Create app.py

app.py

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+from transformers import AutoModelForTableQuestionAnswering, AutoTokenizer, pipeline
+
+# Use a pipeline as a high-level helper
+import streamlit as st
+import pandas as pd
+
+# Load model & tokenizer
+tapas_model = AutoModelForTableQuestionAnswering.from_pretrained('navteca/tapas-large-finetuned-wtq')
+tapas_tokenizer = AutoTokenizer.from_pretrained('navteca/tapas-large-finetuned-wtq')
+
+# Get predictions
+nlp = pipeline('table-question-answering', model=tapas_model, tokenizer=tapas_tokenizer)
+
+st.title('Query your data with text')
+file = st.file_uploader('Upload a csv file here')
+
+if file is not None:
+    query = st.text_input('Query')
+    data = pd.read_csv(file)
+
+  print(data)
+'''
+    st.subheader('Data')
+    st.table(data.head())
+    if query:
+        st.write(pipe(table=data, query=query))'''