Update app.py

app.py

@@ -1,26 +1,38 @@
-from transformers import pipeline
-
-import torch
+from transformers import AutoConfig, AutoModelForTokenClassification, AutoTokenizer
+import numpy as np
 import gradio as gr
 
-repository_id = "PRAli22/AraBert-Arabic-Sentiment-Analysis"
 
-def sentiment_predict(sentences):
-    pipe = pipeline("sentiment-analysis", model=repository_id, return_all_scores=True)
+tokenizer = AutoTokenizer.from_pretrained("PRAli22/AraBert-Arabic-Sentiment-Analysis" )
+model = AutoModelForSequenceClassification.from_pretrained("PRAli22/AraBert-Arabic-Sentiment-Analysis")
+
+def classify_sentiment(text):
+  
+  # Tokenize the text
+  inputs = tokenizer(text, return_tensors="pt")
+
+  # Get model predictions
+  outputs = model(**inputs)
+  predicted_label_index = np.argmax(outputs[0].detach().numpy()).item()
+
+  # Retrieve label names from the model's config
+  label_names = {0: 'Positive', 1: 'Negative', 2: 'Neutral', 3: 'Mixed'}
+
+  predicted_label = label_names[predicted_label_index]
 
-    return pipe(sentences)
+  return predicted_label
 
 css_code='body{background-image:url("https://media.istockphoto.com/id/1256252051/vector/people-using-online-translation-app.jpg?s=612x612&w=0&k=20&c=aa6ykHXnSwqKu31fFR6r6Y1bYMS5FMAU9yHqwwylA94=");}'
 
 demo = gr.Interface(
-    fn=sentiment_predict,
+    fn=classify_sentiment,
     inputs=
         gr.Textbox(label="sentence", placeholder=" Enter the sentence "),
         
     
-    outputs=[gr.Textbox(label="entity name")],
-    title="Arabic Named Entity Recognition",
-    description= "This is Arabic Named Entity Recognition System, it takes an arabian sentence as input and returns every entity name within it",
+    outputs=[gr.Textbox(label="the sentiment")],
+    title="Arabic Sentiment Analyzer",
+    description= "This is Arabic Sentiment Analyzer, it takes an arabian sentence as input and returns the sentiment behind it",
     css = css_code
 )
 demo.launch()