Update app.py

app.py

@@ -1,5 +1,4 @@
-# Install necessary libraries
-pip install torch transformers gradio Pillow scikit-learn requests
+# app.py
 
 import numpy as np
 import gradio as gr
@@ -13,16 +12,11 @@ from transformers import (
 from PIL import Image, ImageDraw
 import requests
 from io import BytesIO
-# Load Hugging Face models globally
-print("Loading Hugging Face models...")
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-caption_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
-tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
-translation_model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
 
 # Download example images
 def download_example_images():
     image_urls = [
+        # URL format: ("Image Description", "Image URL")
         ("Sunset over Mountains", "https://images.unsplash.com/photo-1501785888041-af3ef285b470"),
         ("Forest Path", "https://images.unsplash.com/photo-1502082553048-f009c37129b9"),
         ("City Skyline", "https://images.unsplash.com/photo-1498598453737-8913e843c47b"),
@@ -32,14 +26,13 @@ def download_example_images():
     
     example_images = []
     for idx, (description, url) in enumerate(image_urls, start=1):
-        try:
-            response = requests.get(url)
-            response.raise_for_status()
+        response = requests.get(url)
+        if response.status_code == 200:
             img = Image.open(BytesIO(response.content))
             img.save(f'example{idx}.jpg')
             example_images.append([f'example{idx}.jpg'])
-        except requests.RequestException as e:
-            print(f"Failed to download image from {url}: {e}")
+        else:
+            print(f"Failed to download image from {url}")
     return example_images
 
 # Download example images and prepare examples list
@@ -47,77 +40,140 @@ examples = download_example_images()
 
 # Load and Process the Entire Image
 def load_image(image):
+    # Convert PIL image to numpy array (RGB)
+    image_np = np.array(image.convert('RGB'))
+    
+    # Resize the image for better processing
     resized_image = image.resize((300, 300), resample=Image.LANCZOS)
-    return np.array(resized_image)
+    resized_image_np = np.array(resized_image)
+    
+    return resized_image_np
 
 # Extract Dominant Colors from the Image
 def extract_colors(image, k=8):
-    pixels = image.reshape(-1, 3) / 255.0
+    # Flatten the image
+    pixels = image.reshape(-1, 3)
+    # Normalize pixel values to [0, 1]
+    pixels = pixels / 255.0
+    # Ensure data type is float64
+    pixels = pixels.astype(np.float64)
+    # Apply K-means clustering to find dominant colors
     kmeans = KMeans(n_clusters=k, random_state=0, n_init=10, max_iter=300)
     kmeans.fit(pixels)
-    return (kmeans.cluster_centers_ * 255).astype(int)
+    # Convert normalized colors back to 0-255 scale
+    colors = (kmeans.cluster_centers_ * 255).astype(int)
+    return colors
 
 # Create an Image for the Color Palette
 def create_palette_image(colors):
     num_colors = len(colors)
-    palette_image = Image.new("RGB", (100 * num_colors, 100))
+    palette_height = 100
+    palette_width = 100 * num_colors
+    palette_image = Image.new("RGB", (palette_width, palette_height))
+    
     draw = ImageDraw.Draw(palette_image)
     for i, color in enumerate(colors):
+        # Ensure color values are within the valid range and integers
         color = tuple(np.clip(color, 0, 255).astype(int))
-        draw.rectangle([i * 100, 0, (i + 1) * 100, 100], fill=color)
+        draw.rectangle([i * 100, 0, (i + 1) * 100, palette_height], fill=color)
+    
     return palette_image
 
 # Display Color Palette as Hex Codes
 def display_palette(colors):
-    return ["#{:02x}{:02x}{:02x}".format(*np.clip(color, 0, 255)) for color in colors]
+    hex_colors = []
+    for color in colors:
+        # Ensure color values are within valid range and integers
+        color = np.clip(color, 0, 255).astype(int)
+        hex_color = "#{:02x}{:02x}{:02x}".format(color[0], color[1], color[2])
+        hex_colors.append(hex_color)
+    return hex_colors
 
 # Generate Image Caption Using Hugging Face BLIP
 def generate_caption(image):
+    # Load models only once
+    if 'processor' not in generate_caption.__dict__:
+        generate_caption.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+        generate_caption.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+    processor = generate_caption.processor
+    model = generate_caption.model
+    
     inputs = processor(images=image, return_tensors="pt")
-    output = caption_model.generate(**inputs)
-    return processor.decode(output[0], skip_special_tokens=True)
+    output = model.generate(**inputs)
+    caption = processor.decode(output[0], skip_special_tokens=True)
+    return caption
 
 # Translate Caption to Arabic Using mBART
 def translate_to_arabic(text):
+    # Load models only once
+    if 'tokenizer' not in translate_to_arabic.__dict__:
+        translate_to_arabic.tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+        translate_to_arabic.model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+    tokenizer = translate_to_arabic.tokenizer
+    model = translate_to_arabic.model
+    
     tokenizer.src_lang = "en_XX"
     encoded = tokenizer(text, return_tensors="pt")
-    generated_tokens = translation_model.generate(
+    generated_tokens = model.generate(
         **encoded, 
         forced_bos_token_id=tokenizer.lang_code_to_id["ar_AR"]
     )
-    return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+    translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+    return translated_text
 
 # Gradio Interface Function (Combining Elements)
 def process_image(image):
+    # Ensure input is a PIL Image
     if isinstance(image, np.ndarray):
         image = Image.fromarray(image)
     
+    # Convert to RGB format for PIL processing
     image_rgb = image.convert("RGB")
+    
+    # Load and resize the entire image
     resized_image_np = load_image(image_rgb)
+    
+    # Convert resized image to PIL Image for Gradio output
     resized_image_pil = Image.fromarray(resized_image_np)
     
+    # Generate caption using BLIP model
     caption = generate_caption(image_rgb)
+    
+    # Translate caption to Arabic
     caption_arabic = translate_to_arabic(caption)
     
+    # Extract dominant colors from the entire image
     colors = extract_colors(resized_image_np, k=8)
     color_palette = display_palette(colors)
+    
+    # Create palette image
     palette_image = create_palette_image(colors)
     
+    # Combine English and Arabic captions
     bilingual_caption = f"English: {caption}\nArabic: {caption_arabic}"
     
     return bilingual_caption, ", ".join(color_palette), palette_image, resized_image_pil
 
-# Create Gradio Interface
+# Create Gradio Interface using Blocks and add a submit button
 with gr.Blocks(css=".gradio-container { height: 1000px !important; }") as demo:
     gr.Markdown("<h1 style='text-align: center;'>Palette Generator from Image with Image Captioning</h1>")
     gr.Markdown(
-        "<p style='text-align: center;'>Upload an image or select one of the example images below to generate a color palette and a description of the image in both English and Arabic.</p>"
+        """
+        <p style='text-align: center;'>
+        Upload an image or select one of the example images below to generate a color palette and a description of the image in both English and Arabic.
+        </p>
+        """
     )
     with gr.Row():
         with gr.Column(scale=1):
             image_input = gr.Image(type="pil", label="Upload your image or select an example below")
             submit_button = gr.Button("Submit")
-            gr.Examples(examples=examples, inputs=image_input, label="Example Images", examples_per_page=5)
+            gr.Examples(
+                examples=examples,
+                inputs=image_input,
+                label="Example Images",
+                examples_per_page=5,
+            )
         with gr.Column(scale=1):
             caption_output = gr.Textbox(label="Bilingual Caption", lines=5, max_lines=10)
             palette_hex_output = gr.Textbox(label="Color Palette Hex Codes", lines=2)
@@ -132,4 +188,3 @@ with gr.Blocks(css=".gradio-container { height: 1000px !important; }") as demo:
 
 # Launch Gradio Interface
 demo.launch()
-