Update app.py

app.py

@@ -1,11 +1,20 @@
-import streamlit as st
-import skops.hub_utils as hub_utils
-import pandas as pd
+#import streamlit as st
+#import skops.hub_utils as hub_utils
+#import pandas as pd
 import re
 from nltk.tokenize import word_tokenize
 import nltk
 
 
+
+
+import gradio as gr
+import pandas as pd
+from nltk.tokenize import word_tokenize
+from transformers import AutoModelForSequenceClassification
+import hub_utils  # Assuming you have a custom module for interacting with the Hugging Face model hub
+
+
 nltk.download('punkt')
 
 
@@ -114,18 +123,17 @@ def features(sentence, index):
     }
 
 
-import gradio as gr
 
 # Define the function for processing user input
 def process_text(text_input):
     if text_input:
-        # Prepare text
+        # Prepare text (define this function)
         prepared_text = prepare_text(text_input)
         
         # Tokenize text
         tokenized_text = word_tokenize(prepared_text)
         
-        # Extract features
+        # Extract features (define this function)
         features_list = [features(tokenized_text, i) for i in range(len(tokenized_text))]
         
         # Create a DataFrame with the features
@@ -133,7 +141,10 @@ def process_text(text_input):
         
         # Load the model from the Hub
         model_id = "Alshargi/arabic-msa-dialects-segmentation"
-        res = hub_utils.get_model_output(model_id, data)
+        model = AutoModelForSequenceClassification.from_pretrained(model_id)
+        
+        # Get model output (define or import the get_model_output function)
+        res = hub_utils.get_model_output(model, data)
         
         # Return the model output
         return res
@@ -145,3 +156,4 @@ iface = gr.Interface(fn=process_text, inputs="text", outputs="text", title="Arab
 
 # Launch the Gradio interface
 iface.launch(share=True)
+