Update app.py

app.py

@@ -6,14 +6,24 @@ from deep_translator import GoogleTranslator
 from diffusers import StableDiffusionPipeline
 import torch
 import huggingface_hub
+from huggingface_hub import login
 
-# Replace with your actual Groq API key
+# Set up Groq API key
 api_key = os.getenv("GROQ_API_KEY")
 client = Groq(api_key=api_key)
 
-# Set Hugging Face API key
-HF_API_KEY = "https://huggingface.co/ByteDance/SDXL-Lightning/resolve/main/sdxl_lightning_1step_x0.safetensors"
-huggingface_hub.login(HF_API_KEY)
+# Retrieve Hugging Face API key from environment variable
+HF_API_KEY = os.getenv("HF_API_KEY")
+
+if HF_API_KEY is None:
+    raise ValueError("Hugging Face API key not found. Please set it as an environment variable.")
+
+# Login to Hugging Face
+try:
+    login(HF_API_KEY)
+    print("Login successful!")
+except Exception as e:
+    print(f"Error during Hugging Face login: {str(e)}")
 
 # Set device: CUDA if available, else CPU
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -24,16 +34,7 @@ whisper_model = whisper.load_model("base")
 # Model IDs for Stable Diffusion pipelines
 model_id1 = "dreamlike-art/dreamlike-diffusion-1.0"
 model_id2 = "stabilityai/stable-diffusion-xl-base-1.0"
-restricted_model_id = "ByteDance/SDXL-Lightning"  # Model you want to access using HF_API_KEY
-
-# # Initialize Stable Diffusion pipeline based on device
-# if torch.cuda.is_available():
-#     pipe = StableDiffusionPipeline.from_pretrained(model_id2, torch_dtype=torch.float16)
-# else:
-#     pipe = StableDiffusionPipeline.from_pretrained(model_id2)  # Omit torch_dtype for CPU
-
-# # Move model to the selected device (either GPU or CPU)
-# pipe = pipe.to(device)
+restricted_model_id = "ByteDance/SDXL-Lightning"  # Model to access using HF_API_KEY
 
 # Function to transcribe, translate, and analyze sentiment
 def process_audio(audio_path, image_option):