initial commit

app.py

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+import gradio as gr
+from transformers import MarianMTModel, MarianTokenizer
+import torch
+import nltk
+
+# Download punkt for sentence tokenization
+nltk.download('punkt')
+nltk.download('punkt_tab')
+
+from nltk.tokenize import sent_tokenize
+
+# Cache for storing models and tokenizers
+models_cache = {}
+
+def load_model(model_name):
+    """
+    Load and cache the MarianMT model and tokenizer.
+    """
+    if model_name not in models_cache:
+        tokenizer = MarianTokenizer.from_pretrained(model_name)
+        model = MarianMTModel.from_pretrained(model_name)
+        if torch.cuda.is_available():
+            model = model.to('cuda')
+        models_cache[model_name] = (model, tokenizer)
+    return models_cache[model_name]
+
+def translate_text(model_name, text):
+    """
+    Translate input text sentence by sentence using the specified model.
+    """
+    if not model_name or not text:
+        return "Please select a model and provide text for translation."
+    
+    try:
+        # Load the model and tokenizer
+        model, tokenizer = load_model(model_name)
+        
+        # Split text into sentences
+        sentences = sent_tokenize(text)
+        translated_sentences = []
+        
+        for sentence in sentences:
+            # Tokenize the sentence
+            print(f"Sentence: {sentence}\n")
+            tokens = tokenizer(sentence, return_tensors="pt", padding=True)
+            if torch.cuda.is_available():
+                tokens = {k: v.to('cuda') for k, v in tokens.items()}
+            
+            # Generate translation for the sentence
+            translated = model.generate(**tokens)
+            translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+            translated_sentences.append(translated_text)
+        
+        # Join translated sentences back into a single string
+        return " ".join(translated_sentences)
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Model options
+model_options = [
+    ("English to Turkish", "Helsinki-NLP/opus-mt-tc-big-en-tr"),
+    ("Turkish to English", "Helsinki-NLP/opus-mt-tc-big-tr-en"),
+    ("English to French", "Helsinki-NLP/opus-mt-tc-big-en-fr"),
+    ("French to English", "Helsinki-NLP/opus-mt-tc-big-fr-en"),
+    ("English to German", "Helsinki-NLP/opus-mt-en-de"),
+    ("German to English", "Helsinki-NLP/opus-mt-de-en"),
+    ("English to Spanish", "Helsinki-NLP/opus-mt-tc-big-en-es"),
+    ("Spanish to English", "Helsinki-NLP/opus-mt-es-en"),
+    ("English to Arabic", "Helsinki-NLP/opus-mt-tc-big-en-ar"),
+    ("Arabic to English", "Helsinki-NLP/opus-mt-tc-big-ar-en"),
+    ("English to Urdu", "Helsinki-NLP/opus-mt-en-ur"),
+    ("Urdu to English", "Helsinki-NLP/opus-mt-ur-en"),
+    ("English to Hindi", "Helsinki-NLP/opus-mt-en-hi"),
+    ("Hindi to English", "Helsinki-NLP/opus-mt-hi-en"),
+    ("English to Chinese", "Helsinki-NLP/opus-mt-en-zh"),
+    ("Chinese to English", "Helsinki-NLP/opus-mt-zh-en")
+]
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🌍 Real-Time Sentence Translation")
+    
+    with gr.Row():
+        model_dropdown = gr.Dropdown(
+            label="Select Translation Model",
+            choices=[option[1] for option in model_options],
+            type="value",
+        )
+    
+    with gr.Row():
+        input_text = gr.Textbox(
+            label="Enter text (complete sentences)",
+            lines=5,
+            placeholder="Type here...",
+        )
+    
+    with gr.Row():
+        translate_button = gr.Button("Translate")
+        clear_button = gr.Button("Clear")
+    
+    output_text = gr.Textbox(label="Translated Text", interactive=False)
+    
+    def clear_inputs():
+        return "", ""
+    
+    translate_button.click(
+        fn=translate_text,
+        inputs=[model_dropdown, input_text],
+        outputs=output_text,
+    )
+    
+    clear_button.click(
+        fn=clear_inputs,
+        inputs=[],
+        outputs=[input_text, output_text],
+    )
+
+# Run the Gradio app
+demo.launch()

requirements.txt

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+transformers
+torch
+nltk
+gradio
+sentencepiece
+sacremoses