initial commit

app.py

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+import gradio as gr
+from transformers import MarianMTModel, MarianTokenizer
+import torch
+
+# 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 text after detecting complete sentences.
+    """
+    if not model_name or not text.strip():
+        return "Select a model and provide text."
+
+    try:
+        # Load the selected model
+        model, tokenizer = load_model(model_name)
+
+        # Tokenize the input text
+        tokens = tokenizer(text, return_tensors="pt", padding=True)
+        if torch.cuda.is_available():
+            tokens = {k: v.to('cuda') for k, v in tokens.items()}
+
+        # Generate translated tokens
+        translated = model.generate(**tokens)
+
+        # Decode translation
+        return tokenizer.decode(translated[0], skip_special_tokens=True)
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def process_input(model_name, text):
+    """
+    Process user input to detect completed sentences and translate in real-time.
+    """
+    sentences = text.strip().split('. ')
+    translations = []
+    for sentence in sentences:
+        if sentence.endswith('.') or sentence.endswith('!') or sentence.endswith('?'):
+            translations.append(translate_text(model_name, sentence))
+        else:
+            # If the sentence is incomplete, skip translation
+            translations.append(f"Waiting for completion: {sentence}")
+    return '\n'.join(translations)
+
+# Define Gradio Interface
+with gr.Blocks() as app:
+    gr.Markdown("## 🌍 Real-Time Sentence Translation")
+    gr.Markdown("### Enter text in the textbox, and it will be translated after each sentence ends!")
+
+    model_dropdown = gr.Dropdown(
+        label="Select Translation Model",
+        choices=[
+            "Helsinki-NLP/opus-mt-tc-big-en-tr",  # English to Turkish
+            "Helsinki-NLP/opus-mt-tc-big-tr-en",  # Turkish to English
+            "Helsinki-NLP/opus-mt-tc-big-en-fr",  # English to French
+            "Helsinki-NLP/opus-mt-tc-big-fr-en",  # French to English
+            "Helsinki-NLP/opus-mt-en-de",        # English to German
+            "Helsinki-NLP/opus-mt-de-en",        # German to English
+            "Helsinki-NLP/opus-mt-tc-big-en-es", # English to Spanish
+            "Helsinki-NLP/opus-mt-es-en",        # Spanish to English
+            "Helsinki-NLP/opus-mt-tc-big-en-ar", # English to Arabic
+            "Helsinki-NLP/opus-mt-tc-big-ar-en", # Arabic to English
+            "Helsinki-NLP/opus-mt-en-ur",        # English to Urdu
+            "Helsinki-NLP/opus-mt-ur-en",        # Urdu to English
+            "Helsinki-NLP/opus-mt-en-hi",        # English to Hindi
+            "Helsinki-NLP/opus-mt-hi-en",        # Hindi to English
+            "Helsinki-NLP/opus-mt-en-zh",        # English to Chinese
+            "Helsinki-NLP/opus-mt-zh-en",        # Chinese to English",
+        ],
+        value="Helsinki-NLP/opus-mt-tc-big-en-tr",
+        interactive=True
+    )
+
+    input_textbox = gr.Textbox(
+        label="Enter Text:",
+        placeholder="Type here...",
+        lines=5,
+        interactive=True
+    )
+
+    output_textbox = gr.Textbox(
+        label="Translated Text:",
+        lines=5,
+        interactive=False
+    )
+
+    input_textbox.change(
+        fn=process_input,
+        inputs=[model_dropdown, input_textbox],
+        outputs=[output_textbox],
+    )
+
+# Launch Gradio App
+app.launch()

requirements.txt

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+gradio
+transformers
+torch