Update app.py

app.py

@@ -1,71 +1,68 @@
-# TTS Space: app.py
-# This code creates a dedicated API endpoint for our TTS model.
+# FINAL TTS Space: app.py
+# This version includes the fix for the MeCab/unidic dependency issue.
 import gradio as gr
-from TTS.api import TTS
 import torch
 import os
 
-# Set a default speaker file
-DEFAULT_SPEAKER_WAV = "tutor_voice.wav"
+# --- FIX for MeCab/unidic START ---
+# This command downloads the necessary Japanese dictionary for the TTS library.
+# It runs only once when the Space builds.
+print("Fix: Triggering unidic download...")
+os.system('python -m unidic download')
+print("Fix: Unidic download command executed.")
+# --- FIX for MeCab/unidic END ---
+
+from TTS.api import TTS
 
-# Check if CUDA (GPU) is available, otherwise use CPU.
-# On the free tier, this will always be "cpu".
+# --- Standard Application Code ---
+
+DEFAULT_SPEAKER_WAV = "tutor_voice.wav"
 device = "cuda" if torch.cuda.is_available() else "cpu"
 print(f"TTS Service: Using device: {device}")
 
-# Load the powerful XTTS-v2 model into memory.
-# This happens only once when the Space starts.
 print("TTS Service: Loading model...")
-tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)
-print("TTS Service: Model loaded successfully.")
+try:
+    tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)
+    print("TTS Service: Model loaded successfully.")
+except Exception as e:
+    print(f"FATAL: Could not load TTS model. Error: {e}")
+    # If the model fails to load, we can't do anything else.
+    # This will cause the app to crash, and the logs will show the error.
+    raise e
 
-# This is the core function that will be exposed as an API.
 def synthesize(text_to_speak, speaker_wav_path):
-    # Use the default voice if the provided path doesn't exist.
     if not os.path.exists(speaker_wav_path):
         print(f"Warning: Speaker file not found at '{speaker_wav_path}'. Using default.")
         speaker_wav_path = DEFAULT_SPEAKER_WAV
     
-    # Check if the default voice exists
     if not os.path.exists(speaker_wav_path):
          raise gr.Error("The default 'tutor_voice.wav' file is missing! Please upload it.")
 
     print(f"TTS Service: Synthesizing text: '{text_to_speak[:30]}...'")
-    
-    # Define where to save the output audio
     output_wav_path = "output.wav"
     
-    # Use the TTS model to generate speech and save it to a file.
     tts.tts_to_file(
         text=text_to_speak,
         file_path=output_wav_path,
         speaker_wav=speaker_wav_path,
-        language="en"  # Set the language here
+        language="en"
     )
     
     print(f"TTS Service: Audio saved to '{output_wav_path}'")
-    # The function returns the path to the generated audio file.
     return output_wav_path
 
-# Create a simple Gradio interface.
-# This is useful for testing but our primary use is the API.
 with gr.Blocks() as app:
-    gr.Markdown("# EveryPrep XII - TTS Voice Service")
-    gr.Markdown("This service generates audio from text using a cloned voice. It is primarily used via API.")
-    
-    text_input = gr.Textbox(label="Text to Synthesize", value="Hello! I am your personal AI tutor.")
-    # Use our uploaded voice file as the default speaker.
-    speaker_input = gr.File(label="Speaker WAV (Optional)", value=DEFAULT_SPEAKER_WAV)
+    gr.Markdown("# EveryPrep XII - TTS Voice Service (v2 - Fixed)")
     
-    # This is the crucial part that creates the API endpoint.
-    # When our Replit app calls the API, it's this function that runs.
     gr.Interface(
         fn=synthesize,
-        inputs=[text_input, speaker_input],
+        inputs=[
+            gr.Textbox(label="Text to Synthesize", value="This is a test of the fixed TTS service."),
+            gr.File(label="Speaker WAV (Optional)", value=DEFAULT_SPEAKER_WAV)
+        ],
         outputs=gr.Audio(label="Synthesized Audio"),
         title="TTS API Test Interface",
-        api_name="synthesize" # This sets the API endpoint name to /synthesize
+        api_name="synthesize"
     )
 
-# Launch the application.
 app.launch()