Create streamlit-app.py

streamlit-app.py

@@ -0,0 +1,52 @@
+import os
+import requests
+import streamlit as st
+
+API_URL = "https://router.huggingface.co/hf-inference/models/cardiffnlp/twitter-roberta-base-sentiment-latest"
+headers = {"Authorization": f"Bearer {os.environ.get('HF_TOKEN')}"}
+
+def query(payload):
+    return requests.post(API_URL, headers=headers, json=payload).json()
+
+def color(label):
+    return {
+        "positive": "green",
+        "negative": "red",
+        "neutral": "orange"
+    }.get(label.lower(), "black")
+
+st.set_page_config(page_title="Twitter Sentiment Analysis", page_icon="🧠")
+
+st.title("🧠 Twitter Sentiment Analysis (RoBERTa)")
+st.write("Masukkan teks untuk dianalisis sentimennya:")
+
+text = st.text_area("Teks Input")
+
+if st.button("Analisis Sentimen"):
+    if not text.strip():
+        st.warning("⚠️ Input kosong")
+    else:
+        output = query({"inputs": text})
+
+        try:
+            preds = output[0]
+            main = max(preds, key=lambda x: x["score"])
+            c = color(main["label"])
+
+            st.markdown(
+                f"<h3>🎯 Prediksi Utama: "
+                f"<span style='color:{c}'>{main['label']}</span> ({main['score']:.4f})</h3>",
+                unsafe_allow_html=True
+            )
+
+            st.write("---")
+            st.subheader("Detail Skor:")
+
+            for p in preds:
+                st.markdown(
+                    f"<div><b style='color:{color(p['label'])}'>{p['label']}</b> → {p['score']:.4f}</div>",
+                    unsafe_allow_html=True
+                )
+
+        except:
+            st.error(output)