Create app.py

app.py

@@ -0,0 +1,37 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+
+model_name = "Helsinki-NLP/opus-mt-en-ur"
+
+print("Downloading tokenizer and model directly...")
+# Loading the tokenizer and model directly bypasses the buggy pipeline
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+print("Model loaded successfully!")
+
+def translate_en_to_ur(text):
+    if not text.strip():
+        return ""
+    
+    # 1. Tokenize: Convert the text into numbers the model understands
+    inputs = tokenizer(text, return_tensors="pt", padding=True)
+    
+    # 2. Generate: The model predicts the Urdu translation tokens
+    translated_tokens = model.generate(**inputs)
+    
+    # 3. Decode: Convert the predicted tokens back into readable text
+    result = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    return result
+
+# Build the Gradio Interface
+interface = gr.Interface(
+    fn=translate_en_to_ur,
+    inputs=gr.Textbox(lines=5, placeholder="Enter English text here...", label="English Input"),
+    outputs=gr.Textbox(lines=5, label="Urdu Translation"),
+    title="English to Urdu Translator 🇵🇰",
+    description="Enter any English text and the AI will translate it into Urdu.",
+    theme="default"
+)
+
+# Launch the app
+interface.launch(share=True)