Upload app.py

app.py

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+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from scipy.spatial.distance import cdist
+import gradio as gr
+
+
+df=pd.read_csv("english_idioms.csv")
+meaning=list(df.meaning)
+idioms= list(df.idioms)
+
+model = SentenceTransformer("all-mpnet-base-v2")
+idiom_meaning_embeddings=vectors =  np.load("vectors.npy")
+
+def get_best(query):
+  query_embedding = model.encode([query])
+  distances = cdist(query_embedding, idiom_meaning_embeddings, "cosine")[0]
+  ind = np.argsort(distances, axis=0)
+  return idioms[ind[0]],  distances[ind[0]], meaning[ind[0]]
+
+gr.Interface(fn=get_best, inputs=[gr.Text(label="Enter a descriptive sentence for the idiom you're looking for",placeholder="I feel sick!"  )], outputs=[gr.Text(label="Idiom"),gr.Number(label="Distance Score"), gr.Text(label="Idiom Explanation")]).launch()
+