Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import os
+from datetime import datetime
+from functools import lru_cache
+import torch
+
+# Language codes
+LANGUAGE_CODES = {
+    "English": "eng_Latn", "Korean": "kor_Hang", "Japanese": "jpn_Jpan", "Chinese": "zho_Hans",
+    "Spanish": "spa_Latn", "French": "fra_Latn", "German": "deu_Latn", "Russian": "rus_Cyrl",
+    "Portuguese": "por_Latn", "Italian": "ita_Latn", "Burmese": "mya_Mymr", "Thai": "tha_Thai"
+}
+
+# Translation history class
+class TranslationHistory:
+    def __init__(self):
+        self.history = []
+
+    def add(self, src, translated, src_lang, tgt_lang):
+        self.history.insert(0, {
+            "source": src, "translated": translated,
+            "src_lang": src_lang, "tgt_lang": tgt_lang,
+            "timestamp": datetime.now().isoformat()
+        })
+        if len(self.history) > 100:
+            self.history.pop()
+
+    def get(self):
+        return self.history
+
+    def clear(self):
+        self.history = []
+
+# Initialize history
+history = TranslationHistory()
+
+# Load model and tokenizer
+model_name = "facebook/nllb-200-distilled-600M"
+try:
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+except Exception as e:
+    raise RuntimeError(f"Failed to load model: {e}")
+
+# Cache translation
+@lru_cache(maxsize=512)
+def cached_translate(text, src_lang, tgt_lang, max_length=128, temperature=0.7):
+    if not text.strip():
+        return ""
+    try:
+        src_code = LANGUAGE_CODES.get(src_lang, src_lang)
+        tgt_code = LANGUAGE_CODES.get(tgt_lang, tgt_lang)
+
+        input_tokens = tokenizer(text, return_tensors="pt", padding=True)
+        input_tokens = {k: v.to(device) for k, v in input_tokens.items()}
+
+        forced_bos_token_id = tokenizer.convert_tokens_to_ids(tgt_code)
+
+        output = model.generate(
+            **input_tokens,
+            forced_bos_token_id=forced_bos_token_id,
+            max_length=max_length,
+            temperature=temperature,
+            num_beams=5,
+            early_stopping=True
+        )
+        result = tokenizer.decode(output[0], skip_special_tokens=True)
+        history.add(text, result, src_lang, tgt_lang)
+        return result
+    except Exception as e:
+        return f"Translation error: {e}"
+
+# Swap languages
+swap_langs = lambda src, tgt: (tgt, src)
+
+# Translate file
+def translate_file(file, src_lang, tgt_lang, max_length, temperature):
+    try:
+        lines = file.decode("utf-8").splitlines()
+        translated = [cached_translate(line, src_lang, tgt_lang, max_length, temperature) for line in lines if line.strip()]
+        return "\n".join(translated)
+    except Exception as e:
+        return f"File translation error: {e}"
+
+# Custom CSS to improve UI
+gradio_style = """
+.gr-button { border-radius: 12px !important; padding: 10px 20px !important; font-weight: bold; }
+textarea, input[type=text] { border: 2px solid #00ADB5 !important; border-radius: 10px; transition: 0.2s; }
+textarea:focus, input[type=text]:focus { border-color: #FF5722 !important; box-shadow: 0 0 8px #FF5722 !important; }
+"""
+
+with gr.Blocks(css=gradio_style, theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🌍 PolyLinguaAI: Translate Across Worlds
+    Translate instantly between 12+ languages using Facebook's NLLB model.
+    """)
+
+    with gr.Tab("🌐 Text Translator"):
+        with gr.Row():
+            src_lang = gr.Dropdown(list(LANGUAGE_CODES.keys()), label="🌐 From", value="English")
+            swap = gr.Button("⇄")
+            tgt_lang = gr.Dropdown(list(LANGUAGE_CODES.keys()), label="🎯 To", value="Korean")
+
+        with gr.Row():
+            input_text = gr.Textbox(lines=3, label="✍️ Input Text")
+            output_text = gr.Textbox(lines=3, label="📤 Translated Output", interactive=False)
+
+        with gr.Row():
+            translate = gr.Button("🚀 Translate", variant="primary")
+            clear = gr.Button("🧽 Clear")
+
+        with gr.Accordion("⚙️ Advanced Settings", open=False):
+            max_length = gr.Slider(10, 512, value=128, step=1, label="Max Length")
+            temperature = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature")
+
+        with gr.Accordion("📜 Translation History", open=False):
+            history_json = gr.JSON(label="Recent Translations")
+            with gr.Row():
+                refresh = gr.Button("🔄 Refresh")
+                clear_history = gr.Button("🧹 Clear History")
+
+    with gr.Tab("📁 File Translator"):
+        file_input = gr.File(label="📂 Upload .txt File", file_types=[".txt"])
+        file_src = gr.Dropdown(list(LANGUAGE_CODES.keys()), label="📌 From", value="English")
+        file_tgt = gr.Dropdown(list(LANGUAGE_CODES.keys()), label="📌 To", value="Korean")
+        file_translate = gr.Button("📄 Translate File", variant="primary")
+        file_result = gr.Textbox(label="📑 File Output", lines=10, interactive=False)
+
+        with gr.Accordion("⚙️ Advanced Settings", open=False):
+            f_max_length = gr.Slider(10, 512, value=128, step=1, label="Max Length")
+            f_temp = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature")
+
+    # Events
+    translate.click(cached_translate, [input_text, src_lang, tgt_lang, max_length, temperature], output_text)
+    clear.click(lambda: ("", ""), None, [input_text, output_text])
+    swap.click(swap_langs, [src_lang, tgt_lang], [src_lang, tgt_lang])
+    refresh.click(lambda: history.get(), None, history_json)
+    clear_history.click(lambda: history.clear() or [], None, history_json)
+    file_translate.click(lambda file, src, tgt, ml, t: translate_file(file.read(), src, tgt, ml, t),
+                         [file_input, file_src, file_tgt, f_max_length, f_temp], file_result)
+
+    gr.Markdown(f"""
+    ### 🛠 Model Info
+    - Model: `{model_name}`
+    - Device: `{device}`
+    - Cached Translations: 512
+    """)
+
+if __name__ == "__main__":
+    demo.launch(share=True)