Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""code-search.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1-TlihNx5XCiVSxUHDF1oHFNcfpuy_k0N
+"""
+
+# Install Cohere for embeddings
+
+import cohere
+import numpy as np
+import pandas as pd
+import gradio as gr
+
+from sklearn.metrics.pairwise import cosine_similarity
+from annoy import AnnoyIndex
+import warnings
+warnings.filterwarnings('ignore')
+pd.set_option('display.max_colwidth', None)
+
+data_df = pd.read_csv('functions_data.csv')
+#data_df.head()
+
+data_df['docstring'].fillna('not specified', inplace=True)
+
+# Paste your API key here. Remember to not share publicly
+api_key = '2IdvZuDAwqcpMuwN3yjAXBOHKAT1Mqxr4N8hZFKN'
+
+# Create and retrieve a Cohere API key from dashboard.cohere.ai/welcome/register
+co = cohere.Client(api_key)
+
+
+search_index = AnnoyIndex(4096, 'angular')
+search_index.load('code.ann') # super fast, will just mmap the file
+
+def get_code(query):
+    # Get the query's embedding
+    query_embed = co.embed(texts=[query],
+                    model="large",
+                    truncate="LEFT").embeddings
+
+    # Retrieve the nearest neighbors
+    similar_item_ids = search_index.get_nns_by_vector(query_embed[0],3,
+                                                    include_distances=True)
+    
+    return data_df.iloc[similar_item_ids[0]]['function_body'] , data_df.iloc[similar_item_ids[0]]['file_path']
+
+iface = gr.Interface(fn=get_code, inputs="text", outputs=[gr.Markdown(), "text"])
+iface.launch()
+
+
+