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# Import necessary libraries
import gradio as gr
import numpy as np
import pandas as pd
from rapidfuzz.distance import Levenshtein, JaroWinkler
from sentence_transformers import SentenceTransformer, util
from typing import List
import zipfile
import os
import io
from gradio_huggingfacehub_search import HuggingfaceHubSearch
from matheel.similarity import get_sim_list, calculate_similarity
def calculate_similarity_gradio(code1, code2, Ws, Wl, Wj, model_name):
result = calculate_similarity(code1, code2, Ws, Wl, Wj, model_name)
return "The similarity score between the two codes is: %.2f" % result
def get_sim_list_gradio(zipped_file,Ws, Wl, Wj, model_name,threshold,number_results):
result = get_sim_list(zipped_file,Ws, Wl, Wj, model_name,threshold,number_results)
return result
# Define the Gradio app
with gr.Blocks() as demo:
# Tab for similarity calculation
with gr.Tab("Code Pair Similarity"):
# Input components
code1 = gr.Textbox(label="Code 1")
code2 = gr.Textbox(label="Code 2")
model_dropdown = HuggingfaceHubSearch(
label="Pre-Trained Model to use for Embeddings",
placeholder="Search for Pre-Trained models on Hugging Face",
search_type="model",
)
# Accordion for weights and models
with gr.Accordion("Weights and Models", open=False):
Ws = gr.Slider(0, 1, value=0.7, label="Semantic Search Weight", step=0.1)
Wl = gr.Slider(0, 1, value=0.3, label="Levenshiern Distance Weight", step=0.1)
Wj = gr.Slider(0, 1, value=0.0, label="Jaro Winkler Weight", step=0.1)
# Output component
output = gr.Textbox(label="Similarity Score")
def update_weights(Ws, Wl, Wj):
total = Ws + Wl + Wj
if total != 1:
Wj = 1 - (Ws + Wl)
return Ws, Wl, Wj
# Update weights when any slider changes
Ws.change(update_weights, [Ws, Wl, Wj], [Ws, Wl, Wj])
Wl.change(update_weights, [Ws, Wl, Wj], [Ws, Wl, Wj])
Wj.change(update_weights, [Ws, Wl, Wj], [Ws, Wl, Wj])
# Button to trigger the similarity calculation
calculate_btn = gr.Button("Calculate Similarity")
calculate_btn.click(calculate_similarity_gradio, inputs=[code1, code2, Ws, Wl, Wj, model_dropdown], outputs=output)
# Tab for file upload and DataFrame output
with gr.Tab("Code Collection Pair Similarity"):
# File uploader component
file_uploader = gr.File(label="Upload a Zip file",file_types=[".zip"])
model_dropdown = HuggingfaceHubSearch(
label="Pre-Trained Model to use for Embeddings",
placeholder="Search for Pre-Trained models on Hugging Face",
search_type="model",
)
with gr.Accordion("Weights and Models", open=False):
Ws = gr.Slider(0, 1, value=0.7, label="Semantic Search Weight", step=0.1)
Wl = gr.Slider(0, 1, value=0.3, label="Levenshiern Distance Weight", step=0.1)
Wj = gr.Slider(0, 1, value=0.0, label="Jaro Winkler Weight", step=0.1)
threshold = gr.Slider(0, 1, value=0, label="Threshold", step=0.01)
number_results = gr.Slider(1, 1000, value=10, label="Number of Returned pairs", step=1)
# Output component for the DataFrame
df_output = gr.Dataframe(label="Results")
# Button to trigger the file processing
process_btn = gr.Button("Process File")
process_btn.click(get_sim_list, inputs=[file_uploader, Ws, Wl, Wj, model_dropdown,threshold,number_results], outputs=df_output)
# Launch the Gradio app with live=True
demo.launch(show_error=True,debug=True) |