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| import gradio as gr | |
| import tensorflow as tf | |
| from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM | |
| path = 'tf_model/' | |
| model_checkpoint = "Helsinki-NLP/opus-mt-en-hi" | |
| tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) | |
| model = TFAutoModelForSeq2SeqLM.from_pretrained(path) | |
| title = 'Text Translation(English to Hindi)' | |
| def process_input(text): | |
| # Tokenize the input text using the tokenizer and convert to NumPy arrays | |
| tokenized = tokenizer([text], return_tensors='np') | |
| # Generate output sequences using the pre-trained model | |
| out = model.generate(**tokenized, max_length=128) | |
| # Switch the tokenizer to target mode | |
| with tokenizer.as_target_tokenizer(): | |
| # Decode the generated output sequence, skipping special tokens | |
| result = tokenizer.decode(out[0], skip_special_tokens=True) | |
| return result | |
| # Example input text for the GUI | |
| examples = ['If you have the time, come along with me.', 'I can come if you want.', 'Tom was at home alone.', 'Wow!','How rude of you!',"What's in your hand?"] | |
| # Create a Gradio Interface for the model | |
| model_gui = gr.Interface( | |
| process_input, # Function for processing input and generating output | |
| gr.Textbox(lines=3, label="English"), # Textbox for entering English text | |
| gr.Textbox(lines=3, label="Hindi"), # Textbox for displaying translated Hindi text | |
| title=title, # Set the title of the GUI | |
| examples=examples # Provide example input text for the GUI | |
| ) | |
| # Launch the Gradio GUI with sharing enabled | |
| model_gui.launch() |