Create app.py

app.py

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+import ripple
+import streamlit as stl
+from tqdm.auto import tqdm
+
+# streamlit app
+stl.set_page_config(
+    page_title="Ripple",
+)
+
+stl.title("ripple search")
+stl.write(
+    "An app that uses text input to search for described images, using embeddings of selected image datasets. Uses contrastive learning models(CLIP) and the sentence transformers library"
+)
+stl.link_button(
+    label="link to github and full library code",
+    url="https://github.com/kelechi-c/ripple_net",
+)
+
+dataset = stl.selectbox(
+    "choose huggingface dataset(bgger datasets take more time to embed..)",
+    options=[
+        "huggan/wikiart(1k)",
+        "huggan/wikiart(11k)",
+        "zh-plus/tiny-imagenet(110k)",
+        "lambdalabs/naruto-blip-captions(1k)",
+        "detection-datasets/fashionpedia(45k)",
+    ],
+)
+# initalized global variables
+
+embedded_data = None
+embedder = None
+text_search = None
+
+ret_images = []
+scores = []
+
+
+if dataset and stl.button("embed image dataset"):
+    with stl.spinner("Initializing and creating image embeddings from dataset"):
+        embedder = ripple.ImageEmbedder(
+            dataset, retrieval_type="text-image", dataset_type="huggingface"
+        )
+
+        embedded_data = embedder.create_embeddings(device="cpu")
+        stl.success("Sucessfully embedded and dcreated image index")
+
+if embedded_data is not None:
+    text_search = ripple.TextSearch(embedded_data, embedder.embed_model)
+    stl.success("Initialized text search class")
+
+search_term = stl.text_input("Text description/search for image")
+
+if search_term:
+    with stl.spinner("retrieving images with description.."):
+        scores, ret_images = text_search.get_similar_images(
+            search_term, k_images=4)
+        stl.success(f"sucessfully retrieved {len(ret_images)}")
+
+for count, score, image in tqdm(zip(range(len(ret_images)), scores, ret_images)):
+    stl.image(image["image"][count])
+    stl.write(score)