Update app.py

app.py

@@ -1,28 +1,28 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import MBart50TokenizerFast, MBartForConditionalGeneration
 
-# Load the conversational model
-def load_chat_model():
-    return pipeline("text-generation", model="microsoft/DialoGPT-medium")
+# Load the fine-tuned model
+model_name = "abdulwaheed1/urdu_to_english_translation_mbart"
+tokenizer = MBart50TokenizerFast.from_pretrained(model_name, src_lang="ur_PK", tgt_lang="en_XX")
+model = MBartForConditionalGeneration.from_pretrained(model_name)
 
-chat_model = load_chat_model()
-
-# Define the chat function
-def chat_with_bot(user_input):
-    # Generate response from the chatbot model
-    response = chat_model(user_input, max_length=100, num_return_sequences=1)
-    return response[0]['generated_text']
+def translate_urdu_to_english(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    # Generate translation
+    outputs = model.generate(**inputs)
+    # Decode the translated text
+    translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+    return translation
 
 # Create the Gradio interface
 iface = gr.Interface(
-    fn=chat_with_bot,
-    inputs=gr.Textbox(label="You:", placeholder="Type your message here..."),
-    outputs=gr.Textbox(label="Bot:"),
-    title="English Learning Chatbot",
-    description="Talk to the chatbot to practice English.",
-    theme="default"
+    fn=translate_urdu_to_english,
+    inputs=gr.inputs.Textbox(label="Input Urdu Text"),
+    outputs=gr.outputs.Textbox(label="Translated English Text"),
+    title="Urdu to English Translation",
+    description="Enter Urdu text to get the English translation."
 )
 
 # Launch the app
-if __name__ == "__main__":
-    iface.launch()
+iface.launch()