Create app.py

app.py

@@ -0,0 +1,41 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+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)}")
+            
+            # create a dataframe to hold the sentiment distribution
+            df_sentiments = pd.DataFrame({"sentiment": ["Positive", "Negative", "Neutral"], "count": [0, 0, 0]})
+            
+            # get the sentiment distribution of the text using the selected model
+            results = sentiment_classifier(text_input, task="sentiment-analysis")
+            for r in results:
+                if r["label"] == "POSITIVE":
+                    df_sentiments.loc[0, "count"] = r["score"]
+                elif r["label"] == "NEGATIVE":
+                    df_sentiments.loc[1, "count"] = r["score"]
+                elif r["label"] == "NEUTRAL":
+                    df_sentiments.loc[2, "count"] = r["score"]
+            
+            # plot the sentiment distribution as a pie chart
+            fig = px.pie(df_sentiments, values='count', names='sentiment', hole=.4)
+            st.plotly_chart(fig)
+            
+        else:
+            st.warning("Please enter some text for sentiment analysis")
+
+if __name__ == "__main__":
+    main()