Add initial demo files

app.py

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+import os
+import numpy as np
+import torch
+from PIL import Image
+import open_clip
+import gradio as gr
+import pickle
+
+# Load pre-trained model
+model, _, tokenizer = open_clip.create_model_and_transforms('ViT-L-14', pretrained='openai')
+
+# Load features
+def load_features(pickle_file):
+    with open(pickle_file, 'rb') as f:
+        data = pickle.load(f)
+    return data
+
+# Calculate similarity
+def calculate_similarity(image_features, text_feature, lambda_val=0.5):
+    image_similarities = image_features @ text_feature.T
+    text_similarities = text_feature @ text_feature.T
+    combined_similarities = (1 - lambda_val) * image_similarities + lambda_val * text_similarities
+    return combined_similarities
+
+# Load precomputed features
+features = load_features('features/patternnet_clip.pkl')
+image_features = torch.tensor(features['feats']).cuda()
+image_paths = features['paths']
+
+def image_text_retrieval(image, text, lambda_val):
+    # Preprocess image
+    preprocess = open_clip.get_preprocess('ViT-L-14')
+    image = preprocess(image).unsqueeze(0).cuda()
+
+    # Encode image and text
+    image_feature = model.encode_image(image).cpu()
+    text_feature = model.encode_text(tokenizer(text).unsqueeze(0).cuda()).cpu()
+
+    # Calculate combined similarities
+    similarities = calculate_similarity(image_features, text_feature, lambda_val)
+    top_indices = similarities.topk(5).indices.squeeze().tolist()
+
+    # Retrieve top images
+    top_images = [Image.open(image_paths[i]) for i in top_indices]
+    return top_images
+
+# Create Gradio interface
+def demo(image, text, lambda_val):
+    return image_text_retrieval(image, text, lambda_val)
+
+iface = gr.Interface(
+    fn=demo,
+    inputs=[
+        gr.inputs.Image(type="pil", label="Query Image"),
+        gr.inputs.Textbox(lines=2, placeholder="Enter text query...", label="Text Query"),
+        gr.inputs.Slider(minimum=0, maximum=1, default=0.5, label="Lambda Value (Image-Text Weight)")
+    ],
+    outputs=[gr.outputs.Gallery(label="Retrieved Images")],
+    title="Composed Image Retrieval for Remote Sensing",
+    description="Upload a query image, enter a text query, and adjust the lambda value to retrieve images based on both image and text inputs."
+)
+
+iface.launch()

features/patternnet_clip.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a21d512ab9fd037ac31f1752948eee34c51a31e57a2bffe7e3d253e861ce3b7f
+size 96401525