Update app.py

app.py

@@ -1,6 +1,52 @@
 import gradio as gr
+from transformers import pipeline
 
-gr.load(
-   "models/facebook/nllb-200-1.3B",
-   provider="hf-inference",
-).launch()
+# Create a translation pipeline
+model_name = "facebook/nllb-200-1.3B"
+translator = pipeline("translation", model=model_name)
+
+# Define available languages
+languages = {
+    "English": "eng_Latn",
+    "French": "fra_Latn",
+    "Spanish": "spa_Latn",
+    "German": "deu_Latn",
+    "Chinese": "zho_Hans",
+    "Arabic": "ara_Arab",
+    "Russian": "rus_Cyrl",
+    "Hindi": "hin_Deva",
+    "Japanese": "jpn_Jpan"
+}
+
+def translate(text, source_lang, target_lang):
+    if not text:
+        return ""
+    
+    source_code = languages.get(source_lang)
+    target_code = languages.get(target_lang)
+    
+    # NLLB requires specific format for translation
+    translation = translator(
+        text, 
+        src_lang=source_code, 
+        tgt_lang=target_code,
+        max_length=400
+    )
+    
+    return translation[0]["translation_text"]
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Input Text", lines=5),
+        gr.Dropdown(list(languages.keys()), label="Source Language", value="English"),
+        gr.Dropdown(list(languages.keys()), label="Target Language", value="French")
+    ],
+    outputs=gr.Textbox(label="Translated Text", lines=5),
+    title="NLLB-200 Multilingual Translation",
+    description="Translate text between multiple languages using Facebook's NLLB-200 model."
+)
+
+if __name__ == "__main__":
+    demo.launch()