updated sentiment classification

app.py

@@ -1,9 +1,72 @@
 import pandas as pd
 import streamlit as st
+from sentiment import classify_sentiment
+
+st.set_page_config(  # Alternate names: setup_page, page, layout
+    layout="wide",  # Can be "centered" or "wide". In the future also "dashboard", etc.
+    initial_sidebar_state="auto",  # Can be "auto", "expanded", "collapsed"
+    page_title='None',  # String or None. Strings get appended with "• Streamlit".
+)
+# padding_top = 0
+# st.markdown(f"""
+#     <style>
+#         .reportview-container .main .block-container{{
+#             padding-top: {padding_top}rem;
+#         }}
+#     </style>""",
+#     unsafe_allow_html=True,
+# )
+
+def set_page_title(title):
+    st.sidebar.markdown(unsafe_allow_html=True, body=f"""
+        <iframe height=0 srcdoc="<script>
+            const title = window.parent.document.querySelector('title') \
+
+            const oldObserver = window.parent.titleObserver
+            if (oldObserver) {{
+                oldObserver.disconnect()
+            }} \
+
+            const newObserver = new MutationObserver(function(mutations) {{
+                const target = mutations[0].target
+                if (target.text !== '{title}') {{
+                    target.text = '{title}'
+                }}
+            }}) \
+
+            newObserver.observe(title, {{ childList: true }})
+            window.parent.titleObserver = newObserver \
+
+            title.text = '{title}'
+        </script>" />
+    """)
+
+
+set_page_title('NLP use cases')
+
+# Hide Menu Option
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
 
 st.title("NLP use cases")
 
 with st.sidebar:
     st.title("NLP tasks")
-    st.selectbox(label="Select task from drop down menu",
+    select_task=st.selectbox(label="Select task from drop down menu",
                  options=['Detect Sentiment','Zero Shot Classification'])
+
+
+if select_task=='Detect Sentiment':
+    st.header("You are now performing Sentiment Analysis")
+    input_texts = st.text_input(label="Input texts separated by comma")
+
+    if input_texts!='':
+        sentiments = classify_sentiment(input_texts)
+        for i,t in enumerate(input_texts.split(',')):
+            st.write("** " + t + f"--> This statement is {sentiments[i]}")