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import pandas as pd
import numpy as np
from tqdm import tqdm
from copy import deepcopy
import torch
import json
from numpy.linalg import norm
import gradio as gr
from sentence_transformers import SentenceTransformer
# necessary function
def cosinesimilarity(vector1, vector2):
cosine = np.dot(vector1, vector2)/(norm(vector1)*norm(vector2))
return cosine
def encode_input_and_return_top_n(input_in, db_dff, top_k, new2oldmatching):
embed1 = model.encode(input_in)
scores = []
db_df_in = deepcopy(db_dff)
db_in = list(set(db_df_in['Câu lệnh có sẵn'].tolist()))
for i, func in enumerate(db_in):
embed2 = db_df_in['Embedding'].loc[i]
scores.append(round(cosinesimilarity(embed1, embed2), 3))
db_df_in["Điểm"] = scores
db_df_in.sort_values(by=['Điểm'], inplace=True, ascending=False)
ids = db_df_in[:top_k].index.tolist()
output = {new2oldmatching[db_df_in['Câu lệnh có sẵn'][i].strip()]: round(db_df_in['Điểm'][i].item(), 2) for i in ids}
return output
def image_classifier(Input):
inputt = Input.lower()
result = encode_input_and_return_top_n(inputt, db_df, 3, new2oldmatch)
return result
def encode_database(db_in):
df = pd.DataFrame(list(zip(db_in, [[]]*len(db_in))), columns=["Câu lệnh có sẵn", "Embedding"])
for i, func in tqdm(enumerate(db_in)):
embedding2 = model.encode(func)
df['Embedding'].loc[i] = embedding2
else:
print()
print("Encode database successfully")
return df
model = SentenceTransformer("Huy1432884/function_retrieval")
model.eval()
with open('new2oldmatch.json', 'r') as openfile:
new2oldmatch = json.load(openfile)
new2oldmatch = {u.strip().lower(): v.strip() for u, v in new2oldmatch.items()}
database = [cmd.lower() for cmd in new2oldmatch.keys()]
db_df = encode_database(database)
demo = gr.Interface(fn=image_classifier, inputs="text", outputs="label")
demo.launch() |