Update app.py

app.py

@@ -1,21 +1,44 @@
 import os
+import sys
 import subprocess
 
-os.chdir('diffusers_repo') 
-subprocess.check_call(['pip', 'install', '-e', '.'])
+# Ensure we're in the right directory and install diffusers
+try:
+    os.chdir('diffusers_repo')
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-e', '.'])
+    print("Successfully installed diffusers from local repository")
+except Exception as e:
+    print(f"Installation error: {e}")
+    # Fallback to standard diffusers installation
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'diffusers'])
 
-import gradio as gr
-import spaces
-import torch
-from diffusers import CosmosTextToImagePipeline
-import random
+# Add the current directory to Python path if needed
+if os.getcwd() not in sys.path:
+    sys.path.insert(0, os.getcwd())
+
+try:
+    import gradio as gr
+    import spaces
+    import torch
+    from diffusers import CosmosTextToImagePipeline
+    import random
+    print("All imports successful")
+except ImportError as e:
+    print(f"Import error: {e}")
+    print("Please ensure all required packages are installed")
+    sys.exit(1)
 
 # Available checkpoints: nvidia/Cosmos-Predict2-2B-Text2Image, nvidia/Cosmos-Predict2-14B-Text2Image
 model_id = "diffusers-internal-dev/ct2i-14B"
 
 # Load the pipeline once to avoid repeated loading
-pipe = CosmosTextToImagePipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
+try:
+    pipe = CosmosTextToImagePipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+    pipe.to("cuda")
+    print("Pipeline loaded successfully")
+except Exception as e:
+    print(f"Error loading pipeline: {e}")
+    sys.exit(1)
 
 @spaces.GPU
 def generate_image(prompt, negative_prompt, seed, randomize_seed):
@@ -62,7 +85,7 @@ with gr.Blocks() as demo:
             with gr.Row():
                 randomize_seed_checkbox = gr.Checkbox(
                     label="Randomize Seed",
-                    value=TRue
+                    value=True  # Fixed: was "TRue"
                 )
                 seed_input = gr.Slider(
                     minimum=0,