Create app.py

app.py

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+# Install dependencies (Run this in Google Colab),!pip install transformers gradio --quiet
+
+# Import libraries
+from transformers import MarianMTModel, MarianTokenizer
+import gradio as gr
+
+# Define model names for English↔Urdu translation
+en_to_ur_model_name = "Helsinki-NLP/opus-mt-en-ur"
+ur_to_en_model_name = "Helsinki-NLP/opus-mt-ur-en"
+
+# Load models and tokenizers
+en_to_ur_tokenizer = MarianTokenizer.from_pretrained(en_to_ur_model_name)
+en_to_ur_model = MarianMTModel.from_pretrained(en_to_ur_model_name)
+
+ur_to_en_tokenizer = MarianTokenizer.from_pretrained(ur_to_en_model_name)
+ur_to_en_model = MarianMTModel.from_pretrained(ur_to_en_model_name)
+
+# Translation functions
+def translate_en_to_ur(text):
+    inputs = en_to_ur_tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    translated = en_to_ur_model.generate(**inputs)
+    return en_to_ur_tokenizer.decode(translated[0], skip_special_tokens=True)
+
+def translate_ur_to_en(text):
+    inputs = ur_to_en_tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    translated = ur_to_en_model.generate(**inputs)
+    return ur_to_en_tokenizer.decode(translated[0], skip_special_tokens=True)
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## 🈯 English ↔ Urdu Translator")
+    with gr.Tab("English ➜ Urdu"):
+        en_input = gr.Textbox(label="Enter English Text")
+        en_output = gr.Textbox(label="Translated Urdu Text")
+        en_translate_btn = gr.Button("Translate to Urdu")
+        en_translate_btn.click(translate_en_to_ur, en_input, en_output)
+
+    with gr.Tab("Urdu ➜ English"):
+        ur_input = gr.Textbox(label="اردو متن داخل کریں")
+        ur_output = gr.Textbox(label="Translated English Text")
+        ur_translate_btn = gr.Button("Translate to English")
+        ur_translate_btn.click(translate_ur_to_en, ur_input, ur_output)
+
+# Launch the app (for local testing or in Colab)
+demo.launch()