Delete app.py

app.py

@@ -1,77 +0,0 @@
-import tensorflow.compat.v1 as tf
-import os 
-import shutil
-import csv
-import pandas as pd
-import numpy as np
-import IPython
-import streamlit as st
-import subprocess
-from itertools import islice
-import random
-from transformers import TapasTokenizer, TapasForQuestionAnswering
-tf.get_logger().setLevel('ERROR')
-model_name = 'google/tapas-base-finetuned-wtq'
-model = TapasForQuestionAnswering.from_pretrained(model_name, local_files_only=False)
-tokenizer = TapasTokenizer.from_pretrained(model_name)
-st.set_option('deprecation.showfileUploaderEncoding', False)
-st.title('Query your Table')
-st.header('Upload CSV file')
-uploaded_file = st.file_uploader("Choose your CSV file",type = 'csv')
-placeholder = st.empty()
-if uploaded_file is not None:
-    data = pd.read_csv(uploaded_file)
-    data.replace(',','', regex=True, inplace=True)
-    if st.checkbox('Want to see the data?'):
-        placeholder.dataframe(data)
-st.header('Enter your queries')
-input_queries = st.text_input('Type your queries separated by comma(,)',value='')
-input_queries = input_queries.split(',')
-colors1 = ["#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)]) for i in range(len(input_queries))]
-colors2 = ['background-color:'+str(color)+'; color: black' for color in colors1]
-def styling_specific_cell(x,tags,colors):
-    df_styler = pd.DataFrame('', index=x.index, columns=x.columns)
-    for idx,tag in enumerate(tags):
-        for r,c in tag:
-            df_styler.iloc[r, c] = colors[idx]
-    return df_styler
-    
-if st.button('Predict Answers'):
-    with st.spinner('It will take approx a minute'):
-        data = data.astype(str)
-        inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="pt")
-        outputs = model(**inputs)
-        predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions( inputs, outputs.logits.detach(), outputs.logits_aggregation.detach())
-        
-        id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
-        aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
-    
-        answers = []
-        
-        for coordinates in predicted_answer_coordinates:
-           if len(coordinates) == 1:
-             # only a single cell:
-             answers.append(table.iat[coordinates[0]])
-           else:
-             # multiple cells
-             cell_values = []
-             for coordinate in coordinates:
-                cell_values.append(table.iat[coordinate])
-             answers.append(", ".join(cell_values))
-             
-    st.success('Done! Please check below the answers and its cells highlighted in table above')
-    
-    placeholder.dataframe(data.style.apply(styling_specific_cell,tags=predicted_answer_coordinates,colors=colors2,axis=None))
-      
-    for query, answer, predicted_agg, c in zip(queries, answers, aggregation_predictions_string, colors1):
-        st.write('\n')
-        st.markdown('<font color={} size=4>**{}**</font>'.format(c,query), unsafe_allow_html=True)
-        st.write('\n')
-        
-        if predicted_agg == "NONE" or predicted_agg == 'COUNT':
-            st.markdown('**>** '+str(answer))
-        else:
-            if predicted_agg == 'SUM':
-                st.markdown('**>** '+str(sum(answer.split(','))))
-            else:
-                st.markdown('**>** '+str(np.round(np.mean(answer.split(',')),2)))