chore: init

app.py

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+import gradio as gr
+import torch
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import semantic_search
+
+title_dataset = load_dataset("pyimagesearch/blog-title", data_files="bp-title.csv")
+
+title_embeddings = load_dataset("pyimagesearch/blog-title", data_files="embeddings.csv")
+title_embeddings = torch.from_numpy(title_embeddings["train"].to_pandas().to_numpy()).to(torch.float)
+
+model = SentenceTransformer("paraphrase-MiniLM-L6-v2")
+
+title="Title Semantic Search"
+description="Provide a blog post title, and we'll find the most similar titles from our already written blog posts."
+
+examples=[
+    "Introduction to Keras",
+    "Conditional GANs with Keras",
+    "A Gentle Introduction to PyTorch with Deep Learning",
+]
+
+def get_titles(query):
+    query_embed = model.encode(query)
+    hits = semantic_search(query_embed, title_embeddings, top_k=5)[0]
+    
+    titles = list()
+
+    for hit in hits:
+        index = hit["corpus_id"]
+        selected_title = title_dataset["train"]["title"][index]
+        # score = hit["score"]
+        titles.append(selected_title)
+    return "\n".join(titles)
+
+space = gr.Interface(
+    fn=get_titles,
+    inputs=gr.Textbox(label="Input Title"),
+    outputs=gr.Textbox(label="Similar Titles"),
+    title=title,
+    description=description,
+    examples=examples,
+)
+
+space.launch()

requirements.txt

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+sentence-transformers
+datasets
+torch