Create app.py

app.py

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+import gradio as gr
+from transformers import MarianMTModel, MarianTokenizer, pipeline
+import pyttsx3  # Text-to-Speech for feedback
+
+# Initialize Translation Model
+model_name = 'Helsinki-NLP/opus-mt-en-ur'
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+translator = pipeline('translation', model=model, tokenizer=tokenizer)
+
+# Initialize Text-to-Speech Engine
+engine = pyttsx3.init()
+
+# Function to translate text
+def translate_text(input_text, lang_direction):
+    if lang_direction == 'English to Urdu':
+        translation = translator(input_text, max_length=100)[0]['translation_text']
+    else:
+        translation = translator(input_text, max_length=100)[0]['translation_text']
+    return translation
+
+# Function to provide pronunciation feedback (using TTS)
+def provide_pronunciation_feedback(text, lang):
+    engine.say(text)
+    engine.runAndWait()
+
+# Gradio Interface
+def translate_and_practice(sentence, lang_direction):
+    translation = translate_text(sentence, lang_direction)
+    provide_pronunciation_feedback(translation, 'ur' if lang_direction == 'English to Urdu' else 'en')
+    return translation
+
+# UI with Gradio
+iface = gr.Interface(fn=translate_and_practice,
+                     inputs=[gr.Textbox(label="Enter Sentence"), 
+                             gr.Radio(choices=["English to Urdu", "Urdu to English"], label="Language Direction")],
+                     outputs="text",
+                     live=True,  # To enable real-time updates
+                     title="English-Urdu Language Tutor",
+                     description="Enter a sentence and get the translation with pronunciation feedback.")
+iface.launch()