Update app.py

app.py

@@ -4,15 +4,12 @@ import numpy as np
 import re
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-from huggingface_hub import snapshot_download, login
+from huggingface_hub import snapshot_download
 import logging
 import os
 import spaces
 import warnings
 from snac import SNAC
-from dotenv import load_dotenv
-
-load_dotenv()
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
@@ -40,7 +37,7 @@ def load_model():
 
         snapshot_download(
             repo_id=model_name,
-            use_auth_token=os.environ.get("HUGGINGFACE_TOKEN"),
+            use_auth_token=os.environ.get("HF_TOKEN"),
             allow_patterns=["config.json", "*.safetensors", "model.safetensors.index.json", "vocab.json", "merges.txt", "tokenizer.json"],
             ignore_patterns=["optimizer.pt", "pytorch_model.bin", "training_args.bin", "scheduler.pt"]
         )
@@ -57,7 +54,7 @@ def load_model():
 def generate_podcast_script(api_key, content, uploaded_file, duration, num_hosts):
     try:
         genai.configure(api_key=api_key)
-        model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
+        model = genai.GenerativeModel('gemini-pro')
         
         combined_content = content or ""
         if uploaded_file:
@@ -249,6 +246,6 @@ with gr.Blocks() as demo:
 if __name__ == "__main__":
     try:
         load_model()
-        demo.queue().launch(share=False, ssr_mode=False)
+        demo.queue().launch()
     except Exception as e:
         logger.error(f"Error launching the application: {str(e)}")