Upload app.py

app.py

@@ -0,0 +1,117 @@
+import streamlit as st
+from huggingface_hub import InferenceApi
+import pandas as pd
+from transformers import pipeline
+
+STYLE = """
+<style>
+img {
+    max-width: 100%;
+}
+
+th {
+    text-align: left!important
+}
+
+td {
+    font-size:
+}
+</style>
+"""
+
+MASK_TOKEN = "<mask>"
+
+EMOJI_MAP = {
+    "anger": "😡",
+    "fear": "😱",
+    "happy": "😄",
+    "love": "😍",
+    "sadness": "😭",
+    "positive": "🤗",
+    "negative": "😤",
+    "neutral": "😐",
+}
+
+
+def display_table(df: pd.DataFrame, subheader: str):
+    st.subheader(subheader)
+    st.table(df)
+
+
+def setup():
+    st.markdown(STYLE, unsafe_allow_html=True)
+    st.markdown(
+        """
+        # 🇮🇩 Indonesian RoBERTa Base
+
+        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..",
+    )
+
+    mlm_model = "BigSalmon/BestMask2"
+    mask_api = InferenceApi(mlm_model)
+
+    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:
+        try:
+            user_input.index(MASK_TOKEN)
+        except ValueError:
+            st.error(
+                f"Please enter a sentence with the correct {MASK_TOKEN} token // Harap masukkan kalimat dengan token {MASK_TOKEN} yang benar"
+            )
+        else:
+            # render masked language modeling table
+            mlm_result = mask_api(inputs=user_input)
+
+            if mlm_result == None:
+                st.write("Model is loading. Please try again later...")
+                return
+
+            mlm_df = pd.DataFrame(mlm_result)
+            mlm_df.drop(columns=["token", "token_str"], inplace=True)
+            mlm_df_styled = mlm_df.copy(deep=False)
+            mlm_df_styled = mlm_df_styled.style.set_properties(
+                subset=["sequence", "score"], **{"text-align": "left"}
+            )
+            display_table(mlm_df_styled, "🎈 Top 5 Predictions")
+
+            # render sentiment analysis table
+            sa_df = pd.DataFrame(columns=["sequence", "label", "score"])
+            for sequence in mlm_df["sequence"].values:
+                sa_output = sa_pipeline(sequence)  # predict for every mlm output
+                result_dict = {"sequence": sequence}
+                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 + " " + 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"}
+            )
+            display_table(sa_df_styled, "🤔 By saying that, I guess you are feeling..")
+
+
+if __name__ == "__main__":
+    main()