Update app.py

app.py

@@ -1,7 +1,20 @@
 import streamlit as st
+from datasets import load_dataset
+
+# Existing Streamlit logic
+st.title('My Streamlit App')
+st.write('Welcome to my app!')
+
+# Load your dataset
+dataset = load_dataset('csv', data_files='CCI_Details_Structured_Full.csv')
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Streamlit widget for user input
+contributor_query = st.text_input('Enter Contributor to search:')
+
+# Run the query based on the widget input
+if contributor_query:
+    results = [example for example in dataset['train'] if example['Contributor'].lower() == contributor_query.lower()]
+    st.write(results)  # Display the results
 
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 from datasets import load_dataset
@@ -43,21 +56,3 @@ for example in dataset['train']:
     print(f"References: {context}")
     print(f"Answer: {answer}")
     print("---")
-
-import streamlit as st
-from datasets import load_dataset
-
-# Existing Streamlit logic
-st.title('My Streamlit App')
-st.write('Welcome to my app!')
-
-# Load your dataset
-dataset = load_dataset('csv', data_files='CCI_Details_Structured_Full.csv')
-
-# Streamlit widget for user input
-contributor_query = st.text_input('Enter Contributor to search:')
-
-# Run the query based on the widget input
-if contributor_query:
-    results = [example for example in dataset['train'] if example['Contributor'].lower() == contributor_query.lower()]
-    st.write(results)  # Display the results