Create app.py

app.py

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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()