basic sentiment analysis demo, with defaults

src/streamlit_app.py

@@ -1,32 +1,30 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+
 import streamlit as st
+from transformers import pipeline
+
+st.title("Sentiment Analysis")
+
+@st.cache_resource
+def load_pipe():
+    return pipeline("sentiment-analysis")
 
 
+sentiment_analysis_classifier = load_pipe()
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+user_input = st.text_area("Enter text to analyze:")
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+if st.button("Analyze"):
+    if user_input:
+        
+        result = sentiment_analysis_classifier(user_input)[0]
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        label = result['label']
+        score = result['score']
+        
+        st.write(f"**Sentiment:** {label}")
+        st.progress(score)
+        st.write(f"Confidence: {100*score:.2f}%")
+    else:
+        st.warning("Please enter some text.")