Update app.py

app.py

@@ -106,7 +106,7 @@ def transliterate(word):
     # TinyLLaMA prediction
     if has_llm:
         try:
-            prompt = f"Output only the Hindi Devanagari transliteration of: {word}"
+            prompt = f"Transliterate the following English word to Hindi (Devanagari script).\nEnglish word: {word}\nHindi transliteration:"
             inputs = llm_tokenizer(prompt, return_tensors="pt").to(device)
             
             with torch.no_grad():
@@ -122,14 +122,7 @@ def transliterate(word):
             
             generated = llm_tokenizer.decode(output_ids[0], skip_special_tokens=True)
             llm_pred = generated.replace(prompt, "").strip()
-            
-            # Remove common prefixes and colons
-            for prefix in ["Devanagari script:", "Hindi transliteration:", "Transliteration:", "Translation:"]:
-                llm_pred = llm_pred.replace(prefix, "").strip()
-            
-            # Remove leading/trailing colons and whitespace
-            llm_pred = llm_pred.lstrip(":").strip()
-            
+            llm_pred = ''.join(c for c in llm_pred if not (c.isascii() and c.isalpha()) and c.strip())
         except Exception as e:
             llm_pred = f"Error: {str(e)[:50]}"
     else: