adding code comments

app.py

@@ -6,59 +6,78 @@ from huggingface_hub import hf_hub_download
 from piper import PiperVoice 
 from transformers import pipeline
 
-# Load the NSFW classifier model
+# Import necessary libraries:
+# gradio for creating the web interface,
+# wave for handling WAV audio format,
+# numpy for numerical operations,
+# BytesIO for in-memory byte handling,
+# huggingface_hub for downloading models from the Hugging Face Hub,
+# PiperVoice for the text-to-speech functionality,
+# pipeline from transformers for the NSFW classifier.
+
+# Load the NSFW classifier model using Hugging Face's pipeline
 nsfw_detector = pipeline("text-classification", model="michellejieli/NSFW_text_classifier")
 
 def synthesize_speech(text):
-    # Check for NSFW content
+    # Function to synthesize speech from text.
+
+    # First, check if the input text contains NSFW content.
     nsfw_result = nsfw_detector(text)
     if nsfw_result[0]['label'] == 'NSFW':
-        # Path to your default error audio file
+        # If NSFW content is detected, load a pre-recorded error message.
         error_audio_path = hf_hub_download(repo_id="DLI-SLQ/speaker_01234", filename="error_audio.wav")
         with open(error_audio_path, 'rb') as error_audio_file:
-            error_audio = error_audio_file.read()   
+            error_audio = error_audio_file.read()
         return error_audio, "NSFW content detected. Cannot process."
 
-
+    # If the content is safe, proceed with speech synthesis.
+    # Download the model and configuration from Hugging Face Hub.
     model_path = hf_hub_download(repo_id="DLI-SLQ/speaker_01234", filename="speaker__01234_model.onnx")
     config_path = hf_hub_download(repo_id="DLI-SLQ/speaker_01234", filename="speaker__1234_model.onnx.json")
     
-
+    # Load the PiperVoice model for speech synthesis.
     voice = PiperVoice.load(model_path, config_path)
     
-    # Create an in-memory buffer for the WAV file
+    # Create a BytesIO buffer to hold the synthesized WAV file in memory.
     buffer = BytesIO()
     with wave.open(buffer, 'wb') as wav_file:
+        # Set WAV file properties: sample rate, bit depth, and mono channel.
         wav_file.setframerate(voice.config.sample_rate)
         wav_file.setsampwidth(2)  # 16-bit
         wav_file.setnchannels(1)  # mono
 
-        # Synthesize speech
+        # Use the PiperVoice model to synthesize speech from the text.
         voice.synthesize(text, wav_file)
 
-    # Convert buffer to NumPy array for Gradio output
+    # Convert the buffer content to a NumPy array, then to bytes for Gradio output.
     buffer.seek(0)
     audio_data = np.frombuffer(buffer.read(), dtype=np.int16)
     return audio_data.tobytes(), None
-    
 
-# Gradio Interface
+# Set up the Gradio interface.
 with gr.Blocks(theme=gr.themes.Base()) as blocks:
+    # Create a user-friendly markdown title and description.
     gr.Markdown("# Text to Speech Synthesizer")
     gr.Markdown("Enter text to synthesize it into speech using models from the State Library of Queensland's collection using Piper.")
+    
+    # Define Gradio interface components: input textbox, audio output, and output textbox.
     input_text = gr.Textbox(label="Input Text")
     output_audio = gr.Audio(label="Synthesized Speech", type="numpy")
-    output_text = gr.Textbox(label="Output Text", visible=True)  # Make this visible for error messages
-    submit_button = gr.Button("Synthesize")
+    output_text = gr.Textbox(label="Output Text", visible=True)  # Visible for error messages
 
+    # Define a function to process the input text and produce outputs.
     def process_and_output(text):
         audio, message = synthesize_speech(text)
         if message:
-            return None, message  # Return None for audio and the error message
+            # If there's a message (e.g., an error message), return None for audio and the message.
+            return None, message
         else:
-            return audio, None  # Return the audio data and None for the message
+            # Otherwise, return the synthesized audio and None for the message.
+            return audio, None
 
+    # Link the processing function to the Gradio interface button.
+    submit_button = gr.Button("Synthesize")
     submit_button.click(process_and_output, inputs=input_text, outputs=[output_audio, output_text])
 
-# Run the app
+# Launch the Gradio web application.
 blocks.launch()