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app.py

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+import gradio as gr
+import torch
+import re
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# ============================================
+# 1. Configuration & Label Mapping
+# ============================================
+MODEL_ID = "mahmoudmohammad/marbertv2_single-label-dialect"
+
+# The exact label map mapped during your training
+LABEL_MAP = {
+    0: 'Algerian', 1: 'Egyptian', 2: 'Iraqi', 3: 'Jordanian', 
+    4: 'Lebanese', 5: 'Libyan', 6: 'MSA', 7: 'Moroccan', 
+    8: 'Palestinian', 9: 'Qatari', 10: 'Saudi', 11: 'Syrian', 
+    12: 'Tunisian', 13: 'Yemeni'
+}
+
+# ============================================
+# 2. Caching & Loading Model Locally
+# ============================================
+# Defining them at the module level loads them once during Space spin-up
+# making all future inferences blazingly fast.
+print(f"Loading {MODEL_ID} from Hugging Face...")
+try:
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+    model.eval() # Ensure dropout layers are frozen
+    print("✅ Model loaded successfully!")
+except Exception as e:
+    print(f"❌ Error loading model: {e}")
+
+# ============================================
+# 3. Preprocessing Logic
+# ============================================
+def preprocess_arabic_dialect(text: str) -> str:
+    """Cleans social media dialectal Arabic text. Exact copy from training script."""
+    if not isinstance(text, str):
+        return ""
+
+    text = re.sub(r'http\S+|www\.\S+|<.*?>', ' ', text)
+    text = re.sub(r'@\w+', ' ', text)
+    text = re.sub(r'#', '', text)
+    tashkeel = re.compile(r'[\u0617-\u061A\u064B-\u0652]')
+    text = re.sub(tashkeel, '', text)
+    text = re.sub(r'\u0640', '', text)
+    text = re.sub(r'(.)\1+', r'\1\1', text)
+    text = re.sub(r'[^\w\s\u0600-\u06FF]', ' ', text)
+    text = re.sub(r'\s+', ' ', text).strip()
+
+    return text
+
+# ============================================
+# 4. Inference Function
+# ============================================
+def predict_dialect(text: str):
+    if not text.strip():
+        # Handle empty text gently
+        return {label: 0.0 for label in LABEL_MAP.values()}
+
+    # 1. Clean the incoming text
+    clean_text = preprocess_arabic_dialect(text)
+
+    # 2. Tokenize (ensuring dimensions align with max_len 128)
+    inputs = tokenizer(
+        clean_text, 
+        return_tensors="pt", 
+        truncation=True, 
+        max_length=128, 
+        padding="max_length" # As trained in the model script
+    )
+
+    # 3. Model Inference (No Gradient tracking)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        # Calculate Softmax Probabilities
+        probs = torch.nn.functional.softmax(logits, dim=-1)[0]
+    
+    # 4. Format into a Dictionary for the Gradio 'Label' UI
+    # Gradio will use these numbers to automatically populate prediction progress bars
+    results = {LABEL_MAP[i]: float(probs[i]) for i in range(len(LABEL_MAP))}
+    
+    return results
+
+# ============================================
+# 5. UI Application Definition (Dark Mode Native)
+# ============================================
+
+# Dark mode snippet using Gradio js injection
+dark_mode_js = """
+function() { 
+    document.body.classList.add('dark');
+}
+"""
+
+with gr.Blocks(js=dark_mode_js, theme=gr.themes.Monochrome(primary_hue="purple")) as demo:
+    gr.Markdown("# 🌍 Arabic Dialect Detector")
+    gr.Markdown("Identify whether text represents **MSA** or one of 13 Regional **Arabic Dialects** (e.g., Egyptian, Saudi, Moroccan, Lebanese...). \n*Powered by a Fine-Tuned MARBERTv2 base model.*")
+    
+    with gr.Row():
+        
+        # Left Panel (Inputs and Buttons)
+        with gr.Column(scale=5):
+            text_input = gr.Textbox(
+                label="أدخل النص (Enter Arabic Text Here)", 
+                placeholder="إزيك يا صاحبي عامل إيه؟",
+                lines=5
+            )
+            submit_btn = gr.Button("Detect Dialect 🔎", variant="primary")
+            
+            # Diverse dialect examples to populate inside the Space
+            examples_list = [
+                ["إزيك يا صاحبي عامل إيه؟ فينك من زمان"],           # Egyptian
+                ["شو أخبارك؟ وين هالغيبة اشتقنالك كتير"],          # Lebanese/Syrian
+                ["كيداير لاباس عليك؟ شنو كتدير؟"],                 # Moroccan
+                ["وشلونك طال عمرك؟ عساك طيب ومبسوط"],            # Saudi / Gulf
+                ["السلام عليكم ورحمة الله وبركاته، كيف حالكم اليوم؟"], # MSA
+                ["أنا هسا رايح عالدار بدك اشي؟"],                  # Jordanian/Palestinian
+            ]
+            gr.Examples(
+                examples=examples_list,
+                inputs=text_input,
+                label="Try these Examples"
+            )
+
+        # Right Panel (Output Predictions Bar)
+        with gr.Column(scale=4):
+            # Showing Top 4 detected probabilities smoothly
+            output_labels = gr.Label(num_top_classes=4, label="Dialect Confidence")
+            
+            # Just to show preprocessing mapping in backend visually to users
+            gr.Markdown("*(Internal Text pre-processing strips tags, mentions, tashkeel, repeated letters etc. via REGEX just like the model training before execution!)*")
+
+    # Connect UI button -> Inference Logic
+    submit_btn.click(
+        fn=predict_dialect,
+        inputs=text_input,
+        outputs=output_labels
+    )
+    
+# Boot Gradio Application
+if __name__ == "__main__":
+    # Ensure memory handling on Gradio hosting wrapper
+    demo.launch(show_error=True)

requirements.txt

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+torch
+transformers
+gradio