Update app.py

app.py

@@ -1,3 +1,4 @@
+import os
 import torch
 from diffusers import DiffusionPipeline
 import gradio as gr
@@ -8,14 +9,14 @@ def load_pipeline():
     lora_repo = "strangerzonehf/Flux-Super-Realism-LoRA"
     trigger_word = "Super Realism"  # Recommended trigger word
 
-# Retrieve your Hugging Face token from an environment variable
-hf_token = os.environ.get("HF_TOKEN")
+    # Retrieve your Hugging Face token from an environment variable
+    hf_token = os.environ.get("HF_TOKEN")
 
-pipe = DiffusionPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    torch_dtype=torch.bfloat16,
-    use_auth_token=hf_token  # Use the token stored in your environment variable
-)
+    pipe = DiffusionPipeline.from_pretrained(
+        base_model,
+        torch_dtype=torch.bfloat16,
+        use_auth_token=hf_token  # Use the token stored in your environment variable
+    )
 
     # Load the LoRA weights into the pipeline
     pipe.load_lora_weights(lora_repo)
@@ -52,7 +53,11 @@ def generate_image(prompt, seed, width, height, guidance_scale, randomize_seed):
 iface = gr.Interface(
     fn=generate_image,
     inputs=[
-        gr.inputs.Textbox(lines=2, label="Prompt", placeholder="Enter your prompt, e.g., 'A tiny astronaut hatching from an egg on the moon, 4k, planet theme'"),
+        gr.inputs.Textbox(
+            lines=2, 
+            label="Prompt", 
+            placeholder="Enter your prompt, e.g., 'A tiny astronaut hatching from an egg on the moon, 4k, planet theme'"
+        ),
         gr.inputs.Slider(0, 10000, step=1, default=0, label="Seed (0 for random)"),
         gr.inputs.Slider(256, 1024, step=64, default=1024, label="Width"),
         gr.inputs.Slider(256, 1024, step=64, default=1024, label="Height"),
@@ -69,4 +74,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-   iface.launch(share=True)
+    iface.launch(share=True)