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