import tensorflow.compat.v1 as tf import os import shutil import csv import sys 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 pipeline from transformers import TapasTokenizer, TapasForQuestionAnswering tf.get_logger().setLevel('ERROR') model_name = 'google/tapas-large-finetuned-wtq' #model_name = "table-question-answering" #model = pipeline(model_name) 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'): table = data.astype(str) inputs = tokenizer(table=table , queries=input_queries, padding='max_length',truncation=True, return_tensors="pt") outputs = model(**inputs) #outputs = model(table = data, query = queries) 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(input_queries, answers, aggregation_predictions_string, colors1): st.write('\n') st.markdown('**{}**'.format(c,query), unsafe_allow_html=True) st.write('\n') if predicted_agg == "NONE" or predicted_agg == 'COUNT': st.markdown('**>** '+str(answer)) else: #st.write(predicted_agg) #st.write(answer) if predicted_agg == 'SUM': st.markdown('**>** '+str(sum(list(map(float,answer.split(',')))))) else: st.markdown('**>** '+str(np.round(np.mean(list(map(float,answer.split(',')))),2)))