Update app.py

app.py

@@ -1,144 +1,148 @@
-import os
-import numpy as np
-import pandas as pd
-import gradio as gr
-from glob import glob
-import tensorflow as tf
-from annoy import AnnoyIndex
-from tensorflow import keras
-
-
-def load_image(image_path):
-    image = tf.io.read_file(image_path)
-    image = tf.image.decode_jpeg(image, channels=3)
-    image = tf.image.resize(image, (224, 224))
-    image = tf.image.convert_image_dtype(image, tf.float32)
-    image = image/255.
-    return image.numpy()
-
-
-# Specify Database Path
-database_path = './LandscapeSubset'
-
-# Create Example Images
-class_names = []
-with open('./Landscape-ClassNames.txt', mode='r') as names:
-    class_names = names.read().split(',')[:-1]
-
-example_image_paths = [
-    glob(os.path.join(database_path, name, '*'))[0]
-    for name in class_names
-]
-example_images = [load_image(path) for path in example_image_paths]
-
-# Load Feature Extractor
-feature_extractor_path = './Landscape-FeatureExtractor.keras'
-feature_extractor = keras.models.load_model(
-    feature_extractor_path, compile=False)
-
-# Load Annoy index
-index_path = './LandscapeSubset.ann'
-annoy_index = AnnoyIndex(256, 'angular')
-annoy_index.load(index_path)
-
-
-def similarity_search(
-    query_image, num_images=5, *_,
-    feature_extractor=feature_extractor,
-    annoy_index=annoy_index,
-    database_path=database_path,
-    metadata_path='./Landscapes.csv'
-):
-
-    if np.max(query_image) == 255:
-        query_image = query_image/255.
-
-    query_vector = feature_extractor.predict(
-        query_image[np.newaxis, ...], verbose=0)[0]
-
-    # Compute nearest neighbors
-    nearest_neighbors = annoy_index.get_nns_by_vector(query_vector, num_images)
-
-    # Load metadata
-    metadata = pd.read_csv(metadata_path, index_col=0)
-    metadata = metadata.iloc[nearest_neighbors]
-    closest_class = metadata.class_name.values[0]
-
-    # Similar Images
-    similar_images = [
-        load_image(os.path.join(database_path, class_name, file_name))
-        for class_name, file_name in zip(metadata.class_name.values, metadata.file_name.values)
-    ]
-
-    image_gallery = gr.Gallery(
-        value=similar_images,
-        label='Similar Images',
-        object_fit='fill',
-        preview=True,
-        visible=True,
-        height='50vh'
-    )
-    return closest_class, image_gallery
-
-
-# Gradio Application
-with gr.Blocks(theme='soft') as app:
-
-    gr.Markdown("# Landscape - Content Based Image Retrieval (CBIR)")
-    gr.Markdown(
-        f"Model only supports: {', '.join(class_names[:-1])} and {class_names[-1]}")
-    gr.Markdown(
-        "Disclaimer:- Model might suggest incorrect images, try using a different image.")
-
-    with gr.Row(equal_height=True):
-        # Image Input
-        query_image = gr.Image(
-            label='Query Image',
-            sources=['upload', 'clipboard'],
-            height='50vh'
-        )
-
-        # Output Gallery Display
-        output_gallery = gr.Gallery(visible=False)
-
-    with gr.Row(equal_height=True):
-
-        # Predicted Class
-        pred_class = gr.Textbox(
-            label='Predicted Class', placeholder='Let the model think!!...')
-
-        # Number of images to search
-        n_images = gr.Slider(
-            value=10,
-            label='Number of images to search',
-            minimum=1,
-            maximum=99,
-            step=1
-        )
-
-        # Search Button
-        search_btn = gr.Button('Search')
-
-    # Example Images
-    examples = gr.Examples(
-        examples=example_images,
-        inputs=query_image,
-        label='Something similar to me??',
-    )
-
-    # Input - On Change
-    query_image.change(
-        fn=similarity_search,
-        inputs=[query_image, n_images],
-        outputs=[pred_class, output_gallery]
-    )
-
-    # Search - On Click
-    search_btn.click(
-        fn=similarity_search,
-        inputs=[query_image, n_images],
-        outputs=[pred_class, output_gallery]
-    )
-
-if __name__ == '__main__':
-    app.launch()
+import os
+import numpy as np
+import pandas as pd
+import gradio as gr
+from glob import glob
+import tensorflow as tf
+from annoy import AnnoyIndex
+from tensorflow import keras
+
+
+def load_image(image_path):
+    image = tf.io.read_file(image_path)
+    image = tf.image.decode_jpeg(image, channels=3)
+    image = tf.image.resize(image, (224, 224))
+    image = tf.image.convert_image_dtype(image, tf.float32)
+    image = image/255.
+    return image.numpy()
+
+
+# Specify Database Path
+database_path = './LandscapeSubset'
+
+# Create Example Images
+class_names = []
+with open('./Landscape-ClassNames.txt', mode='r') as names:
+    class_names = names.read().split(',')[:-1]
+
+example_image_paths = [
+    glob(os.path.join(database_path, name, '*'))[0]
+    for name in class_names
+]
+example_images = [load_image(path) for path in example_image_paths]
+
+# Load Feature Extractor
+feature_extractor_path = './Landscape-FeatureExtractor.keras'
+feature_extractor = keras.models.load_model(
+    feature_extractor_path, compile=False)
+
+# Load Annoy index
+index_path = './LandscapeSubset.ann'
+annoy_index = AnnoyIndex(256, 'angular')
+annoy_index.load(index_path)
+
+
+def similarity_search(
+    query_image, num_images=5, *_,
+    feature_extractor=feature_extractor,
+    annoy_index=annoy_index,
+    database_path=database_path,
+    metadata_path='./Landscapes.csv'
+):
+
+    if np.max(query_image) == 255:
+        query_image = query_image/255.
+
+    query_vector = feature_extractor.predict(
+        query_image[np.newaxis, ...], verbose=0)[0]
+
+    # Compute nearest neighbors
+    nearest_neighbors = annoy_index.get_nns_by_vector(query_vector, num_images)
+
+    # Load metadata
+    metadata = pd.read_csv(metadata_path, index_col=0)
+    metadata = metadata.iloc[nearest_neighbors]
+    closest_class = metadata.class_name.values[0]
+
+    # Similar Images
+    similar_images_paths = [
+        os.path.join(database_path, class_name, file_name)
+        for class_name, file_name in zip(metadata.class_name.values, metadata.file_name.values)
+    ]
+    similar_images = [load_image(img) for img in similar_images_paths]
+
+    image_gallery = gr.Gallery(
+        value=similar_images,
+        label='Similar Images',
+        object_fit='fill',
+        preview=True,
+        visible=True,
+        height='50vh'
+    )
+    return closest_class, image_gallery, similar_images_paths
+
+
+# Gradio Application
+with gr.Blocks(theme='soft') as app:
+
+    gr.Markdown("# Landscape - Content Based Image Retrieval (CBIR)")
+    gr.Markdown(
+        f"Model only supports: {', '.join(class_names[:-1])} and {class_names[-1]}")
+    gr.Markdown(
+        "Disclaimer:- Model might suggest incorrect images, try using a different image.")
+
+    with gr.Row(equal_height=True):
+        # Image Input
+        query_image = gr.Image(
+            label='Query Image',
+            sources=['upload', 'clipboard'],
+            height='50vh'
+        )
+
+        # Output Gallery Display
+        output_gallery = gr.Gallery(visible=False)
+
+    # Hidden output for similar images paths
+    similar_paths_output = gr.Textbox(visible=False)
+
+    with gr.Row(equal_height=True):
+
+        # Predicted Class
+        pred_class = gr.Textbox(
+            label='Predicted Class', placeholder='Let the model think!!...')
+
+        # Number of images to search
+        n_images = gr.Slider(
+            value=10,
+            label='Number of images to search',
+            minimum=1,
+            maximum=99,
+            step=1
+        )
+
+        # Search Button
+        search_btn = gr.Button('Search')
+
+    # Example Images
+    examples = gr.Examples(
+        examples=example_images,
+        inputs=query_image,
+        label='Something similar to me??',
+    )
+
+    # Input - On Change
+    query_image.change(
+        fn=similarity_search,
+        inputs=[query_image, n_images],
+        outputs=[pred_class, output_gallery, similar_paths_output]
+    )
+
+    # Search - On Click
+    search_btn.click(
+        fn=similarity_search,
+        inputs=[query_image, n_images],
+        outputs=[pred_class, output_gallery, similar_paths_output]
+    )
+
+if __name__ == '__main__':
+    app.launch()