Update app.py

app.py

@@ -1,13 +1,8 @@
 import streamlit as st
-import joblib
+import skops.hub_utils as hub_utils
+import pandas as pd
 import re
-from transformers import pipeline
-
-# Load the scikit-learn model
-sklearn_model = joblib.load("arabic-msa-dialects-segmentation-v1.pkl")
-
-# Wrap the scikit-learn model inside a Hugging Face pipeline
-pipeline_model = pipeline(task="feature-extraction", model=sklearn_model)
+from nltk.tokenize import word_tokenize
 
 # Define feature functions
 def features(sentence, index):
@@ -58,10 +53,14 @@ if text_input:
     # Extract features
     features_list = [features(tokenized_text, i) for i in range(len(tokenized_text))]
     
-    # Use the Hugging Face pipeline to make predictions
-    prediction = pipeline_model(features_list)
+    # Create a DataFrame with the features
+    data = pd.DataFrame(features_list)
+    
+    # Load the model from the Hub
+    model_id = "Alshargi/arabic-msa-dialects-segmentation"
+    res = hub_utils.get_model_output(model_id, data)
     
-    # Display the prediction
-    st.write("Prediction:", prediction)
+    # Display the model output
+    st.write("Model Output:", res)
 else:
     st.write("Please enter some text.")