Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,42 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-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
-
-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),
-})
-
-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])),
-    ))
+from transformers import pipeline
+
+# --- PAGE SETUP ---
+st.set_page_config(page_title="Hugging Face Sentiment AI", page_icon="🤗")
+
+# --- MODEL LOADING (Cached) ---
+@st.cache_resource
+def load_sentiment_model():
+    # This uses the default 'distilbert-base-uncased-finetuned-sst-2-english'
+    return pipeline("sentiment-analysis")
+
+classifier = load_sentiment_model()
+
+# --- UI ELEMENTS ---
+st.title("🤗 AI Sentiment Analyzer")
+st.write("This app uses a Hugging Face Transformer model to detect sentiment.")
+
+user_input = st.text_area("Enter text to analyze:", placeholder="I am so excited to build this app!")
+
+if st.button("Analyze Sentiment"):
+    if user_input.strip():
+        with st.spinner("Analyzing..."):
+            # Run prediction
+            results = classifier(user_input)
+            
+            # Extract data
+            label = results[0]['label']
+            score = results[0]['score']
+            
+            # --- DISPLAY RESULTS ---
+            st.divider()
+            if label == "POSITIVE":
+                st.success(f"### {label} 😊")
+            else:
+                st.error(f"### {label} 😡")
+                
+            st.metric(label="Confidence Score", value=f"{score:.2%}")
+    else:
+        st.warning("Please enter some text first!")
+
+st.caption("Running on Hugging Face Spaces with Streamlit")