Update app.py

app.py

@@ -1,56 +1,42 @@
-import gradio as gr
-import torch
+from Crypto.Cipher import AES
+from Crypto.Protocol.KDF import PBKDF2
+import os
 import tempfile
-import soundfile as sf
-from tortoise.api import TextToSpeech
-from tortoise.utils.audio import load_audio
-
-# 1) Initialize the Tortoise TTS engine at startup
-tts = TextToSpeech()  # Downloads and caches models automatically
-
-# 2) Define a helper to generate speech from a reference clip + text
-def generate_speech(reference_audio_path, text):
-    """
-    reference_audio_path: filepath to a WAV sampled at 22 050 Hz
-    text: the string to synthesize
-    returns: path to a 24 kHz WAV file with your cloned voice
-    """
-    # ✅ FIXED: Provide sampling_rate as a required positional argument
-    ref_waveform = load_audio(reference_audio_path, 22050)
-
-    # Generate speech using 'fast' preset (alternatives: ultra_fast, standard, high_quality)
-    output_tensor = tts.tts_with_preset(
-        text,
-        voice_samples=[ref_waveform],
-        preset="fast"
-    )
-
-    # Save to temp WAV (float32, 24 kHz)
-    wav_np = output_tensor.squeeze().cpu().numpy()
-    tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
-    sf.write(tmp.name, wav_np, samplerate=24000)
-    return tmp.name
-
-# 3) Build the Gradio interface
-with gr.Blocks(title="Tortoise Voice Cloning TTS") as app:
-    gr.Markdown("## 🗣️ Voice Cloning with Tortoise TTS")
-    gr.Markdown(
-        "Upload a ~10 sec WAV clip (22 050 Hz), enter English text, "
-        "and hear it spoken back in **your** voice!"
-    )
-
-    with gr.Row():
-        voice_sample = gr.Audio(type="filepath", label="🎙️ Upload Reference Voice (22 050 Hz WAV)")
-        text_input   = gr.Textbox(label="💬 Text to Synthesize", placeholder="e.g., Hello, world!")
-
-    generate_btn = gr.Button("🔊 Generate Speech")
-    output_audio = gr.Audio(label="📢 Cloned Speech Output (24 kHz)", interactive=False)
-
-    generate_btn.click(
-        fn=generate_speech,
-        inputs=[voice_sample, text_input],
-        outputs=output_audio
-    )
+from dotenv import load_dotenv
+
+load_dotenv()  # Load all environment variables
+
+def unpad(data):
+    return data[:-data[-1]]
+
+def decrypt_and_run():
+    # Get password from Hugging Face Secrets environment variable
+    password = os.getenv("PASSWORD")
+    if not password:
+        raise ValueError("PASSWORD secret not found in environment variables")
+
+    password = password.encode()
+
+    with open("code.enc", "rb") as f:
+        encrypted = f.read()
+
+    salt = encrypted[:16]
+    iv = encrypted[16:32]
+    ciphertext = encrypted[32:]
+
+    key = PBKDF2(password, salt, dkLen=32, count=1000000)
+    cipher = AES.new(key, AES.MODE_CBC, iv)
+
+    plaintext = unpad(cipher.decrypt(ciphertext))
+
+    with tempfile.NamedTemporaryFile(suffix=".py", delete=False, mode='wb') as tmp:
+        tmp.write(plaintext)
+        tmp.flush()
+        print(f"[INFO] Running decrypted code from {tmp.name}")
+        os.system(f"python {tmp.name}")
 
 if __name__ == "__main__":
-    app.launch()
+    decrypt_and_run()
+
+# This script decrypts the encrypted code and runs it.
+# Ensure you have the PASSWORD secret set in your Hugging Face Secrets