Create app.py

app.py

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+import gradio as gr
+
+import gensim
+
+model_g = gensim.models.KeyedVectors.load_word2vec_format('./v_glove_1024_2.0' , binary=True)
+
+#retrieve the most similar words
+
+def generate(text):
+    result= model_g.most_similar('together',topn=10)
+    return result
+
+
+examples = [
+    ["sad"],
+    ["together"],
+    ["lake"]
+]
+
+title = "Visually Grounded embeddings"
+description = 'Get the top 10 nearest neighbors from a visually grounded word embedding model described in [this paper](https://arxiv.org/abs/2206.08823).<br>'
+txt = gr.Textbox(lines=1, label="Query word", placeholder="muffin")
+out = gr.Textbox(lines=4, label="top 10 nearest neighbors")
+
+demo = gr.Interface(
+    fn =generate,
+    inputs=txt,
+    outputs=out,
+    examples=examples,
+    title=title,
+    description=description,
+    theme="default",
+    cache_examples="never"
+)
+
+demo.launch(enable_queue=True, debug=True)