Update app.py

app.py

@@ -7,11 +7,35 @@ import google.generativeai as genai
 from e2_tts_pytorch import E2TTS, DurationPredictor
 import numpy as np
 import os
+import requests
+from tqdm import tqdm
 
 # Initialize Gemini AI
 genai.configure(api_key='YOUR_GEMINI_API_KEY')
 model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
 
+# Function to download the model file
+def download_model(url, filename):
+    response = requests.get(url, stream=True)
+    total_size = int(response.headers.get('content-length', 0))
+    block_size = 1024  # 1 KB
+    progress_bar = tqdm(total=total_size, unit='iB', unit_scale=True)
+    
+    os.makedirs(os.path.dirname(filename), exist_ok=True)
+    with open(filename, 'wb') as file:
+        for data in response.iter_content(block_size):
+            size = file.write(data)
+            progress_bar.update(size)
+    progress_bar.close()
+
+# Check if model file exists, if not, download it
+model_path = "ckpts/E2TTS_Base/model_1200000.safetensors"
+if not os.path.exists(model_path):
+    print("Downloading model file...")
+    model_url = "https://huggingface.co/SWivid/E2-TTS/resolve/main/E2TTS_Base/model_1200000.safetensors"
+    download_model(model_url, model_path)
+    print("Model file downloaded successfully.")
+
 # Initialize E2-TTS model
 duration_predictor = DurationPredictor(
     transformer=dict(
@@ -29,7 +53,6 @@ e2tts = E2TTS(
 )
 
 # Load the pre-trained model
-model_path = "ckpts/E2TTS_Base/model_1200000.safetensors"
 e2tts.load_state_dict(torch.load(model_path))
 e2tts.eval()