Update app.py

app.py

@@ -1,17 +1,44 @@
+import pandas as pd
 import streamlit as st
+import altair as alt
 from transformers import pipeline
 
 def summarize_text(text):
-    unmasker = pipeline("text-classification", model = "roberta-large-mnli")
-    text_input = unmasker(text)
-    return text_input
+    unmasker = pipeline("sentiment-analysis", model="stevhliu/my_awesome_model")
+    sentiment_output = unmasker(text)[0]
+    sentiment_label = sentiment_output["label"]
+    sentiment_score = sentiment_output["score"]
+    return sentiment_label, sentiment_score
 
 def main():
     user_input = st.text_area("Input Text Here")
-    if st.button("Summarize"):
-        summary = summarize_text(user_input)
-        st.write("Summary:")
-        st.write(summary)
+    if st.button("Analyze Sentiment"):
+        sentiment_label, sentiment_score = summarize_text(user_input)
+        if sentiment_label == "LABEL_1":
+            sentiment_label = "Positive"
+            pos_score = sentiment_score
+            neg_score = 1 - sentiment_score
+        else:
+            sentiment_label = "Negative"
+            neg_score = sentiment_score
+            pos_score = 1 - sentiment_score
+
+        st.write(f"Sentiment Label: {sentiment_label}")
+        st.write(f"Positive Score: {pos_score:.2f}")
+        st.write(f"Negative Score: {neg_score:.2f}")
+
+        chart_data = pd.DataFrame({"Sentiment Score": [pos_score, neg_score]}, index=["Positive", "Negative"])
+
+        chart = alt.Chart(chart_data.reset_index()).mark_bar().encode(
+            x=alt.X('index:N', title=None),
+            y=alt.Y('Sentiment Score:Q', title="Sentiment Score"),
+            color=alt.Color('index:N', scale=alt.Scale(domain=['Positive', 'Negative'], range=['green', 'red']), legend=None)
+        ).properties(
+            width=300,
+            height=200
+        )
+
+        st.altair_chart(chart, use_container_width=True)
 
 if __name__ == "__main__":
-    main()
+    main()