Create translation_app.py

translation_app.py

@@ -0,0 +1,56 @@
+import os
+import gradio as gr
+from langdetect import detect, LangDetectException
+from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+
+try:
+    from groq import Groq
+except Exception:
+    Groq = None
+
+# Config
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+GROQ_MODEL = os.getenv("GROQ_MODEL", "mixtral-8x7b-32768")
+
+groq_client = None
+if GROQ_API_KEY and Groq is not None:
+    try:
+        groq_client = Groq(api_key=GROQ_API_KEY)
+    except Exception:
+        pass
+
+m2m_model_name = "facebook/m2m100_418M"
+m2m_tokenizer = M2M100Tokenizer.from_pretrained(m2m_model_name)
+m2m_model = M2M100ForConditionalGeneration.from_pretrained(m2m_model_name)
+
+LANG_UI_TO_CODE = {"English": "en", "Spanish": "es", "French": "fr"}
+
+def call_m2m(user_text, target_code):
+    try:
+        src_code = detect(user_text)
+    except LangDetectException:
+        src_code = "en"
+    if src_code == target_code:
+        return user_text
+    m2m_tokenizer.src_lang = src_code
+    encoded = m2m_tokenizer(user_text, return_tensors="pt")
+    generated = m2m_model.generate(**encoded, forced_bos_token_id=m2m_tokenizer.get_lang_id(target_code))
+    return m2m_tokenizer.decode(generated[0], skip_special_tokens=True)
+
+def translate_text(user_text, target_lang_ui):
+    if not user_text.strip():
+        return "⚠️ Please enter text."
+    target_code = LANG_UI_TO_CODE.get(target_lang_ui, "en")
+    try:
+        return call_m2m(user_text, target_code)
+    except:
+        return "❌ Translation failed."
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🌐 Universal Translator")
+    with gr.Row():
+        txt = gr.Textbox(label="Enter your text", lines=6)
+        tgt = gr.Dropdown(choices=["English", "Spanish", "French"], value="English", label="Target Language")
+    out = gr.Textbox(label="Translated Output", lines=6)
+    btn = gr.Button("Translate")
+    btn.click(fn=translate_text, inputs=[txt, tgt], outputs=[out])