Update app.py

app.py

@@ -5,26 +5,32 @@ from groq import Groq
 from deep_translator import GoogleTranslator
 from diffusers import StableDiffusionPipeline
 import torch
+import huggingface_hub
 
-# Set up Groq API key
-api_key = os.getenv("GROQ_API_KEY")
+# Replace with your actual Groq API key
+api_key = "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"
 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)
+
 # Set device: CUDA if available, else CPU
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 # Load Whisper model (if using locally, else use API as in original code)
-# This is assuming you're using Whisper locally, if not, the client API is used.
 whisper_model = whisper.load_model("base")
 
 # Model IDs for Stable Diffusion pipelines
- HF_API_KEY = "https://huggingface.co/ByteDance/SDXL-Lightning/resolve/main/sdxl_lightning_1step_x0.safetensors"
+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(HF_API_KEY, torch_dtype=torch.float16)
+    pipe = StableDiffusionPipeline.from_pretrained(model_id2, torch_dtype=torch.float16)
 else:
-    pipe = StableDiffusionPipeline.from_pretrained(HF_API_KEY)  # Omit torch_dtype for CPU
+    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)
@@ -58,12 +64,12 @@ def process_audio(audio_path, image_option):
     image = None
     if image_option == "Generate Image":
         try:
-            model_id1 = "dreamlike-art/dreamlike-diffusion-1.0"
-            pipe = StableDiffusionPipeline.from_pretrained(model_id1, torch_dtype=torch.float16, use_safetensors=True)
-            pipe = pipe.to("cuda")
+            # Use the Hugging Face API key to load the restricted model for image generation
+            pipe = StableDiffusionPipeline.from_pretrained(restricted_model_id, torch_dtype=torch.float16, use_auth_token=HF_API_KEY)
+            pipe = pipe.to(device)
             image = pipe(translation).images[0]
         except Exception as e:
-            return tamil_text, translation, f"An error occurred during image generation: {str(e)}"
+            return tamil_text, translation, f"An error occurred during image generation: {str(e)}", None
 
     return tamil_text, translation, image