Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,22 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
+from transformers import pipeline
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
+# Use local cache directory to avoid permission issues
+os.environ['TRANSFORMERS_CACHE'] = '/app/cache'
 
-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).
+# Create zero-shot classification pipeline
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+# Streamlit UI
+st.title("Zero-Shot Text Classifier")
+text = st.text_area("Enter text to classify")
+labels = st.text_input("Enter candidate labels (comma-separated)", "finance, education, health")
 
-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])),
-    ))
+if st.button("Classify"):
+    if text and labels:
+        label_list = [l.strip() for l in labels.split(",")]
+        result = classifier(text, candidate_labels=label_list)
+        st.write(result)
+    else:
+        st.warning("Please enter both text and labels.")