Update app.py

app.py

@@ -1,26 +1,33 @@
+import os
 import torch
 import gradio as gr
 from PIL import Image
 from diffusers import StableDiffusionPipeline
-
-# Use a pipeline as a high-level helper
 from transformers import pipeline
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Set Hugging Face API token from environment variable
+hf_token = os.getenv('HF_TOKEN')
+if not hf_token:
+    raise ValueError("Hugging Face API token not found. Please set HF_TOKEN in your .env file.")
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 caption_image = pipeline("image-to-text",
                 model="Salesforce/blip-image-captioning-large", device=device)
 
-
 def image_generation(prompt):
     device = "cuda" if torch.cuda.is_available() else "cpu"
     pipeline = StableDiffusionPipeline.from_pretrained(
         "stabilityai/stable-diffusion-3-medium",
         torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+        use_auth_token=hf_token  # Use the Hugging Face API token for authentication
     )
-    #pipeline.to(device)
     pipeline.enable_model_cpu_offload()
-    
+
     image = pipeline(
         prompt=prompt + " 8K, Ultra HD",
         negative_prompt="blurred, ugly, watermark, low resolution, blurry, nude",
@@ -38,8 +45,8 @@ def caption_my_image(pil_image):
     return images
 
 demo = gr.Interface(fn=caption_my_image,
-                    inputs=[gr.Image(label="Select Image",type="pil")],
-                    outputs=[gr.Image(label="New Image genrated using SD3",type="pil")],
+                    inputs=[gr.Image(label="Select Image", type="pil")],
+                    outputs=[gr.Image(label="New Image generated using SD3", type="pil")],
                     title="PicTalker | ImageNarrator | SnapSpeech | SpeakScene",
                     description="🌟 Transform Ordinary Photos into Extraordinary Art!")
-demo.launch()
+demo.launch()