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app.py

@@ -1,20 +1,23 @@
 import streamlit as st
 from transformers import pipeline
-from PIL import Image
+import pandas as pd
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+tqa = pipeline(task="table-question-answering", 
+                    model="google/tapas-base-finetuned-wikisql-supervised")
 
-st.title("Hot Dog? Or Not?")
+st.title("Table Question Answering")
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+file_name = st.file_uploader("Upload dataset",type=['csv','xlsx'])
 
 if file_name is not None:
-  col1, col2 = st.columns(2)
+  try:
+      df=pd.read_csv(file_name)
+  except:
+      df = pd.read_excel(file_name)
+  df = df.astype(str)
 
-  image = Image.open(file_name)
-  col1.image(image, use_column_width=True)
-  predictions = pipeline(image)
-
-  col2.header("Probabilities")
-  for p in predictions:
-    col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+  question = st.text_input('Type your question')
+  with st.spinner():
+    if(st.button('Answer')):
+      answer = tqa(table=df, query=question,truncation=True)
+      st.success(answer)

requirements.txt

@@ -1,2 +1,4 @@
 transformers
-torch
+torch
+pandas
+torch-scatter