classical display filters"""
    if filter_type == "Charcoal Effect":
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        inverted = cv2.bitwise_not(gray)
        blurred = cv2.GaussianBlur(inverted, (21, 21), 0)
        sketch = cv2.divide(gray, cv2.subtract(255, blurred), scale=256)
        return cv2.cvtColor(sketch, cv2.COLOR_GRAY2BGR)
    
    elif filter_type == "Sharpen":
        kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
        return cv2.filter2D(image, -1, kernel)
    
    elif filter_type == "Embossing":
        kernel = np.array([[0,-1,-1], [1,0,-1], [1,1,0]])
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        emboss = cv2.filter2D(gray, -1, kernel) + 128
        return cv2.cvtColor(emboss, cv2.COLOR_GRAY2BGR)
    
    elif filter_type == "Edge Detection":
        edges = cv2.Canny(image, 100, 200)
        return cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)

def creative_filters(image, filter_type):
    """Creative and unusual image filters"""
    if filter_type == "Pixel Art":
        h, w = image.shape[:2]
        piksel_size = 20
        small = cv2.resize(image, (w//piksel_size, h//piksel_size))
        return cv2.resize(small, (w, h), interpolation=cv2.INTER_NEAREST)
    
    elif filter_type == "Mosaic Effect":
        h, w = image.shape[:2]
        mosaic_size = 30
        for i in range(0, h, mosaic_size):
            for j in range(0, w, mosaic_size):
                roi = image[i:i+mosaic_size, j:j+mosaic_size]
                if roi.size > 0:
                    color = np.mean(roi, axis=(0,1))
                    image[i:i+mosaic_size, j:j+mosaic_size] = color
        return image
    
    elif filter_type == "Rainbow":
        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
        h, w = image.shape[:2]
        for i in range(h):
            hsv[i, :, 0] = (hsv[i, :, 0] + i % 180).astype(np.uint8)
        return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
    
    elif filter_type == "Night Vision":
        green_image = image.copy()
        green_image[:,:,0] = 0  # Blue channel
        green_image[:,:,2] = 0  # Red channel
        return cv2.addWeighted(green_image, 1.5, np.zeros(image.shape, image.dtype), 0, -50)

def special_effects(image, filter_type):
    """Apply special effects"""
    if filter_type == "Matrix Effect":
        green_matrix = np.zeros_like(image)
        green_matrix[:,:,1] = image[:,:,1]  # Only green channel
        random_brightness = np.random.randint(0, 255, size=image.shape[:2])
        green_matrix[:,:,1] = np.minimum(green_matrix[:,:,1] + random_brightness, 255)
        return green_matrix
    
    elif filter_type == "Wave Effect":
        rows, cols = image.shape[:2]
        img_output = np.zeros(image.shape, dtype=image.dtype)
        
        for i in range(rows):
            for j in range(cols):
                offset_x = int(25.0 * np.sin(2 * 3.14 * i / 180))
                offset_y = int(25.0 * np.cos(2 * 3.14 * j / 180))
                if i+offset_x < rows and j+offset_y < cols:
                    img_output[i,j] = image[(i+offset_x)%rows,(j+offset_y)%cols]
                else:
                    img_output[i,j] = 0
        return img_output
    
    elif filter_type == "Time Stamp":
        output = image.copy()
        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        font = cv2.FONT_HERSHEY_SIMPLEX
        cv2.putText(output, timestamp, (10, 30), font, 1, (255, 255, 255), 2)
        return output
    
    elif filter_type == "Glitch Effect":
        glitch = image.copy()
        h, w = image.shape[:2]
        for _ in range(10):
            x1 = random.randint(0, w-50)
            y1 = random.randint(0, h-50)
            x2 = random.randint(x1, min(x1+50, w))
            y2 = random.randint(y1, min(y1+50, h))
            glitch[y1:y2, x1:x2] = np.roll(glitch[y1:y2, x1:x2], 
                                          random.randint(-20, 20), 
                                          axis=random.randint(0, 1))
        return glitch

def artistic_filters(image, filter_type):
    """Applies artistic image filters"""
    if filter_type == "Pop Art":
        img_small = cv2.resize(image, None, fx=0.5, fy=0.5)
        img_color = cv2.resize(img_small, (image.shape[1], image.shape[0]))
        for _ in range(2):
            img_color = cv2.bilateralFilter(img_color, 9, 300, 300)
        hsv = cv2.cvtColor(img_color, cv2.COLOR_BGR2HSV)
        hsv[:,:,1] = hsv[:,:,1]*1.5
        return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
    
    elif filter_type == "Oil Paint":
        ret = np.float32(image.copy())
        ret = cv2.bilateralFilter(ret, 9, 75, 75)
        ret = cv2.detailEnhance(ret, sigma_s=15, sigma_r=0.15)
        ret = cv2.edgePreservingFilter(ret, flags=1, sigma_s=60, sigma_r=0.4)
        return np.uint8(ret)
    
    elif filter_type == "Cartoon":
        # Improved cartoon effect
        color = image.copy()
        gray = cv2.cvtColor(color, cv2.COLOR_BGR2GRAY)
        gray = cv2.medianBlur(gray, 5)
        edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 9)
        color = cv2.bilateralFilter(color, 9, 300, 300)
        cartoon = cv2.bitwise_and(color, color, mask=edges)
        # Increase color saturation
        hsv = cv2.cvtColor(cartoon, cv2.COLOR_BGR2HSV)
        hsv[:,:,1] = hsv[:,:,1]*1.4  # saturation increase
        return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

def atmospheric_filters(image, filter_type):
    """atmospheric filters"""
    if filter_type == "Autumn":
        # Genhanced autumn effect
        autumn_filter = np.array([
            [0.393, 0.769, 0.189],
            [0.349, 0.686, 0.168],
            [0.272, 0.534, 0.131]
        ])
        autumn = cv2.transform(image, autumn_filter)
        # Increase color temperature
        hsv = cv2.cvtColor(autumn, cv2.COLOR_BGR2HSV)
        hsv[:,:,0] = hsv[:,:,0]*0.8  # Shift to orange/yellow tones
        hsv[:,:,1] = hsv[:,:,1]*1.2  # Increase saturation
        return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
    
    elif filter_type == "Nostalgia":
        # Improved nostalgia effect
        # Reduce contrast and add yellowish tone
        image = cv2.convertScaleAbs(image, alpha=0.9, beta=10)
        sepia = cv2.transform(image, np.array([
            [0.393, 0.769, 0.189],
            [0.349, 0.686, 0.168],
            [0.272, 0.534, 0.131]
        ]))
        # Darkening effect in corners
        h, w = image.shape[:2]
        kernel = np.zeros((h, w))
        center = (h//2, w//2)
        for i in range(h):
            for j in range(w):
                dist = np.sqrt((i-center[0])**2 + (j-center[1])**2)
                kernel[i,j] = 1 - min(1, dist/(np.sqrt(h**2 + w**2)/2))
        kernel = np.dstack([kernel]*3)
        return cv2.multiply(sepia, kernel).astype(np.uint8)
    
    elif filter_type == "Increase Brightness":
        # Improved brightness boost
        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
        # Increase brightness
        hsv[:,:,2] = cv2.convertScaleAbs(hsv[:,:,2], alpha=1.2, beta=30)
        # Also increase the contrast slightly
        return cv2.convertScaleAbs(cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR), alpha=1.1, beta=0)

def image_processing(image, filter_type):
    """Main image processing function"""
    if image is None:
        return None
    
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    
    # Process by filter categories
    basic_filter_list = ["Gray Toning", "Sepia", "X-ray", "Burn it"]
    classic_filter_list = ["Charcoal Effect", "Sharpen", "Embossing", "Edge Detection"]
    creative_filters_list = ["Rainbow", "Night Vision"]
    special_effects_list = ["Matrix Effect", "Wave Effect", "Time Stamp", "Glitch Effect"]
    artistic_filters_list = ["Pop Art", "Oil Paint", "Cartoon"]
    atmospheric_filters_list = ["Autumn", "Increase Brightness"]
    
    if filter_type in basic_filter_list:
        output = basic_filter(image, filter_type)
    elif filter_type in classic_filter_list:
        output = classic_filter(image, filter_type)
    elif filter_type in creative_filters_list:
        output = creative_filters(image, filter_type)
    elif filter_type in special_effects_list:
        output = special_effects(image, filter_type)
    elif filter_type in artistic_filters_list:
        output = artistic_filters(image, filter_type)
    elif filter_type in atmospheric_filters_list:
        output = atmospheric_filters(image, filter_type)
    else:
        output = image
        
    return cv2.cvtColor(output, cv2.COLOR_BGR2RGB) if len(output.shape) == 3 else output
    

css = """
#app-container {
    max-width: 1200px;
    margin-left: auto;
    margin-right: auto;
}
"""

# Gradio interface
with gr.Blocks(theme=theme, css=css) as app:
    gr.HTML("<center><h6>🎨 Image Studio</h6></center>")
    with gr.Tab("Flip Image"):
                with gr.Row():
                    image_input = gr.Image(type="numpy", label="Upload Image")
                    image_output = gr.Image(format="png")
                with gr.Row():    
                    image_button = gr.Button("Run", variant='primary')
                    image_button.click(flip_image, inputs=image_input, outputs=image_output)
    with gr.Tab("Image Filters"):    
        with gr.Row():
            with gr.Column():
                image_input = gr.Image(type="numpy", label="Upload Image")
                with gr.Accordion("ℹ️ Filter Categories", open=True):
                    filter_type = gr.Dropdown(
                        [
                            # Basic Filters
                            "Gray Toning", "Sepia", "X-ray", "Burn it",
                            # Classic Filter 
                            "Charcoal Effect", "Sharpen", "Embossing", "Edge Detection",                       
                            # Creative Filters
                            "Rainbow", "Night Vision",
                            # Special Effects
                            "Matrix Effect", "Wave Effect", "Time Stamp", "Glitch Effect",
                            # Artistic Filters
                            "Pop Art", "Oil Paint", "Cartoon",
                            # Atmospheric Filters
                            "Autumn", "Increase Brightness"
                        ],
                        label="🎭 Select Filter",
                        info="Choose the effect you want"
                    )
                submit_button = gr.Button("✨ Apply Filter", variant="primary")
    
            with gr.Column():
                image_output = gr.Image(label="🖼️ Filtered Image")
                
            submit_button.click(
                image_processing,
                inputs=[image_input, filter_type],
                outputs=image_output
            )
    with gr.Tab("Text to Image"):
 
       # gr.load("models/digiplay/AnalogMadness-realistic-model-v7")
        pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
        pipe.load_lora_weights("XLabs-AI/flux-RealismLora")

        prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
        image = pipe(prompt).images[0]
    
app.launch(share=True)