Update app.py

app.py

@@ -1,32 +1,73 @@
+import os
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "0"
 
-# ---- English → Urdu Translator App ----
 from transformers import MBart50TokenizerFast, MBartForConditionalGeneration
 import gradio as gr
 
-# Load model and tokenizer
+# ---- Load model and tokenizer ----
 model_name = "abdulwaheed1/english-to-urdu-translation-mbart"
-tokenizer = MBart50TokenizerFast.from_pretrained(model_name, src_lang="en_XX", tgt_lang="ur_PK")
-model = MBartForConditionalGeneration.from_pretrained(model_name)
-
-# Translation function
-def translate_to_urdu(text):
-    if not text.strip():
-        return "Please enter some English text."
-    inputs = tokenizer(text, return_tensors="pt", padding=True)
-    translated_tokens = model.generate(**inputs)
-    urdu_output = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
-    return urdu_output
-
-# Create a simple dashboard with Gradio
-app = gr.Interface(
-    fn=translate_to_urdu,
-    inputs=gr.Textbox(label="Enter English Text", placeholder="Type your English sentence here..."),
-    outputs=gr.Textbox(label="Urdu Translation"),
-    title="🕌 English → Urdu Translator",
-    description="This is my First fine-tuned mBART model to translate English sentences into Urdu.",
-    theme="soft"
-)
-
-# Launch app
-app.launch(share=True)   # use share=True to get a public link accessible in Chrome
 
+with gr.Blocks(title="English → Urdu Translator") as app:
+    gr.Markdown(
+        """
+        <div style="text-align:center; padding: 10px;">
+            <h1 style="color:#1e3799;">🕌 English → Urdu Translator</h1>
+            <p style="font-size:18px;">Translate English sentences into beautiful Urdu text using a fine-tuned mBART model.</p>
+        </div>
+        """,
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("⏳ <i>Loading model... please wait 10–20 seconds on first launch.</i>")
+    
+    # Load the model only once (outside function)
+    tokenizer = MBart50TokenizerFast.from_pretrained(model_name, src_lang="en_XX", tgt_lang="ur_PK")
+    model = MBartForConditionalGeneration.from_pretrained(model_name)
+
+    # ---- Translation function ----
+    def translate_to_urdu(text):
+        if not text.strip():
+            return "Please enter some English text."
+        inputs = tokenizer(text, return_tensors="pt", padding=True)
+        translated_tokens = model.generate(**inputs)
+        urdu_output = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+        return urdu_output
+
+    # ---- Gradio Interface ----
+    input_box = gr.Textbox(
+        label="Enter English Text",
+        placeholder="Type your English sentence here...",
+        lines=2,
+    )
+    output_box = gr.Textbox(
+        label="Urdu Translation",
+        lines=2,
+    )
+
+    example_texts = [
+        ["How are you?"],
+        ["Today is a beautiful day."],
+        ["Where are you going?"],
+        ["I am learning Artificial Intelligence."],
+        ["Thank you very much!"]
+    ]
+
+    gr.Interface(
+        fn=translate_to_urdu,
+        inputs=input_box,
+        outputs=output_box,
+        examples=example_texts,
+        title="🕌 English → Urdu Translator",
+        description="Built by Khurram Basharat — powered by mBART model fine-tuned for English to Urdu translation.",
+        theme="soft",
+        css="""
+            body {
+                background: linear-gradient(to bottom right, #dff9fb, #c7ecee);
+                font-family: 'Segoe UI', sans-serif;
+            }
+            .gr-button-primary {
+                background-color: #1e3799 !important;
+            }
+        """,
+    ).launch()