Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the fine-tuned model and tokenizer
+model_name = "Addaci/byt5-small-finetuned-yiddish-experiment-10"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Define the translation function
+def translate_yiddish_to_english(input_text):
+    # Add task instruction to the input
+    prompt = "Translate this Yiddish text in Hebrew script into English text in English script:\n"
+    input_ids = tokenizer(prompt + input_text, return_tensors="pt", truncation=True).input_ids
+    output_ids = model.generate(input_ids, max_length=512)
+    translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return translated_text
+
+# Gradio Interface
+with gr.Blocks() as interface:
+    gr.Markdown("### Yiddish-to-English Translation Tool")
+    gr.Markdown("Enter a line of Yiddish text in Hebrew script to translate it into English.")
+    
+    with gr.Row():
+        input_box = gr.Textbox(label="Input Yiddish Text (Hebrew Script)", lines=1, rtl=True, elem_id="input_box")
+        output_box = gr.Textbox(label="Output English Translation (English Script)", lines=1, rtl=False, elem_id="output_box")
+    
+    translate_button = gr.Button("Translate")
+    translate_button.click(translate_yiddish_to_english, inputs=[input_box], outputs=[output_box])
+
+# Launch the interface
+interface.launch()