update with model+data

app.py

@@ -0,0 +1,72 @@
+import faiss
+import gradio as gr
+import numpy as np
+import pandas as pd
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer, util
+
+
+def build_doc_frame(df, idx=0):
+    doc = df.iloc[0]
+    # as df:
+    doc_df = pd.DataFrame(doc).T
+    # keep only sentences + embedding:
+    doc_df = doc_df[["url", "sentences", "embedding"]]
+    # unpack the sentences and embedding in separate rows
+    doc_df = doc_df.explode(["sentences", "embedding"])
+    return doc_df
+
+
+def get_doc_embeddings(doc):
+    return np.array(doc.embedding.tolist(), dtype="float32")
+
+
+def faiss_search(doc_idx, query_str, K=5):
+    # doc_idx is a choice option of (idx, text)
+    idx = doc_idx[0] - 1
+    newdoc = build_doc_frame(df, idx=idx)
+    embeddings = get_doc_embeddings(newdoc)
+
+    faiss.normalize_L2(embeddings)
+    index = faiss.IndexFlatIP(768)
+    index.add(embeddings)
+
+    query_str = "Skade mellom kjøretøy"
+    target_emb = model.encode([query_str])
+    target_emb = np.array([target_emb.reshape(-1)])
+    faiss.normalize_L2(target_emb)
+
+    D, I = index.search(np.array(target_emb), K)
+    print(list(zip(D[0], I[0])))
+
+    # prettyprint the results:
+    pretty_results = []
+    for idx, score in zip(I[0], D[0]):
+        pretty_results.append((round(float(score), 3), newdoc.iloc[idx].sentences))
+    pretty_results_str = "\n".join([f"{score}\t{sent}" for score, sent in pretty_results])
+    top_k_str = f"Top {K} results for: {query_str}"
+    underlines = "__" * 40
+
+    # return str:
+    return f"{top_k_str}\n{pretty_results_str}\n{underlines}"
+
+
+dataset = load_dataset("tollefj/rettsavgjoerelser_100samples_embeddings")
+model = SentenceTransformer("NbAiLab/nb-sbert-base")
+df = dataset["train"].to_pandas()
+
+dropdown_opts = [(idx + 1, f"\t{doc.summary[0][:60]}...") for idx, doc in df.iterrows()]
+
+iface = gr.Interface(
+    fn=faiss_search,
+    inputs=[
+        gr.Dropdown(label="Select a court case", choices=dropdown_opts),
+        gr.Textbox(lines=2, placeholder="Your query here..."),
+        gr.Slider(minimum=1, maximum=10, label="Number of matches", value=5),
+    ],
+    outputs="text",
+    title="Lovdata rettsavgjørelser - semantisk søk",
+    description="Velg en rettsak og søk for å hente ut lignende setninger i saken",
+)
+
+iface.launch()