Add application file

app.py

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+
+import gradio as gr
+# Check if CUDA (GPU) is available
+import torch
+from transformers import T5ForConditionalGeneration, PreTrainedTokenizerFast
+
+# Define the path to the checkpoint directory
+checkpoint_dir = "/home/only_sainaa/home/Huggingface/marianmt_conversion/results/checkpoint-221496"
+
+# Load the model
+model = T5ForConditionalGeneration.from_pretrained(checkpoint_dir)
+model.eval()
+# Load the tokenizer using PreTrainedTokenizerFast
+tokenizer = PreTrainedTokenizerFast.from_pretrained(checkpoint_dir)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Move the model to the same device (GPU or CPU)
+model.to(device)
+
+# Function to perform translation using the model
+def translate_text(input_text):
+    # Tokenize the input text
+    inputs = tokenizer(input_text, return_tensors="pt")
+    
+    # Move the input tensors to the same device as the model
+    inputs = {k: v.to(device) for k, v in inputs.items() if k in ['input_ids', 'attention_mask']}
+    
+    # Generate translation
+    outputs = model.generate(**inputs)
+    
+    # Decode the output to human-readable text
+    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return translated_text
+
+# Create a Gradio interface
+gr_interface = gr.Interface(
+    fn=translate_text, 
+    inputs="text", 
+    outputs="text",
+    title="Mongolian Cyrillic to Mongolian Script Model",
+    description="Enter text in Mongolian Cyrillic"
+)
+
+# Launch the Gradio interface
+gr_interface.launch()