Create app.py

app.py

@@ -0,0 +1,44 @@
+import streamlit as st
+import pandas as pd
+from transformers import pipeline
+
+st.set_page_config(page_title="Sentiment Analysis App")
+st.sidebar.title("Sentiment Analysis App")
+st.sidebar.write("Choose a pre-trained model for sentiment analysis")
+model_name = st.sidebar.selectbox("Select Model", ["bert-base-uncased", "distilbert-base-uncased", "roberta-base"])
+text_input = st.text_input("Enter text for sentiment analysis")
+
+if st.button("Analyze"):
+    if text_input:
+        sentiment_classifier = pipeline("sentiment-analysis", model=model_name)
+        result = sentiment_classifier(text_input)[0]
+        st.write(f"Sentiment: {result['label']}")
+        st.write(f"Score: {round(result['score'], 4)}")
+    else:
+        st.warning("Please enter some text for sentiment analysis")
+
+if __name__ == "__main__":
+    main()
+
+import streamlit as st
+import pandas as pd
+from transformers import pipeline
+
+def main():
+    st.set_page_config(page_title="Sentiment Analysis App")
+    st.sidebar.title("Sentiment Analysis App")
+    st.sidebar.write("Choose a pre-trained model for sentiment analysis")
+    model_name = st.sidebar.selectbox("Select Model", ["bert-base-uncased", "distilbert-base-uncased", "roberta-base"])
+    text_input = st.text_input("Enter text for sentiment analysis")
+
+    if st.button("Analyze"):
+        if text_input:
+            sentiment_classifier = pipeline("sentiment-analysis", model=model_name)
+            result = sentiment_classifier(text_input)[0]
+            st.write(f"Sentiment: {result['label']}")
+            st.write(f"Score: {round(result['score'], 4)}")
+        else:
+            st.warning("Please enter some text for sentiment analysis")
+
+if __name__ == "__main__":
+    main()