Update app.py

app.py

@@ -11,9 +11,12 @@ BN_TO_EN_MODEL = "csebuetnlp/banglat5_nmt_bn_en"
 NORMALIZER_REPO = "https://github.com/csebuetnlp/normalizer.git"
 
 # --- Helper function to install/import normalizer ---
-# This ensures the normalizer is available.
-# In HF Spaces, requirements.txt is the primary method.
 normalizer_module = None
+dummy_normalizer_flag = False # Flag to indicate if dummy is used
+
+def dummy_normalize_func(text): # Define the dummy function clearly
+    raise RuntimeError("Normalizer library could not be loaded. Please check installation and logs.")
+
 try:
     from normalizer import normalize as normalize_fn_imported
     normalizer_module = normalize_fn_imported
@@ -21,8 +24,6 @@ try:
 except ImportError:
     print(f"Normalizer library not found. Attempting to install from {NORMALIZER_REPO}...")
     try:
-        # This command installs the package directly from git.
-        # The #egg=normalizer part helps pip identify the package name.
         subprocess.check_call([sys.executable, "-m", "pip", "install", f"git+{NORMALIZER_REPO}#egg=normalizer"])
         from normalizer import normalize as normalize_fn_imported_after_install
         normalizer_module = normalize_fn_imported_after_install
@@ -30,10 +31,9 @@ except ImportError:
     except Exception as e:
         print(f"Failed to install or import normalizer: {e}")
         print("Please ensure 'git+https://github.com/csebuetnlp/normalizer.git#egg=normalizer' is in your requirements.txt for Hugging Face Spaces.")
-        # Fallback to a dummy function if installation fails, so the app can still load and show an error.
-        def dummy_normalize(text):
-            raise RuntimeError("Normalizer library could not be loaded. Please check installation.")
-        normalizer_module = dummy_normalize
+        normalizer_module = dummy_normalize_func # Assign the actual dummy function
+        dummy_normalizer_flag = True
+
 
 # --- Model Loading (Globally, when the script starts) ---
 sylheti_to_bn_pipe = None
@@ -63,8 +63,6 @@ try:
 
 except Exception as e:
     print(f"FATAL: Error loading one or more models: {e}")
-    # To prevent the app from crashing entirely if models don't load,
-    # but it will show errors during translation.
     sylheti_to_bn_pipe = None
     bn_to_en_model = None
     bn_to_en_tokenizer = None
@@ -78,7 +76,9 @@ def translate_sylheti_to_english_gradio(sylheti_text_input):
         return "Error: Sylheti-to-Bengali model not loaded. Check logs.", ""
     if not bn_to_en_model or not bn_to_en_tokenizer:
         return "Error: Bengali-to-English model not loaded. Check logs.", ""
-    if normalizer_module is None or isinstance(normalizer_module, type(lambda:0)) and normalizer_module.__name__ == 'dummy_normalize': # Check if it's the dummy
+    
+    # Check if the normalizer is the dummy function
+    if dummy_normalizer_flag or normalizer_module is None:
         return "Error: Bengali normalizer library not available. Check logs.", ""
 
 
@@ -99,18 +99,24 @@ def translate_sylheti_to_english_gradio(sylheti_text_input):
     except Exception as e:
         print(f"Error during Sylheti to Bengali translation: {e}")
         bengali_text_intermediate = f"Sylheti->Bengali Error: {str(e)}"
-        return bengali_text_intermediate, english_text_final # Stop if first step fails
+        return bengali_text_intermediate, english_text_final
 
     # Step 2: Bengali → English
     try:
         print(f"Normalizing and translating Bengali to English: '{bengali_text_intermediate}'")
-        normalized_bn_text = normalizer_module(bengali_text_intermediate)
+        # Ensure normalizer_module is callable before calling
+        if callable(normalizer_module):
+            normalized_bn_text = normalizer_module(bengali_text_intermediate)
+        else:
+            # This case should ideally be caught by the check above, but as a safeguard:
+            raise RuntimeError("Normalizer function is not callable.")
+            
         print(f"Normalized Bengali: '{normalized_bn_text}'")
 
         input_ids = bn_to_en_tokenizer(
             normalized_bn_text,
             return_tensors="pt"
-        ).input_ids.to(model_device) # Ensure tensor is on the same device
+        ).input_ids.to(model_device)
 
         generated_tokens = bn_to_en_model.generate(
             input_ids,
@@ -152,11 +158,10 @@ iface = gr.Interface(
         ["আফনে ভালা আছনি?"]
     ],
     allow_flagging="never",
-    theme=gr.themes.Soft() # Optional: adds a bit of styling
+    cache_examples=False, # Explicitly disable example caching
+    theme=gr.themes.Soft()
 )
 
 # --- Launch the Gradio app ---
 if __name__ == "__main__":
-    # When running locally, this launches the server.
-    # In Hugging Face Spaces, the `app.py` is typically run by their infrastructure.
     iface.launch()