v1.0

.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+models

app.py

@@ -0,0 +1,78 @@
+import gradio as gr
+from model import run
+from languages import language_names
+
+
+
+example_text = "Now let's make my mum's favourite. So three mars bars into the pan. Then we add the tuna and just stir for a bit, just let the chocolate and fish infuse. A sprinkle of olive oil and some tomato ketchup. Now smell that. Oh boy this is going to be incredible."
+css = """
+body {
+    background: linear-gradient(135deg, #0f2027, #203a43, #2c5364);
+}
+.gradio-container {
+    font-family: 'Inter', sans-serif;
+}
+textarea {
+    border-radius: 12px !important;
+    font-size: 16px !important;
+}
+button {
+    border-radius: 999px !important;
+    font-weight: 600 !important;
+}
+"""
+
+with gr.Blocks(
+    theme=gr.themes.Soft(
+        primary_hue="indigo",
+        secondary_hue="cyan",
+        neutral_hue="slate"
+    ),
+    css=css
+) as app:
+
+    gr.Markdown(
+        """
+        # 🌍 Universal Translator
+        Translate texts quickly between multiple languages.
+        Simple and functional.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            source_language = gr.Dropdown(
+                choices=language_names,
+                value="English",
+                label="Source Language"
+            )
+            input_text = gr.Textbox(
+                label="Original text",
+                placeholder="Please enter the text...",
+                lines=8,
+                value=example_text
+            )
+
+        with gr.Column(scale=1):
+            target_language = gr.Dropdown(
+                choices=language_names,
+                value="Portuguese",
+                label="Target Language"
+            )
+            output_text = gr.Textbox(
+                label="Translation",
+                lines=8,
+                interactive=False
+            )
+
+    translate_btn = gr.Button("Translate ✨")
+
+    translate_btn.click(
+        run,
+        inputs=[input_text, target_language],
+        outputs=output_text
+    )
+
+
+if __name__ == "__main__":
+    app.queue(max_size=10).launch(mcp_server=True)

languages.py

@@ -0,0 +1,42 @@
+languages = {
+    "Chinese": "zh",
+    "English": "en",
+    "French": "fr",
+    "Portuguese": "pt",
+    "Spanish": "es",
+    "Japanese": "ja",
+    "Turkish": "tr",
+    "Russian": "ru",
+    "Arabic": "ar",
+    "Korean": "ko",
+    "Thai": "th",
+    "Italian": "it",
+    "German": "de",
+    "Vietnamese": "vi",
+    "Malay": "ms",
+    "Indonesian": "id",
+    "Filipino": "tl",
+    "Hindi": "hi",
+    "Traditional Chinese": "zh-Hant",
+    "Polish": "pl",
+    "Czech": "cs",
+    "Dutch": "nl",
+    "Khmer": "km",
+    "Burmese": "my",
+    "Persian": "fa",
+    "Gujarati": "gu",
+    "Urdu": "ur",
+    "Telugu": "te",
+    "Marathi": "mr",
+    "Hebrew": "he",
+    "Bengali": "bn",
+    "Tamil": "ta",
+    "Ukrainian": "uk",
+    "Tibetan": "bo",
+    "Kazakh": "kk",
+    "Mongolian": "mn",
+    "Uyghur": "ug",
+    "Cantonese": "yue"
+}
+
+language_names = list(languages.keys())

model.py

@@ -0,0 +1,34 @@
+from huggingface_hub import snapshot_download
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+model_id = "tencent/HY-MT1.5-1.8B"
+local_dir = "./models"
+model_path = snapshot_download(model_id, local_dir=local_dir)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path)
+
+def run(
+    text: str = "It’s on the house.",
+    target_language: str = "Portuguese",
+): 
+    messages = [
+        {
+            "role": "user", 
+            "content": f"Translate the following segment into {target_language}, without additional explanation.\n\n{text}"
+        },
+    ]
+    tokenized_chat = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=False,
+        return_tensors="pt"
+    )
+
+    outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=2048)
+    output_text = tokenizer.decode(outputs[0])
+    return output_text
+
+if __name__ == "__main__":
+    translated_text = run("Now let's make my mum's favourite. So three mars bars into the pan. Then we add the tuna and just stir for a bit, just let the chocolate and fish infuse. A sprinkle of olive oil and some tomato ketchup. Now smell that. Oh boy this is going to be incredible.")
+    print(translated_text)

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+huggingface_hub
+transformers
+torch