app modified to pages

Hello.py

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+import streamlit as st
+
+st.set_page_config(
+    page_title="Zero-Shot Classification",
+    page_icon="👋",
+)
+
+st.write("# Welcome to Zero-Shot Classification! 👋")
+
+st.sidebar.success("Select a demo above.")
+
+st.markdown(
+    """
+Zero-shot text classification is a task in natural language processing where a model is trained on a 
+set of labeled examples but is then able to classify new examples from previously unseen classes.
+"""
+)
+st.image('capture.png')

pages/2_Batch_classification.py

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+import pandas as pd
+import streamlit as st
+from transformers import pipeline
+
+st.title("Zeroshot Classification - Batched")
+st.caption("A streamlit powered app")
+
+uploaded_file = st.file_uploader("Upload your file here")
+if uploaded_file is not None:
+    # Can be used wherever a "file-like" object is accepted:
+    df = pd.read_excel(uploaded_file)
+    st.write(df)
+col_name = st.text_input("Which column contains text that you want to classify")
+
+classes = st.text_input("Enter possible class names (comma-separated)")
+
+pipe = pipeline('zero-shot-classification','facebook/bart-large-mnli')
+batch_output = pipe(list(df[col_name]),classes)
+df['predicted']=[i['labels'][0] for i in batch_output]
+
+st.write(df)
+
+@st.cache_data
+def convert_df(df):
+    # IMPORTANT: Cache the conversion to prevent computation on every rerun
+    return df.to_csv().encode("utf-8")
+
+csv = convert_df(df)
+
+st.download_button(
+    label="Download data as CSV",
+    data=csv,
+    file_name="large_df.csv",
+    mime="text/csv",
+)
+# with st.form('upload-form', clear_on_submit=True):
+#     uploaded_file = st.file_uploader("Upload your file here")
+#     submitted = st.form_submit_button("Upload")