Create the app.py

app.py

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+import torch
+import os
+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from IndicTransToolkit.processor import IndicProcessor
+
+# Get token from environment variable
+token = os.getenv("HUGGINGFACE_HUB_TOKEN")
+
+# Device configuration
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Model configuration - English to Kannada translation
+src_lang, tgt_lang = "eng_Latn", "kan_Knda"
+model_name = "ai4bharat/indictrans2-en-indic-dist-200M"
+
+# Global variables to store model and tokenizer
+model = None
+tokenizer = None
+ip = None
+
+def load_model():
+    """Load the translation model and tokenizer"""
+    global model, tokenizer, ip
+    
+    try:
+        print(f"Loading model: {model_name}")
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_name, 
+            trust_remote_code=True,
+            token=token
+        )
+        
+        model = AutoModelForSeq2SeqLM.from_pretrained(
+            model_name, 
+            trust_remote_code=True, 
+            dtype=torch.float16,
+            token=token
+        ).to(DEVICE)
+        
+        ip = IndicProcessor(inference=True)
+        print(f"Model loaded successfully on {DEVICE}")
+        return True
+        
+    except Exception as e:
+        print(f"Error loading model: {str(e)}")
+        return False
+
+def translate_text(input_text):
+    """
+    Translate input text using the loaded model
+    
+    Args:
+        input_text: Single sentence to translate
+    
+    Returns:
+        Translated text
+    """
+    if not model or not tokenizer or not ip:
+        return "❌ Model not loaded. Please check the model configuration."
+    
+    if not input_text.strip():
+        return "Please enter some text to translate."
+    
+    try:
+        # Single sentence translation
+        input_sentences = [input_text.strip()]
+        
+        if not input_sentences:
+            return "No valid sentences found."
+        
+        # Preprocess the input
+        batch = ip.preprocess_batch(
+            input_sentences,
+            src_lang=src_lang,
+            tgt_lang=tgt_lang,
+        )
+        
+        # Tokenize the sentences
+        inputs = tokenizer(
+            batch,
+            truncation=True,
+            padding="longest",
+            return_tensors="pt",
+            return_attention_mask=True,
+        ).to(DEVICE)
+        
+        # Generate translations
+        with torch.no_grad():
+            generated_tokens = model.generate(
+                **inputs,
+                use_cache=False,
+                min_length=0,
+                max_length=256,
+                num_beams=5,
+                num_return_sequences=1,
+            )
+        
+        # Decode the generated tokens
+        generated_tokens = tokenizer.batch_decode(
+            generated_tokens,
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=True,
+        )
+        
+        # Postprocess the translations
+        translations = ip.postprocess_batch(generated_tokens, lang=tgt_lang)
+        
+        # Return single translation
+        return translations[0] if translations else "Translation failed."
+            
+    except Exception as e:
+        return f"❌ Translation error: {str(e)}"
+
+def create_interface():
+    """Create and configure the Gradio interface"""
+    
+    # Load model on startup
+    model_loaded = load_model()
+    
+    if not model_loaded:
+        # Create a simple error interface
+        with gr.Blocks(title="Translation App - Error") as demo:
+            gr.Markdown("## ❌ Model Loading Error")
+            gr.Markdown("Failed to load the translation model. Please check:")
+            gr.Markdown("- Your Hugging Face token is set correctly")
+            gr.Markdown("- You have access to the gated model")
+            gr.Markdown("- Your internet connection is working")
+        return demo
+    
+    # Create the main interface
+    with gr.Blocks(
+        title="AI4Bharat IndicTrans2 Translation",
+        theme=gr.themes.Soft(),
+    ) as demo:
+        
+        gr.Markdown(
+            f"""
+            # 🌍 AI4Bharat IndicTrans2 Translation
+            
+            **Current Configuration:**
+            - **Source Language:** {src_lang} (English)
+            - **Target Language:** {tgt_lang} (Kannada)
+            - **Model:** {model_name}
+            - **Device:** {DEVICE}
+            
+            Enter text below to translate from English to Kannada.
+            """)
+        
+        
+        with gr.Row():
+            with gr.Column():
+                input_text = gr.Textbox(
+                    label=f"Input Text ({src_lang})",
+                    placeholder="Enter English text to translate...",
+                    lines=5,
+                    max_lines=10
+                )
+                
+                with gr.Row():
+                    translate_btn = gr.Button("🔄 Translate", variant="primary")
+                    clear_btn = gr.Button("🗑️ Clear")
+            
+            with gr.Column():
+                output_text = gr.Textbox(
+                    label=f"Translation ({tgt_lang})",
+                    lines=5,
+                    max_lines=10,
+                    interactive=False
+                )
+        
+        # Example inputs
+        gr.Markdown("### 📝 Example Inputs:")
+        examples = [
+            ["Hello, how are you?"],
+            ["I am going to the market today."],
+            ["This is a very beautiful place."],
+            ["Can you help me?"],
+        ]
+        
+        gr.Examples(
+            examples=examples,
+            inputs=[input_text],
+            outputs=[output_text],
+            fn=translate_text,
+            cache_examples=True
+        )
+        
+        # Event handlers
+        translate_btn.click(
+            fn=translate_text,
+            inputs=[input_text],
+            outputs=[output_text]
+        )
+        
+        clear_btn.click(
+            fn=lambda: ("", ""),
+            outputs=[input_text, output_text]
+        )
+        
+        # Add footer
+        gr.Markdown("---")
+    
+    return demo
+
+if __name__ == "__main__":
+    # Create and launch the interface
+    demo = create_interface()
+    
+    # Launch the app
+    demo.launch(
+        server_name="0.0.0.0",  # Allow external connections
+        server_port=7860,       # Default Gradio port
+        share=False,            # Set to True if you want a public link
+        debug=True,
+        show_error=True
+    )