Update app.back

app.back

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+import gradio as gr
+import random
+import os
+import torch
+import subprocess
+import numpy as np
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+from diffusers import DiffusionPipeline
+import cv2
+from datetime import datetime
+from theme import theme
+from fastapi import FastAPI
+
+app = FastAPI()
+
+
+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
+
+    # Get absolute path of image file
+image_path = 'https://huggingface.co/spaces/DigiP-AI/Image_Studio/blob/main/abstract.jpg' # Replace with your image file path
+
+absolute_path = os.path.abspath(image_path)
+    
+css = """
+.gradio-container {
+    background: url(https://huggingface.co/spaces/DigiP-AI/Image_Studio/blob/main/abstract.jpg)
+}
+"""
+
+# Gradio interface
+with gr.Blocks(theme=theme, css=css) as app:
+    gr.HTML("<center><h6>🎨 Image Studio</h6></center>")
+
+    with gr.Tab("Image to Prompt"): 
+        subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+        # Initialize Florence model
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
+        florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
+
+        # api_key = os.getenv("HF_READ_TOKEN")
+        
+        def generate_caption(image):
+            if not isinstance(image, Image.Image):
+                image = Image.fromarray(image)
+            
+            inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
+            generated_ids = florence_model.generate(
+                input_ids=inputs["input_ids"],
+                pixel_values=inputs["pixel_values"],
+                max_new_tokens=1024,
+                early_stopping=False,
+                do_sample=False,
+                num_beams=3,
+            )
+            generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+            parsed_answer = florence_processor.post_process_generation(
+                generated_text,
+                task="<MORE_DETAILED_CAPTION>",
+                image_size=(image.width, image.height)
+            )
+            prompt =  parsed_answer["<MORE_DETAILED_CAPTION>"]
+            print("\n\nGeneration completed!:"+ prompt)
+            return prompt
+
+        io = gr.Interface(generate_caption,
+            inputs=[gr.Image(label="Input Image",height=320)],
+            outputs = [gr.Textbox(label="Output Prompt", lines=2, show_copy_button = True),
+            # gr.Image(label="Output Image")
+                       ]
+                    )
+    
+    with gr.Tab("Text to Image"):
+        gr.HTML("<center><h6>ℹ️ Please do not run the models at the same time, the models are currently running on the CPU, which might affect performance.</h6></center>")        
+        with gr.Accordion("Turbo-HyperRealistic", open=False):
+            model1 = gr.load("models/prithivMLmods/SD3.5-Large-Turbo-HyperRealistic-LoRA")
+        with gr.Accordion("Turbo-Realism", open=False):
+            model2 = gr.load("models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA")
+        with gr.Accordion("Stable-Diffusion-3.5-large", open=False):
+            model3 = gr.load("models/stabilityai/stable-diffusion-3.5-large")
+        
+    with gr.Tab("Flip Image"):
+                with gr.Row():
+                    image_input = gr.Image(type="numpy", label="Upload Image",height=320)
+                    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.Row():     
+                    clear_results = gr.Button(value="Clear Image", variant="primary", elem_id="clear_button")
+                    clear_results.click(lambda: (None, None), None, [image_input, image_output])
+    with gr.Tab("Image Filters"):    
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(type="numpy", label="Upload Image", height=320)
+                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("Image Upscaler"):    
+        with gr.Row():
+            with gr.Column():
+                def upscale_image(input_image, radio_input):
+                    upscale_factor = radio_input
+                    output_image = cv2.resize(input_image, None, fx = upscale_factor, fy = upscale_factor, interpolation = cv2.INTER_CUBIC)
+                    return output_image
+                
+                radio_input = gr.Radio(label="Upscale Levels", choices=[2, 4, 6, 8, 10], value=2)
+                
+                iface = gr.Interface(fn=upscale_image, inputs = [gr.Image(label="Input Image", interactive=True), radio_input], outputs = gr.Image(label="Upscaled Image", format="png"), title="Image Upscaler")
+    
+app.launch(share=True)