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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()
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