Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import os
 
 # Set ALL cache directories before importing any model libraries
@@ -39,15 +38,22 @@ for lang, path in MODELS.items():
         print(f"{lang.capitalize()} model loaded successfully!")
     except Exception as e:
         print(f"Error loading {lang} model: {str(e)}")
+        loaded_models[lang] = None  # Mark as unavailable
+        loaded_processors[lang] = None
 
 SAMPLE_RATE = 16000  # Default sample rate
 OUTPUT_DIR = "/tmp/"  # Use /tmp for Hugging Face Spaces (limited storage)
 
+@app.route("/", methods=["GET"])
+def home():
+    """ Root route to check if the server is running """
+    return jsonify({"message": "TTS API is running. Use /tts to generate speech."})
+
 @app.route("/tts", methods=["POST"])
 def generate_tts():
     """ API endpoint to generate speech based on the selected language. """
     data = request.get_json()
-    text_input = data.get("text", "")
+    text_input = data.get("text", "").strip()
     language = data.get("language", "kapampangan").lower()
 
     if language not in MODELS:
@@ -56,29 +62,36 @@ def generate_tts():
     if not text_input:
         return jsonify({"error": "No text provided"}), 400
 
+    if loaded_models[language] is None:
+        return jsonify({"error": f"Model for {language} failed to load"}), 500
+
     print(f"Generating speech for: '{text_input}' in {language}")
 
-    # Select the correct model and processor
-    model = loaded_models[language]
-    processor = loaded_processors[language]
+    try:
+        # Select the correct model and processor
+        model = loaded_models[language]
+        processor = loaded_processors[language]
 
-    # Tokenize input text
-    inputs = processor(text_input, return_tensors="pt")
+        # Tokenize input text
+        inputs = processor(text_input, return_tensors="pt")
 
-    # Generate audio
-    with torch.no_grad():
-        output = model.generate(**inputs)
+        # Generate audio
+        with torch.no_grad():
+            output = model.generate(**inputs)
 
-    waveform = output.cpu().numpy().flatten()
+        waveform = output.cpu().numpy().flatten()
 
-    # Save as WAV file
-    output_filename = os.path.join(OUTPUT_DIR, f"{language}_output.wav")
-    sf.write(output_filename, waveform, SAMPLE_RATE)
+        # Save as WAV file
+        output_filename = os.path.join(OUTPUT_DIR, f"{language}_output.wav")
+        sf.write(output_filename, waveform, SAMPLE_RATE)
 
-    return jsonify({
-        "message": "TTS audio generated",
-        "file_url": f"/static/{language}_output.wav"
-    })
+        return jsonify({
+            "message": "TTS audio generated",
+            "file_url": f"/static/{language}_output.wav"
+        })
+    except Exception as e:
+        print(f"Error generating TTS: {str(e)}")
+        return jsonify({"error": "Internal server error"}), 500
 
 @app.route("/static/<filename>")
 def serve_audio(filename):
@@ -90,3 +103,4 @@ def serve_audio(filename):
 
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860, debug=True)
+