Create app.py

app.py

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+# prompt: create a gradio for this
+
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load tokenizer and model
+model_name = "jsbeaudry/creole-translation-nllb-600M"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+# Assuming the necessary imports and model loading from the previous code are in place
+
+def translate_text(text):
+    # Set the source and target language codes
+    src_lang = "eng_Latn"
+    tgt_lang = "hat_Latn"
+    tokenizer_ = tokenizer
+    model_ = model
+
+    # Set tokenizer to source language
+    tokenizer_.src_lang = src_lang
+
+    # Tokenize the input
+    inputs = tokenizer_(text, return_tensors="pt")
+
+    # Find the BOS token ID for the target language
+    forced_bos_token_id = tokenizer_.convert_tokens_to_ids(tgt_lang)
+
+    # Move model to GPU if available
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model_ = model_.to(device)
+    inputs = inputs.to(device)
+
+    # Generate translation
+    generated_tokens = model_.generate(
+        **inputs,
+        forced_bos_token_id=forced_bos_token_id,
+        max_length=100
+    )
+
+    # Decode and print
+    translated = tokenizer_.batch_decode(generated_tokens, skip_special_tokens=True)
+    return translated[0]
+
+iface = gr.Interface(
+    fn=translate_text,
+    inputs=gr.Textbox(lines=5, placeholder="Enter text to translate"),
+    outputs="text",
+    title="English to Haitian Creole Translation",
+    description="Translate English text to Haitian Creole using a fine-tuned NLLB model."
+)
+
+iface.launch()