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Upload app.py

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app.py ADDED
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+ import gradio as gr
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+ import tensorflow as tf
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+ from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
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+
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+ path = 'tf_model/'
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+ model_checkpoint = "Helsinki-NLP/opus-mt-en-hi"
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+ tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
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+ model = TFAutoModelForSeq2SeqLM.from_pretrained(path)
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+
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+ title = 'Text Translation(English to Hindi)'
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+ def process_input(text):
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+ # Tokenize the input text using the tokenizer and convert to NumPy arrays
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+ tokenized = tokenizer([text], return_tensors='np')
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+ # Generate output sequences using the pre-trained model
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+ out = model.generate(**tokenized, max_length=128)
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+ # Switch the tokenizer to target mode
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+ with tokenizer.as_target_tokenizer():
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+ # Decode the generated output sequence, skipping special tokens
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+ result = tokenizer.decode(out[0], skip_special_tokens=True)
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+ return result
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+
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+ # Example input text for the GUI
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+ 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?"]
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+
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+ # Create a Gradio Interface for the model
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+ model_gui = gr.Interface(
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+ process_input, # Function for processing input and generating output
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+ gr.Textbox(lines=3, label="English"), # Textbox for entering English text
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+ gr.Textbox(lines=3, label="Hindi"), # Textbox for displaying translated Hindi text
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+ title=title, # Set the title of the GUI
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+ examples=examples # Provide example input text for the GUI
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+ )
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+
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+ # Launch the Gradio GUI with sharing enabled
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+ model_gui.launch(share=True)