adding sign-in with HF

app.py

@@ -6,11 +6,12 @@ import os
 import time
 from PIL import Image
 import json
+from typing import Optional
 from huggingface_hub import InferenceClient
 
 # Project by Nymbo
 
-def query_with_auto_routing(prompt, model, custom_lora, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
+def query_with_auto_routing(prompt, model, custom_lora, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024, oauth_token: Optional[gr.OAuthToken] = None):
     """
     Generate images using HF's automatic provider routing
     """
@@ -35,12 +36,20 @@ def query_with_auto_routing(prompt, model, custom_lora, is_negative=False, steps
 
     try:
         # Use automatic provider routing
-        api_key = os.getenv("HF_READ_TOKEN")
+        api_key: Optional[str] = None
+        if oauth_token is not None and getattr(oauth_token, "token", None):
+            api_key = oauth_token.token
+            print("Using OAuth token from signed-in user for inference.")
+        else:
+            api_key = os.getenv("HF_READ_TOKEN")
+            if api_key:
+                print("Using HF_READ_TOKEN environment variable for inference.")
+
         if not api_key:
-            raise gr.Error("HF_READ_TOKEN not found. Please set your Hugging Face API token.")
-        
+            raise gr.Error("No Hugging Face token available. Sign in with Hugging Face or set HF_READ_TOKEN.")
+
         # Initialize client with automatic provider selection (default is "auto")
-        client = InferenceClient(api_key=api_key)
+        client = InferenceClient(token=api_key)
         
         # Determine the model to use
         if custom_lora.strip() != "":
@@ -331,11 +340,31 @@ def apply_model_prompt_enhancements(model_name, prompt):
     
     return prompt
 
-def query(prompt, model, custom_lora, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
+def render_profile(profile: Optional[gr.OAuthProfile]) -> str:
+    """Display the current authentication status in the UI."""
+    if profile is None:
+        return "Not signed in. Use the button above to sign in with Hugging Face and run inference with your own account."
+    display_name = getattr(profile, "name", None) or getattr(profile, "username", "Hugging Face user")
+    return f"Signed in as **{display_name}**."
+
+def query(prompt, model, custom_lora, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024, oauth_token: Optional[gr.OAuthToken] = None):
     """
     Main query function - now uses automatic provider routing
     """
-    return query_with_auto_routing(prompt, model, custom_lora, is_negative, steps, cfg_scale, sampler, seed, strength, width, height)
+    return query_with_auto_routing(
+        prompt,
+        model,
+        custom_lora,
+        is_negative,
+        steps,
+        cfg_scale,
+        sampler,
+        seed,
+        strength,
+        width,
+        height,
+        oauth_token=oauth_token,
+    )
 
 # Custom CSS to hide the footer in the interface
 css = """
@@ -347,6 +376,10 @@ print("Initializing Gradio interface...")  # Debug log
 
 # Define the Gradio interface
 with gr.Blocks(theme='Nymbo/Nymbo_Theme') as dalle:
+    with gr.Row(elem_id="oauth-row"):
+        login_button = gr.LoginButton()
+        auth_status = gr.Markdown(render_profile(None), elem_id="oauth-status")
+
     # Tab for basic settings
     with gr.Tab("Basic Settings"):
         with gr.Row():
@@ -603,6 +636,9 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme') as dalle:
     # Set up button click event to call the main query function
     text_button.click(query, inputs=[text_prompt, model, custom_lora, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)
 
+    # Update authentication status once the app loads (and after OAuth redirects)
+    dalle.load(render_profile, inputs=None, outputs=auth_status)
+
 print("Launching Gradio interface...")  # Debug log
 # Launch the Gradio interface without showing the API or sharing externally
 dalle.launch(show_api=False, share=False, ssr_mode=False)