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app.py

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+import spaces
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "sarvamai/sarvam-translate"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float32).to('cuda:0')
+
+@spaces.GPU(duration=120)
+def generate_response(tgt_lang, user_prompt):
+    messages = [
+        {"role": "system", "content": f"Translate the following sentence into {tgt_lang}."},
+        {"role": "user", "content": user_prompt},
+    ]
+    
+    # Apply chat template to structure the conversation
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+    )
+    
+    # Tokenize and move input to model device
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    
+    # Generate the output
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=1024,
+        do_sample=True,
+        temperature=0.01,
+        num_return_sequences=1
+    )
+    output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+    return tokenizer.decode(output_ids, skip_special_tokens=True)
+
+# Create Gradio UI
+demo = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Radio(["Hindi", "Bengali", "Marathi", "Telugu", "Tamil", "Gujarati", "Urdu", "Kannada", "Odia", "Malayalam", "Punjabi", "Assamese", "Maithili", "Santali", "Kashmiri", "Nepali", "Sindhi", "Dogri", "Konkani", "Manipuri (Meitei)", "Bodo", "Sanskrit"], label="Target Language", value="Hindi"),
+        gr.Textbox(label="Input Text", value="Be the change you wish to see in the world."),
+    ],
+    outputs=gr.Textbox(label="Translation"),
+    title="SARVAM - TRANSLATE",
+    description="Now supporting 22 Indian languages and structured long-form text"
+)
+
+# Launch the app
+demo.launch()

requirements.txt

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+transformers