Create app.py

app.py

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+import re
+
+def preprocess_arabic_text(text):
+    # Remove diacritics
+    text = re.sub(r'[\u064B-\u0652]', '', text)
+    # Remove punctuation and non-Arabic characters
+    text = re.sub(r'[^\u0600-\u06FF\s]', '', text)
+    # Normalize Arabic letters
+    text = re.sub(r'\u0629', '\u0647', text)  # Replace Teh Marbuta with Heh
+    text = re.sub(r'\u064A', '\u0649', text)  # Replace Yeh with Alef Maqsura
+
+      # Remove diacritics (optional, depending on use case)
+    text = re.sub(r'[\u064B-\u065F]', '', text)
+    
+    # Normalize elongated letters (e.g., "جدااا" -> "جدا")
+    text = re.sub(r'(.)\1{2,}', r'\1\1', text)
+    
+    # Remove non-Arabic characters (e.g., English words, numbers, special symbols)
+    text = re.sub(r'[^\u0600-\u06FF\s]', '', text)
+    
+    # Normalize whitespace
+    text = re.sub(r'\s+', ' ', text).strip()
+    
+    return text
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the tokenizer and model
+model_name = 'aubmindlab/bert-base-arabertv02'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)
+
+def analyze_sentiment(text):
+    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+    sentiment_map = {0: 'Negative', 1: 'Neutral', 2: 'Positive'}
+    return sentiment_map[predicted_class]
+
+import gradio as gr
+
+def process_text_and_analyze_sentiment(text):
+    preprocessed_text = preprocess_arabic_text(text)
+    sentiment = analyze_sentiment(preprocessed_text)
+    return preprocessed_text, sentiment
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=process_text_and_analyze_sentiment,
+    inputs=gr.Textbox(label="Enter Arabic Text"),
+    outputs=[
+        gr.Textbox(label="Preprocessed Text"),
+        gr.Textbox(label="Sentiment")
+    ],
+    title="Arabic Text Analysis",
+    description="This application preprocesses Arabic text using regex and analyzes sentiment using a pre-trained model."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch(share=True)
+