Create app.py

app.py

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+import gradio as gr
+import torch
+import os
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load Hugging Face token from the environment variable
+HF_TOKEN = os.getenv("HF_TOKEN")
+if HF_TOKEN is None:
+    raise ValueError("HF_TOKEN environment variable is not set. Please set it before running the script.")
+
+# Check for GPU support and configure appropriately
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Device being used: {device}")
+
+# Model configurations
+MSA_TO_SYRIAN_MODEL = "Omartificial-Intelligence-Space/Shami-MT"
+SYRIAN_TO_MSA_MODEL = "Omartificial-Intelligence-Space/SHAMI-MT-2MSA"
+
+# Load models and tokenizers
+print("Loading MSA to Syrian model...")
+msa_to_syrian_tokenizer = AutoTokenizer.from_pretrained(MSA_TO_SYRIAN_MODEL)
+msa_to_syrian_model = AutoModelForSeq2SeqLM.from_pretrained(MSA_TO_SYRIAN_MODEL).to(device)
+
+print("Loading Syrian to MSA model...")
+syrian_to_msa_tokenizer = AutoTokenizer.from_pretrained(SYRIAN_TO_MSA_MODEL)
+syrian_to_msa_model = AutoModelForSeq2SeqLM.from_pretrained(SYRIAN_TO_MSA_MODEL).to(device)
+
+print("Models loaded successfully!")
+
+def translate_msa_to_syrian(text):
+    """Translate from Modern Standard Arabic to Syrian dialect"""
+    if not text.strip():
+        return ""
+    
+    try:
+        input_ids = msa_to_syrian_tokenizer(text, return_tensors="pt").input_ids.to(device)
+        outputs = msa_to_syrian_model.generate(input_ids, max_length=512, num_beams=5, early_stopping=True)
+        translated_text = msa_to_syrian_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return translated_text
+    except Exception as e:
+        return f"Translation error: {str(e)}"
+
+def translate_syrian_to_msa(text):
+    """Translate from Syrian dialect to Modern Standard Arabic"""
+    if not text.strip():
+        return ""
+    
+    try:
+        input_ids = syrian_to_msa_tokenizer(text, return_tensors="pt").input_ids.to(device)
+        outputs = syrian_to_msa_model.generate(input_ids, max_length=512, num_beams=5, early_stopping=True)
+        translated_text = syrian_to_msa_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return translated_text
+    except Exception as e:
+        return f"Translation error: {str(e)}"
+
+def bidirectional_translate(text, direction):
+    """Handle bidirectional translation based on user selection"""
+    if direction == "MSA → Syrian":
+        return translate_msa_to_syrian(text)
+    elif direction == "Syrian → MSA":
+        return translate_syrian_to_msa(text)
+    else:
+        return "Please select a translation direction"
+
+# Create Gradio interface
+with gr.Blocks(title="SHAMI-MT: Bidirectional Arabic Translation") as demo:
+    
+    gr.HTML("""
+    <div style="text-align: center; margin-bottom: 2rem;">
+        <h1>🌍 SHAMI-MT: Bidirectional Arabic Translation</h1>
+        <p>Translate between Modern Standard Arabic (MSA) and Syrian Dialect</p>
+        <p><strong>Built on AraT5v2-base-1024 architecture</strong></p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.HTML("""
+            <div style="background: #f8f9fa; padding: 1rem; border-radius: 8px; margin: 1rem 0;">
+                <h3>📚 Model Information</h3>
+                <ul>
+                    <li><strong>Model Type:</strong> Sequence-to-Sequence Translation</li>
+                    <li><strong>Base Model:</strong> UBC-NLP/AraT5v2-base-1024</li>
+                    <li><strong>Languages:</strong> Arabic (MSA ↔ Syrian Dialect)</li>
+                    <li><strong>Device:</strong> GPU/CPU Auto-detection</li>
+                </ul>
+            </div>
+            """)
+    
+        with gr.Column(scale=2):
+            direction = gr.Dropdown(
+                choices=["MSA → Syrian", "Syrian → MSA"],
+                value="MSA → Syrian",
+                label="Translation Direction"
+            )
+            
+            input_text = gr.Textbox(
+                label="Input Text",
+                placeholder="Enter Arabic text here...",
+                lines=5
+            )
+            
+            translate_btn = gr.Button("🚀 Translate", variant="primary")
+            
+            output_text = gr.Textbox(
+                label="Translation",
+                lines=5
+            )
+    
+    # Connect the interface
+    translate_btn.click(
+        fn=bidirectional_translate,
+        inputs=[input_text, direction],
+        outputs=output_text
+    )
+    
+    # Add example inputs
+    gr.Examples(
+        examples=[
+            ["أنا لا أعرف إذا كان سيتمكن من الحضور اليوم أم لا.", "MSA → Syrian"],
+            ["كيف حالك؟", "MSA → Syrian"],
+            ["ما بعرف إذا رح يقدر يجي اليوم ولا لأ.", "Syrian → MSA"],
+            ["شلونك؟", "Syrian → MSA"]
+        ],
+        inputs=[input_text, direction],
+        outputs=output_text,
+        fn=bidirectional_translate
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)