added classifier config

app.py

@@ -21,12 +21,15 @@ td {
 
 MASK_TOKEN = "<mask>"
 
-EMOTION_MAP = {
+EMOJI_MAP = {
     "anger": "😡",
     "fear": "😱",
     "happy": "😄",
     "love": "😍",
     "sadness": "😭",
+    "positive": "🤗",
+    "negative": "😤",
+    "neutral": "😐",
 }
 
 
@@ -37,7 +40,6 @@ def display_table(df: pd.DataFrame, subheader: str):
 
 def setup():
     st.markdown(STYLE, unsafe_allow_html=True)
-    # st.title("")
     st.markdown(
         """
         # 🇮🇩 Indonesian RoBERTa Base
@@ -45,11 +47,18 @@ def setup():
         Demo Powered by [Indonesian RoBERTa Base](https://huggingface.co/flax-community/indonesian-roberta-base).
         """
     )
+    st.sidebar.subheader("Settings")
 
 
 def main():
     setup()
 
+    analyze = st.sidebar.selectbox(
+        "What should we analyze?",
+        ("Emotion", "Sentiment"),
+        help="Classifier model to choose for text analysis",
+    )
+
     user_input = st.text_input(
         f"Insert a sentence to predict with a {MASK_TOKEN} token // Masukkan kalimat untuk diisi dengan token {MASK_TOKEN}",
         value=f"Gila! Hari ini aku {MASK_TOKEN} banget..",
@@ -58,8 +67,11 @@ def main():
     mlm_model = "flax-community/indonesian-roberta-base"
     mask_api = InferenceApi(mlm_model)
 
-    sa_model = "StevenLimcorn/indonesian-roberta-base-emotion-classifier"
-    # sa_model = "w11wo/indonesian-roberta-base-sentiment-classifier"
+    if analyze == "Emotion":
+        sa_model = "StevenLimcorn/indonesian-roberta-base-emotion-classifier"
+    elif analyze == "Sentiment":
+        sa_model = "w11wo/indonesian-roberta-base-sentiment-classifier"
+
     sa_pipeline = pipeline("sentiment-analysis", model=sa_model, tokenizer=sa_model)
 
     if len(user_input) > 0:
@@ -89,7 +101,7 @@ def main():
                 result_dict.update(sa_output[0])
                 sa_df = sa_df.append(result_dict, ignore_index=True)
 
-            sa_df["label"] = sa_df["label"].apply(lambda x: x + " " + EMOTION_MAP[x])
+            sa_df["label"] = sa_df["label"].apply(lambda x: x + " " + EMOJI_MAP[x])
             sa_df_styled = sa_df.copy(deep=False)
             sa_df_styled = sa_df_styled.style.set_properties(
                 subset=["sequence", "label", "score"], **{"text-align": "left"}