gradio app setup

app.py

@@ -0,0 +1,62 @@
+import logging
+
+import datasets
+from findkit import indexes
+
+
+import gradio as gr
+
+logging.basicConfig(level="INFO")
+
+
+def get_html_retrieval_results(retrieval_result, show_only_one_match_per_episode):
+    if show_only_one_match_per_episode:
+        retrieval_result = retrieval_result.drop_duplicates(subset=["episode"])
+    if len(retrieval_result) > 0:
+        retrieval_result_html = retrieval_result.to_html(render_links=True, index=False)
+        return retrieval_result_html
+    else:
+        return ""
+
+
+def get_retrieval_results(findkit_index, query, n_retrieved_results):
+    retrieval_results_df = findkit_index.find_similar(query, n_retrieved_results)
+    return retrieval_results_df.rename({"distance": "bm25_score"})
+
+
+def setup_df():
+    podcast_dataset = datasets.load_dataset("lambdaofgod/lex_fridman_podcast")["train"]
+    df = podcast_dataset.to_pandas()
+    return df.dropna()
+
+
+def setup_index():
+    df = setup_df()
+    return indexes.InMemoryBM25Index.build(df["text"], df)
+
+
+findkit_index = setup_index()
+
+
+def show_retrieval_results(query, n_retrieved_results, show_only_one_match_per_episode):
+    retrieval_results_df = get_retrieval_results(
+        findkit_index, query, n_retrieved_results
+    )
+    return get_html_retrieval_results(
+        retrieval_results_df, show_only_one_match_per_episode
+    )
+
+
+show_only_one_match_per_episode = gr.Checkbox(
+    label="show only one match per episode", value=False
+)
+n_retrieved_results = gr.Number(label="number of results", value=10, precision=0)
+query = gr.Textbox(label="input query", value="artificial life")
+
+demo = gr.Interface(
+    fn=show_retrieval_results,
+    inputs=[query, n_retrieved_results, show_only_one_match_per_episode],
+    outputs="html",
+)
+
+demo.launch()

requirements.txt

@@ -0,0 +1 @@
+git+https://github.com/lambdaofgod/findkit