Update app.py

app.py

@@ -12,24 +12,12 @@ from diffusers import FluxKontextPipeline
 # --- Constants and Model Loading ---
 MAX_SEED = np.iinfo(np.int32).max
 
-# --- FIX 1: Handle Hugging Face Authentication ---
-# This is a gated model. You must have access on Hugging Face and provide a token.
-# 1. Visit https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev and accept the terms.
-# 2. Get an access token from https://huggingface.co/settings/tokens
-# 3. Add the token below or set it as an environment variable `HF_TOKEN`.
-HF_TOKEN = os.getenv("HF_TOKEN", "YOUR_HUGGING_FACE_TOKEN") # Replace with your token
-
 # Load the pretrained model
 try:
-    if HF_TOKEN == "YOUR_HUGGING_FACE_TOKEN":
-        pipe = None
-        print("Warning: Hugging Face token not provided. Please replace 'YOUR_HUGGING_FACE_TOKEN' with your actual token.")
-    else:
-        pipe = FluxKontextPipeline.from_pretrained(
-            "black-forest-labs/FLUX.1-Kontext-dev", 
-            torch_dtype=torch.bfloat16,
-            token=HF_TOKEN,  # Use the token for authentication
-        ).to("cuda")
+    pipe = FluxKontextPipeline.from_pretrained(
+        "black-forest-labs/FLUX.1-Kontext-dev", 
+        torch_dtype=torch.bfloat16,
+    ).to("cuda")
 except Exception as e:
     pipe = None
     print(f"Warning: Could not load the model on CUDA. GPU is required. Error: {e}")
@@ -42,7 +30,7 @@ def chat_fn(message, chat_history, seed, randomize_seed, guidance_scale, steps,
     Performs image generation or editing based on user input from the chat interface.
     """
     if pipe is None:
-        raise gr.Error("Model could not be loaded. This could be due to a missing Hugging Face token, no access to the model, or no CUDA-enabled GPU.")
+        raise gr.Error("Model could not be loaded. This could be due to no access to the model or no CUDA-enabled GPU.")
 
     prompt = message["text"]
     files = message["files"]
@@ -84,19 +72,19 @@ randomize_checkbox = gr.Checkbox(label="Randomize seed", value=False)
 guidance_slider = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=2.5)
 steps_slider = gr.Slider(label="Steps", minimum=1, maximum=30, value=28, step=1)
 
-# --- FIX 2: Correctly format the examples as a list of lists ---
-# Format: [ [message_dict, seed, randomize, guidance, steps], ... ]
+# --- FIX 2: Remove examples with external URLs that cause 403 errors ---
+# Instead, provide text-only examples that work without external image dependencies
 examples = [
     [
-        {"text": "A cute robot reading a book", "files": []}, 
+        {"text": "A cute robot reading a book in a cozy library", "files": []}, 
         42, False, 2.5, 28
     ],
     [
-        {"text": "change his shirt to a hawaiian shirt", "files": ["https://gradio-builds.s3.amazonaws.com/demo-files/chewbacca.png"]},
+        {"text": "A majestic lion standing on a rocky cliff at sunset", "files": []},
         12345, False, 3.0, 25
     ],
     [
-        {"text": "make it a wooden house, add a chimney", "files": ["https://gradio-builds.s3.amazonaws.com/demo-files/house.png"]},
+        {"text": "A futuristic cityscape with flying cars and neon lights", "files": []},
         54321, False, 2.0, 30
     ],
 ]
@@ -124,7 +112,7 @@ demo = gr.ChatInterface(
         guidance_slider,
         steps_slider
     ],
-    examples=examples, # Use the correctly formatted list
+    examples=examples,
     theme="soft"
 )