Create app.py

app.py

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+import clip
+import numpy as np
+import torch
+import gradio as gr
+
+# Load the CLIP model
+model, preprocess = clip.load("ViT-B/32")
+device = "cuda" if torch.cuda.is_available() else "cpu"  # Check for GPU availability
+model.to(device).eval()
+
+# Define the Business Listing variable
+Business_Listing = "Air Guide"
+
+def find_similar_images(text_input):
+    # Directory where you want to load images
+    image_dir = "/content/sample_data/Tourism"
+    
+    # Create an empty description dictionary
+    description = f"{Business_Listing} Logo"
+    
+    # Set up the layout for displaying images
+    num_rows = 4
+    num_cols = 8
+    
+    original_images = []
+    images = []
+    texts = []
+    
+    # Load and preprocess images
+    image_extensions = ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', '.ico', '.svg', '.eps', '.pdf']
+    for filename in [filename for filename in os.listdir(image_dir) if any(filename.endswith(ext) for ext in image_extensions)]:
+        # Get the image name (without extension)
+        image_name, _ = os.path.splitext(filename)
+    
+        # Load the image
+        image = Image.open(os.path.join(image_dir, filename)).convert("RGB")
+        original_images.append(image)
+        images.append(preprocess(image))
+        texts.append(description)
+    
+    # Prepare input text and images
+    image_input = torch.tensor(np.stack(images)).to(device)
+    text_tokens = clip.tokenize([f"This is {text_input}"])
+    text_tokens = text_tokens.to(device)
+    
+    # Encode text and image features
+    with torch.no_grad():
+        image_features = model.encode_image(image_input).float()
+        text_features = model.encode_text(text_tokens).float()
+    
+    # Normalize features and calculate similarity
+    image_features /= image_features.norm(dim=-1, keepdim=True)
+    text_features /= text_features.norm(dim=-1, keepdim=True)
+    similarity = text_features.cpu().numpy() @ image_features.cpu().numpy().T
+    
+    # Find the maximum similarity value
+    max_similarity_value = similarity[0, :].max()
+    
+    # Find all indices with the maximum similarity value
+    max_similarity_indices = np.where(similarity[0, :] == max_similarity_value)
+    
+    # Get the filenames with the highest similarity
+    valid_extensions = ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp', '.ico', '.svg', '.eps', '.pdf')
+    image_files = [filename for filename in os.listdir(image_dir) if filename.endswith(valid_extensions)]
+    filenames_with_highest_similarity = [image_files[i] for i in max_similarity_indices[0]]
+    
+    return filenames_with_highest_similarity, max_similarity_value
+
+# Define a Gradio interface
+iface = gr.Interface(
+    fn=find_similar_images,
+    inputs="text",
+    outputs=["text", "number"],
+    live=True,
+    interpretation="default",
+    title="CLIP Model Image Search",
+)
+
+iface.launch()