Update app.py

app.py

@@ -1,44 +1,30 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 import json
 import os
-
-# Ensure Hugging Face API token is set
 os.environ["HF_TOKEN"] = os.getenv('hf')
 
-# Load the tokenizer and model from Hugging Face
-model_name = "Darwin29/lumina-translate"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-# Mapping for language codes
-lang_codes = {
-    "English to Iban": ("eng_Latn", "iba_Latn"),
-    "Iban to English": ("iba_Latn", "eng_Latn")
-}
+# Load the tokenizer and model from Hugging Face (authentication handled via token)
+tokenizer = AutoTokenizer.from_pretrained("Darwin29/lumina-translate")
+model = AutoModelForSeq2SeqLM.from_pretrained("Darwin29/lumina-translate")
 
-# Translation function
+# Translation function (English to Iban and Iban to English)
 def translate_text(text, translation_direction):
-    if translation_direction not in lang_codes:
-        return json.dumps({"error": "Invalid translation direction"})
-
-    src_lang, tgt_lang = lang_codes[translation_direction]
-
-    # Tokenize input text
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
-
-    # Generate translation
-    outputs = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids(f"[{tgt_lang}]"))
-    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    # Return JSON response
-    return json.dumps({
-        "input_text": text,
-        "translated_text": translated_text,
-        "language": f"{src_lang} to {tgt_lang}"
-    }, indent=4)
-
-# Define the Gradio interface
+    if translation_direction == "English to Iban":
+        # Translate English to Iban
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+        outputs = model.generate(**inputs)
+        translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return json.dumps({"input_text": text, "translated_text": translated_text, "language": "en-to-iban"})
+    
+    elif translation_direction == "Iban to English":
+        # Translate Iban to English (you'll need a reverse model or translation setup here)
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+        outputs = model.generate(**inputs)
+        translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return json.dumps({"input_text": text, "translated_text": translated_text, "language": "iban-to-en"})
+
+# Define the Gradio interface with a dropdown to select translation direction
 iface = gr.Interface(
     fn=translate_text,
     inputs=[
@@ -47,8 +33,8 @@ iface = gr.Interface(
     ],
     outputs=gr.JSON(),
     title="English-Iban Translator",
-    description="This app translates between English and Iban. Choose the translation direction and enter your text."
+    description="This app translates between English and Iban. Choose the translation direction."
 )
 
-# Launch the app with a shareable link
-iface.launch(share=True)
+# Launch the app
+iface.launch()