Update app.py

app.py

@@ -1,32 +1,26 @@
-import gradio as gr
-import tensorflow as tf
-from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
+from transformers import MarianMTModel, MarianTokenizer
 
-path = 'tf_model/'
-model_checkpoint = "Helsinki-NLP/opus-mt-en-hi"
-tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
-model = TFAutoModelForSeq2SeqLM.from_pretrained(path)
+def translate_text(input_text, source_lang="en", target_lang="hi"):
+    # Load pre-trained model and tokenizer for MarianMT
+    model_name = f'Helsinki-NLP/opus-mt-{source_lang}-{target_lang}'
+    model = MarianMTModel.from_pretrained(model_name)
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
 
-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
+    # Tokenize the input text
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
 
-# 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?"]
+    # Generate translation
+    translation_ids = model.generate(input_ids)
 
-# 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
-)
+    # Decode the translated text
+    translated_text = tokenizer.decode(translation_ids[0], skip_special_tokens=True)
+
+    return translated_text
+
+if __name__ == "__main__":
+    # Example usage
+    input_text = "Hello, how are you?"
+    translated_text = translate_text(input_text)
+    
+    print(f"Input Text: {input_text}")
+    print(f"Translated Text: {translated_text}")