Update app.py

app.py

@@ -1,4 +1,5 @@
 import streamlit as st
+import pandas as pd
 from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering
 
 @st.cache(allow_output_mutation=True)
@@ -11,15 +12,34 @@ def load_qa_model():
 
 qa = load_qa_model()
 
-st.title("Ask Questions about your Text")
-sentence = st.text_area('Please paste your article :', height=30)
-question = st.text_input("Questions from this article?")
-button = st.button("Get me Answers")
-max_seq_length = st.sidebar.slider('Select max sequence length', 50, 500, step=10, value=150)
-do_sample = st.sidebar.checkbox("Do sample", value=False)
+def process_batch(data):
+    results = []
+    for index, row in data.iterrows():
+        answer = qa(question=row['Question'], context=row['Article'])
+        results.append({
+            'Question': row['Question'],
+            'Article': row['Article'],
+            'Answer': answer['answer'],
+            'Score': answer['score']
+        })
+    return results
 
-with st.spinner("Discovering Answers.."):
-    if button and sentence:
-        answers = qa(question=question, context=sentence)
-        st.write("Answer:", answers['answer'])
-        st.write("Score:", answers['score'])
+st.title("Batch Question Answering App")
+
+uploaded_file = st.file_uploader("Upload a CSV file", type="csv")
+
+if uploaded_file is not None:
+    data = pd.read_csv(uploaded_file)
+    st.write("Uploaded file:")
+    st.write(data)
+
+    if st.button("Process Batch"):
+        with st.spinner("Processing Batch..."):
+            results = process_batch(data)
+            st.write("Batch Processing Results:")
+            for result in results:
+                st.write("Question:", result['Question'])
+                st.write("Article:", result['Article'])
+                st.write("Answer:", result['Answer'])
+                st.write("Score:", result['Score'])
+                st.write("------")