Create app.py

app.py

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+import streamlit as st
+from st_aggrid import AgGrid
+import pandas as pd 
+# from PIL import Image
+from transformers import pipeline
+st.set_page_config(layout="wide")
+
+# im = Image.open("ai-favicon.png")
+# st.set_page_config(page_title="Table Summarization",
+#     page_icon=im,layout='wide') 
+
+
+style = '''
+    <style>
+        header {visibility: hidden;}
+        div.block-container {padding-top:4rem;}
+        section[data-testid="stSidebar"] div:first-child {
+        padding-top: 0;
+    }
+     .font {                                          
+    text-align:center;
+    font-family:sans-serif;font-size: 1.25rem;}
+    </style>
+'''
+st.markdown(style, unsafe_allow_html=True)
+
+st.markdown('<p style="font-family:sans-serif;font-size: 1.9rem;">Table Question Answering using TAPAS</p>', unsafe_allow_html=True)
+st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'>Pre-trained TAPAS model runs on max 64 rows and 32 columns data. Make sure the file data doesn't exceed these dimensions.</p>", unsafe_allow_html=True)
+
+tqa = pipeline(task="table-question-answering", 
+                    model="google/tapas-large-finetuned-wtq")
+
+
+# st.sidebar.image("ai-logo.png",width=200)
+# with open('data.csv', 'rb') as f:
+#         st.sidebar.download_button('Download sample data', f, file_name='Sample Data.csv')
+file_name = st.sidebar.file_uploader("Upload file:", type=['csv','xlsx'])
+
+if file_name is None:
+    st.markdown('<p class="font">Please upload an excel or csv file </p>', unsafe_allow_html=True)
+    # st.image("loader.png")
+
+else:
+    try:
+        df=pd.read_csv(file_name)
+    except:
+        df = pd.read_excel(file_name)
+         
+    grid_response = AgGrid(
+        df.head(5),
+        columns_auto_size_mode='FIT_CONTENTS',
+        editable=True, 
+        height=300, 
+        width='100%',
+        )
+
+    question = st.text_input('Type your question')
+    df = df.astype(str)
+    
+    with st.spinner():
+        if(st.button('Answer')):
+            answer = tqa(table=df, query=question,truncation=True)
+            st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Results </p>",unsafe_allow_html = True)
+            st.success(answer)