| import numpy as np | |
| import gradio as gr | |
| import os | |
| import pandas as pd | |
| from datasets import load_dataset | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| import plotly.express as px | |
| Secret_token = os.getenv('token') | |
| dataset = load_dataset("FDSRashid/embed_matn") | |
| df = dataset["train"].to_pandas() | |
| taraf_max = np.max(df['taraf_ID'].unique()) | |
| def plot_similarity_score(taraf_num): | |
| embed_taraf = df[df['taraf_ID']== taraf_num]['embed'].to_list() | |
| cos_score = cosine_similarity(embed_taraf) | |
| fig = px.imshow(cos_score) | |
| fig.write_html("test1.html") | |
| return 'test1.html' | |
| with gr.Blocks() as demo: | |
| taraf_number = gr.Slider(1,taraf_max , value=10000, label="Taraf", info="Choose the Taraf to Input", step = 1) | |
| btn = gr.Button('Submit') | |
| btn.click(fn = plot_similarity_score, inputs = [taraf_number], outputs = [gr.HTML()]) | |
| demo.launch() | |