First Commit

app.py

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+import os
+import requests
+from tqdm import tqdm
+from datasets import load_dataset
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
+from tensorflow.keras.preprocessing import image
+from sklearn.neighbors import NearestNeighbors
+import joblib
+from PIL import UnidentifiedImageError, Image
+import gradio as gr
+import matplotlib.pyplot as plt
+
+# Ensure TensorFlow uses GPU
+print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
+assert len(tf.config.list_physical_devices('GPU')) > 0, "No GPU available!"
+
+# Load the dataset
+dataset = load_dataset("thefcraft/civitai-stable-diffusion-337k")
+
+# Take a subset of the dataset
+subset_size = 10000
+dataset_subset = dataset['train'].shuffle(seed=42).select(range(subset_size))
+
+# Directory to save images
+image_dir = 'civitai_images'
+os.makedirs(image_dir, exist_ok=True)
+
+# Load the ResNet50 model pretrained on ImageNet
+with tf.device('/GPU:0'):
+    model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
+
+# Function to extract features
+def extract_features(img_path, model):
+    img = image.load_img(img_path, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+    with tf.device('/GPU:0'):
+        features = model.predict(img_array)
+    return features.flatten()
+
+# Extract features for a sample of images
+features = []
+image_paths = []
+model_names = []
+
+for sample in tqdm(dataset_subset):
+    img_url = sample['url']  # Adjust based on the correct column name
+    model_name = sample['Model']  # Adjust based on the correct column name
+    img_path = os.path.join(image_dir, os.path.basename(img_url))
+
+    # Download the image
+    try:
+        response = requests.get(img_url)
+        response.raise_for_status()  # Check if the download was successful
+
+        if 'image' not in response.headers['Content-Type']:
+            raise ValueError("URL does not contain an image")
+
+        with open(img_path, 'wb') as f:
+            f.write(response.content)
+
+        # Extract features
+        try:
+            img_features = extract_features(img_path, model)
+            features.append(img_features)
+            image_paths.append(img_path)
+            model_names.append(model_name)
+        except UnidentifiedImageError:
+            print(f"UnidentifiedImageError: Skipping file {img_path}")
+            os.remove(img_path)
+
+    except requests.exceptions.RequestException as e:
+        print(f"RequestException: Failed to download {img_url} - {e}")
+
+# Convert features to numpy array
+features = np.array(features)
+
+# Build the NearestNeighbors model
+nbrs = NearestNeighbors(n_neighbors=5, algorithm='ball_tree').fit(features)
+
+# Save the model and features
+joblib.dump(nbrs, 'nearest_neighbors_model.pkl')
+np.save('image_features.npy', features)
+np.save('image_paths.npy', image_paths)
+np.save('model_names.npy', model_names)
+
+# Load the NearestNeighbors model and features
+nbrs = joblib.load('nearest_neighbors_model.pkl')
+features = np.load('image_features.npy')
+image_paths = np.load('image_paths.npy', allow_pickle=True)
+model_names = np.load('model_names.npy', allow_pickle=True)
+
+# Function to get recommendations
+def get_recommendations(img_path, model, nbrs, image_paths, model_names, n_neighbors=5):
+    img_features = extract_features(img_path, model)
+    distances, indices = nbrs.kneighbors([img_features])
+
+    recommended_images = [image_paths[idx] for idx in indices.flatten()]
+    recommended_model_names = [model_names[idx] for idx in indices.flatten()]
+    recommended_distances = distances.flatten()
+
+    return recommended_images, recommended_model_names, recommended_distances
+
+def recommend(image):
+    # Save uploaded image to a path
+    image_path = "uploaded_image.jpg"
+    image.save(image_path)
+    
+    recommended_images, recommended_model_names, recommended_distances = get_recommendations(image_path, model, nbrs, image_paths, model_names)
+    result = list(zip(recommended_images, recommended_model_names, recommended_distances))
+    
+    # Display images with matplotlib
+    display_images(recommended_images, recommended_model_names, recommended_distances)
+    
+    return result
+
+def display_images(image_paths, model_names, distances):
+    plt.figure(figsize=(20, 10))
+    for i, (img_path, model_name, distance) in enumerate(zip(image_paths, model_names, distances)):
+        img = Image.open(img_path)
+        plt.subplot(1, len(image_paths), i+1)
+        plt.imshow(img)
+        plt.title(f'{model_name}\nDistance: {distance:.2f}', fontsize=12)
+        plt.axis('off')
+    plt.show()
+
+# Gradio interface
+interface = gr.Interface(
+    fn=recommend,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs="text",  # Outputs the list of recommended images, models, and distances
+    title="Image Recommendation System",
+    description="Upload an image and get 5 recommended similar images with model names and distances."
+)
+
+interface.launch()