Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+
+# Load the translation model and tokenizer from Hugging Face
+# Here, we use the "Helsinki-NLP" translation models
+model_name = "Helsinki-NLP/opus-mt-en-fr"  # Example model for English to French
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+translator = pipeline("translation", model=model, tokenizer=tokenizer)
+
+# Define the translation function
+def translate_text(text, target_language):
+    model_name = f"Helsinki-NLP/opus-mt-en-{target_language}"
+    translator = pipeline("translation", model=model_name)
+    translation = translator(text)[0]['translation_text']
+    return translation
+
+# Set up the Gradio interface
+languages = {
+    "en": "English",
+    "es": "Spanish",
+    "fr": "French",
+    "de": "German",
+    "zh": "Chinese (Mandarin)",
+    "ja": "Japanese",
+    "ko": "Korean",
+    "it": "Italian",
+    "pt": "Portuguese",
+    "ru": "Russian",
+    "hi": "Hindi",
+    "ar": "Arabic",
+    "nl": "Dutch",
+    "tr": "Turkish",
+    "el": "Greek",
+    "ur": "Urdu"
+}
+
+
+iface = gr.Interface(
+    fn=translate_text,
+    inputs=[
+        gr.inputs.Textbox(label="Enter text to translate"),
+        gr.inputs.Dropdown(list(languages.keys()), label="Target Language", type="value")
+    ],
+    outputs="text",
+    live=True,
+    title="Text Translator",
+    description="Translate text into multiple languages using Hugging Face models."
+)
+
+iface.launch()