Update app.py

app.py

@@ -4,7 +4,7 @@ import gradio as gr
 import os
 import pandas as pd
 from datasets import load_dataset
-from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.metrics.pairwise import cosine_similarity, pairwise_distance
 from datasets import Features, Value
 import plotly.express as px
 
@@ -16,7 +16,7 @@ Secret_token = os.getenv('HF_token')
 dataset = load_dataset("FDSRashid/embed_matn", token = Secret_token)
 books = load_dataset('FDSRashid/Hadith_info', data_files='Books.csv', token=Secret_token)['train'].to_pandas()
 df = dataset["train"].to_pandas()
-
+choice =  ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan', 'canberra', 'chebyshev']
 
 dataset = load_dataset("FDSRashid/hadith_info", data_files = 'All_Matns.csv',token = Secret_token, features = features)
 matn_info = dataset['train'].to_pandas()
@@ -39,12 +39,12 @@ joined_df = pd.merge(matn_info,df[cols_to_use],left_index=True, right_index=True
 df = joined_df.copy()
 taraf_max = np.max(df['taraf_ID'].unique())
 
-def plot_similarity_score(taraf_num):
+def plot_similarity_score(taraf_num, metr):
     taraf_df = df[df['taraf_ID']== taraf_num]
     taraf_df['Number'] = np.arange(len(taraf_df))
     embed_taraf = taraf_df['embed'].to_list()
-    cos_score = cosine_similarity(embed_taraf)
-    fig = px.imshow(cos_score)
+    cos_score = pairwise_distance(embed_taraf, metric = metr)
+    fig = px.imshow(cos_score, color_continuous_scale='plasma_r')
     matr = cos_score
     rows, cols = matr.shape
     mask = np.tril(np.ones((rows, cols), dtype=bool), k=-1)
@@ -55,6 +55,7 @@ def plot_similarity_score(taraf_num):
 
 with gr.Blocks() as demo: 
   taraf_number = gr.Slider(1,taraf_max , value=10000, label="Taraf", info="Choose the Taraf to Input", step = 1)
+  metric = gr.Dropdown(choices = choice, value = 'cosine', label = 'Variable to Display', info = 'Choose the variable to visualize.')
   btn = gr.Button('Submit')
   btn.click(fn = plot_similarity_score, inputs = [taraf_number], outputs = [gr.Plot(),gr.Plot(), gr.DataFrame()])
   demo.launch()