fix additional models

app-Copy2.py → .ipynb_checkpoints/app-Copy1-checkpoint.py

@@ -28,7 +28,6 @@ from library.utils_html import *
 from library.utils_prompt import *
 
 OR = OpenRouterAPI()
-gemini = OpenRouterAPI(api_key = os.getenv("GEMINI_API_KEY"),base_url="https://generativelanguage.googleapis.com/v1beta/openai/")
 
 # Path for storing user preferences
 PREFERENCES_FILE = "data/user_preferences.csv"
@@ -36,14 +35,38 @@ PREFERENCES_FILE = "data/user_preferences.csv"
 # Ensure directory exists
 os.makedirs(os.path.dirname(PREFERENCES_FILE), exist_ok=True)
 
-def get_sys_prompt(length="medium"):
+# Define model pricing information (approximate costs per 100 image API calls)
+MODEL_PRICING = {
+    "google/gemini-2.0-flash-001": "$0.03",
+    "gpt-4.1-mini": "$0.07",
+    "gpt-4.1": "$0.35",
+    "anthropic/claude-3.7-sonnet": "$0.70",
+    "google/gemini-2.5-pro-preview-03-25": "$1.20",
+    "google/gemini-2.5-flash-preview:thinking": "$0.35",
+    "gpt-4.1-nano": "$0.02",
+    "openai/chatgpt-4o-latest": "$0.75",
+    "meta-llama/llama-4-maverick": "$0.04"
+}
+
+def get_sys_prompt(length="medium", photograph=False):
+    extra_prompt = ""
+
+    if photograph:
+        object_type = "wildlife photography"
+        extra_prompt = " Do not guess the exact species of the animals in the photograph unless you are certain - simply use a broader terms to make less errors e.g. say Swan rather mute Swan or Whooper Swan unless you are certain."
+    else:
+        object_type = "museum objects"
+        
+    dev_prompt = f"""You are a museum curator tasked with generating long descriptions (as defined by W3C) of {object_type} for visually impaired and blind users from images. Use British English and follow museum accessibility best practices. Do not start with phrases like 'The image shows', 'This is an image of', 'The photograph'. Be precise, concise and avoid filler and subjective statements."""
+    
     if length == "short":
-        dev_prompt = """You are a museum curator tasked with generating alt-text (as defined by W3C) of museum objects for visually impaired and blind users from images. Use British English and follow museum accessibility best practices. Do not start with phrases like 'The image shows' or 'This is an image of'. Be precise, concise and avoid filler and subjective statements. Repsonses should be a maximum of 130 characters."""
+        dev_prompt = f"""You are a museum curator tasked with generating alt-text (as defined by W3C) of {object_type} for visually impaired and blind users from images. Use British English and follow museum accessibility best practices. Do not start with phrases like 'The image shows' or 'This is an image of'. Be precise, concise and avoid filler and subjective statements. Repsonses should be a maximum of 130 characters."""
     elif length == "medium":
-        dev_prompt = """You are a museum curator tasked with generating long descriptions (as defined in W3C) of museum objects for visually impaired and blind users from images. Use British English and follow museum accessibility best practices. Do not start with phrases like 'The image shows' or 'This is an image of'. Be precise, concise and avoid filler and subjective statements. Repsonses should be between 250-300 characters in length."""
+        dev_prompt += " Repsonses should be a maximum of 250-300 characters."
     else: # long
-        dev_prompt = """You are a museum curator tasked with generating long descriptions (as defined in W3C) of museum objects for visually impaired and blind users from images. Use British English and follow museum accessibility best practices. Do not start with phrases like 'The image shows' or 'This is an image of'. Be precise, concise and avoid filler and subjective statements. Repsonses should be a maxium of 450 characters."""
-    return dev_prompt
+        dev_prompt += " Repsonses should be a maximum of 450 characters."
+    return dev_prompt + extra_prompt
+
 
 def create_csv_file_simple(results):
     """Create a CSV file from the results and return the path"""
@@ -62,7 +85,6 @@ def create_csv_file_simple(results):
         print(f"Error creating CSV: {e}")
         return None
 
-
 def get_base_filename(filepath):
     if not filepath:
         return ""
@@ -72,7 +94,7 @@ def get_base_filename(filepath):
 
 # Define the Gradio interface
 def create_demo():
-    # --- Reintroduce CSS ---
+    # --- Updated CSS with model info styling ---
     custom_css = """
     /* Container for the image component (#current-image-display is the elem_id of gr.Image) */
         #current-image-display {
@@ -93,6 +115,15 @@ def create_demo():
             height: auto;           /* Use natural height unless constrained by max-height */
             display: block;         /* Ensure image behaves predictably in flex */
         }
+        
+        /* Custom style for model info display */
+        #model-info-display {
+            font-size: 0.85rem;     /* Small font size */
+            color: #666;            /* Subtle color */
+            margin-top: 0.5rem;     /* Small top margin */
+            margin-bottom: 1rem;    /* Bottom margin before next element */
+            padding-left: 0.5rem;   /* Slight indentation */
+        }
     """
     # --- Pass css to gr.Blocks ---
     with gr.Blocks(theme=gr.themes.Monochrome(), css=custom_css) as demo:
@@ -112,6 +143,31 @@ def create_demo():
                              show_share_button=False, show_fullscreen_button=False,
                              container=False, elem_id="nml-logo")
 
+        # Store model choices and state
+        show_all_models_state = gr.State(False)
+
+        # Define preferred and additional models directly in the function
+        preferred_models = [
+            ("Gemini 2.0 Flash (cheap)", "google/gemini-2.0-flash-001"),
+            ("GPT-4.1 Mini", "gpt-4.1-mini"),
+            ("GPT-4.1 (Recommended)", "gpt-4.1"),
+            ("Claude 3.7 Sonnet", "anthropic/claude-3.7-sonnet"),
+            ("Gemini 2.5 Pro", "google/gemini-2.5-pro-preview-03-25"),
+            ("Gemini 2.5 Flash Thinking (Recommended)", "google/gemini-2.5-flash-preview:thinking")
+        ]
+        
+        additional_models = [
+            ("GPT-4.1 Nano", "gpt-4.1-nano"),
+            ("ChatGPT Latest", "openai/chatgpt-4o-latest"),
+            ("Llama 4 Maverick", "meta-llama/llama-4-maverick")
+        ]
+        
+        # Calculate all models once
+        all_models_list = preferred_models + additional_models
+        
+        # Default model value
+        default_model = "google/gemini-2.0-flash-001"
+        
         with gr.Row():
             # Left column: Controls and uploads
             with gr.Column(scale=1):
@@ -120,24 +176,48 @@ def create_demo():
                     file_types=["image"],
                     file_count="multiple"
                 )
-                model_choices = [
-                    ("Gemini 2.0 Flash (default)", "google/gemini-2.0-flash-001"),
-                    ("GPT-4.1 Nano", "gpt-4.1-nano"), ("GPT-4.1 Mini", "gpt-4.1-mini"),
-                    ("GPT-4.1", "gpt-4.1"), ("ChatGPT Latest", "openai/chatgpt-4o-latest"),
-                    ("Claude 3.7 Sonnet", "anthropic/claude-3.7-sonnet"),
-                    ("Llama 4 Maverick", "meta-llama/llama-4-maverick"),
-                    ("Gemini 2.5 Pro (Experimental, limited)", "gemini-2.5-pro-exp-03-25"),
-                    ("Gemini 2.0 Flash Thinking (Experimental, limited)", "gemini-2.0-flash-thinking-exp-01-21")
-                ]
-                default_model_internal_value = "google/gemini-2.0-flash-001"
+                
+                # Model dropdown
                 model_choice = gr.Dropdown(
-                    choices=model_choices, label="Select Model",
-                    value=default_model_internal_value, visible=True
+                    choices=preferred_models, 
+                    label="Select Model",
+                    value=default_model
                 )
+                
                 length_choice = gr.Radio(
-                    choices=["short", "medium", "long"], label="Response Length",
-                    value="medium", info="Short: max 130 chars | Medium: 250-300 chars | Long: max 450 chars"
+                    choices=["short", "medium", "long"], 
+                    label="Response Length",
+                    value="medium", 
+                    info="Short: max 130 chars | Medium: 250-300 chars | Long: max 450 chars"
+                )
+                
+                # Advanced settings accordion
+                with gr.Accordion("Advanced Settings", open=False):
+                    show_all_models = gr.Checkbox(
+                        label="Show Additional Models", 
+                        value=False,
+                        info="Display additional model options in the dropdown above"
+                    )
+                    
+                    content_type = gr.Radio(
+                        choices=["Museum Object", "Photography"], 
+                        label="Content Type",
+                        value="Museum Object"
+                    )
+                
+                # Find the default model's display name
+                default_model_name = "Unknown Model"
+                for name, value in preferred_models:
+                    if value == default_model:
+                        default_model_name = name
+                        break
+                
+                model_info = gr.Markdown(
+                    f"""**Current Model**: {default_model_name}  
+                    **Estimated cost per 100 Images**: {MODEL_PRICING[default_model]}""",
+                    elem_id="model-info-display"
                 )
+                
                 gr.Markdown("### Uploaded Images")
                 input_gallery = gr.Gallery(
                     label="Uploaded Image Previews", columns=3, height=150,
@@ -153,7 +233,7 @@ def create_demo():
                 current_image = gr.Image(
                     label="Current Image",
                     type="filepath",
-                    elem_id="current-image-display", # ADDED - for CSS targeting
+                    elem_id="current-image-display",
                     show_fullscreen_button=True,
                     show_download_button=False,
                     show_share_button=False,
@@ -174,8 +254,57 @@ def create_demo():
                 current_index = gr.State(0)
                 all_images = gr.State([])
                 all_results = gr.State([])
+        
+        # Handle checkbox change to update model dropdown - modern version
+        def toggle_models(show_all, current_model):
+            # Make a fresh copy of the models lists to avoid any reference issues
+            preferred_choices = list(preferred_models)
+            all_choices = list(all_models_list)
+            
+            if show_all:
+                # When showing all models, use the fresh copy of all models
+                return gr.Dropdown(choices=all_choices, value=current_model)
+            else:
+                # Check if current model is in preferred models list
+                preferred_values = [value for _, value in preferred_choices]
+                
+                if current_model in preferred_values:
+                    # Keep the current model if it's in preferred models
+                    return gr.Dropdown(choices=preferred_choices, value=current_model)
+                else:
+                    # Reset to default model if current model is not in preferred models
+                    return gr.Dropdown(choices=preferred_choices, value=default_model)
 
-        # --- Functions (handle_upload, analyze_images, navigators) remain the same ---
+        # Update model info when model selection changes
+        def update_model_info(model_value):
+            # Find display name
+            model_name = "Unknown Model"
+            for name, value in all_models_list:
+                if value == model_value:
+                    model_name = name
+                    break
+            
+            # Get cost
+            cost = MODEL_PRICING.get(model_value, "Unknown")
+            
+            # Create markdown
+            return f"""**Current Model**: {model_name}  
+                   **Estimated cost per 100 Images**: {cost}"""
+                
+        # Connect checkbox to toggle model choices
+        show_all_models.change(
+            fn=toggle_models,
+            inputs=[show_all_models, model_choice],
+            outputs=[model_choice]
+        )
+        
+        # Connect model selection to update info
+        model_choice.change(
+            fn=update_model_info,
+            inputs=[model_choice],
+            outputs=[model_info]
+        )
+        
         # Handle file uploads
         def handle_upload(files, current_paths, current_filenames):
             file_paths = []
@@ -194,11 +323,12 @@ def create_demo():
         )
 
         # Analyze images
-        def analyze_images(image_paths, model_choice, length_choice, filenames):
+        def analyze_images(image_paths, model_choice, length_choice, filenames, content_type_choice):
             if not image_paths:
                 return [], [], 0, None, "0 of 0", "No images uploaded to analyze.", None
 
-            sys_prompt = get_sys_prompt(length_choice)
+            is_photography = content_type_choice == "Photography"
+            sys_prompt = get_sys_prompt(length_choice, photograph=is_photography)
             image_results = []
             analysis_progress = gr.Progress(track_tqdm=True)
 
@@ -209,10 +339,6 @@ def create_demo():
                     prompt0 = prompt_new()
                     model_name = model_choice
                     client_to_use = OR # Default client
-                    # Add logic here if you need to switch between OR and gemini clients based on model_name
-                    # Example:
-                    # if model_name.startswith("google/gemini") and gemini:
-                    #      client_to_use = gemini
 
                     result = client_to_use.generate_caption(
                         img, model=model_name, max_image_size=512,
@@ -257,7 +383,7 @@ def create_demo():
         # Connect analyze button
         analyze_button.click(
             fn=analyze_images,
-            inputs=[image_state, model_choice, length_choice, filename_state],
+            inputs=[image_state, model_choice, length_choice, filename_state, content_type],
             outputs=[all_images, all_results, current_index, current_image, image_counter,
                      analysis_text, csv_download]
         )
@@ -294,6 +420,5 @@ def create_demo():
 
 # Launch the app
 if __name__ == "__main__":
-
     app = create_demo()
     app.launch()

README.md

@@ -7,6 +7,7 @@ sdk: gradio
 sdk_version: 5.24.0
 app_file: app.py
 pinned: false
+hf_oauth: true
 license: mit
 short_description: Generate Alt-Text for Museum and Gallery Objects!
 ---

app-Copy1.py

@@ -0,0 +1,424 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+import io
+import os
+import requests
+import json
+from dotenv import load_dotenv
+import openai
+import base64
+import csv
+import tempfile
+import datetime
+
+# Load environment variables from .env file if it exists (for local development)
+# On Hugging Face Spaces, the secrets are automatically available as environment variables
+if os.path.exists(".env"):
+    load_dotenv()
+
+from io import BytesIO
+import numpy as np
+import requests
+from PIL import Image
+
+# import libraries
+from library.utils_model import *
+from library.utils_html import *
+from library.utils_prompt import *
+
+OR = OpenRouterAPI()
+
+# Path for storing user preferences
+PREFERENCES_FILE = "data/user_preferences.csv"
+
+# Ensure directory exists
+os.makedirs(os.path.dirname(PREFERENCES_FILE), exist_ok=True)
+
+# Define model pricing information (approximate costs per 100 image API calls)
+MODEL_PRICING = {
+    "google/gemini-2.0-flash-001": "$0.03",
+    "gpt-4.1-mini": "$0.07",
+    "gpt-4.1": "$0.35",
+    "anthropic/claude-3.7-sonnet": "$0.70",
+    "google/gemini-2.5-pro-preview-03-25": "$1.20",
+    "google/gemini-2.5-flash-preview:thinking": "$0.35",
+    "gpt-4.1-nano": "$0.02",
+    "openai/chatgpt-4o-latest": "$0.75",
+    "meta-llama/llama-4-maverick": "$0.04"
+}
+
+def get_sys_prompt(length="medium", photograph=False):
+    extra_prompt = ""
+
+    if photograph:
+        object_type = "wildlife photography"
+        extra_prompt = " Do not guess the exact species of the animals in the photograph unless you are certain - simply use a broader terms to make less errors e.g. say Swan rather mute Swan or Whooper Swan unless you are certain."
+    else:
+        object_type = "museum objects"
+        
+    dev_prompt = f"""You are a museum curator tasked with generating long descriptions (as defined by W3C) of {object_type} for visually impaired and blind users from images. Use British English and follow museum accessibility best practices. Do not start with phrases like 'The image shows', 'This is an image of', 'The photograph'. Be precise, concise and avoid filler and subjective statements."""
+    
+    if length == "short":
+        dev_prompt = f"""You are a museum curator tasked with generating alt-text (as defined by W3C) of {object_type} for visually impaired and blind users from images. Use British English and follow museum accessibility best practices. Do not start with phrases like 'The image shows' or 'This is an image of'. Be precise, concise and avoid filler and subjective statements. Repsonses should be a maximum of 130 characters."""
+    elif length == "medium":
+        dev_prompt += " Repsonses should be a maximum of 250-300 characters."
+    else: # long
+        dev_prompt += " Repsonses should be a maximum of 450 characters."
+    return dev_prompt + extra_prompt
+
+
+def create_csv_file_simple(results):
+    """Create a CSV file from the results and return the path"""
+    try:
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.csv', delete=False, newline='', encoding='utf-8') as f:
+            path = f.name
+            writer = csv.writer(f)
+            writer.writerow(['image_id', 'content'])
+            for result in results:
+                writer.writerow([
+                    result.get('image_id', ''),
+                    result.get('content', '')
+                ])
+        return path
+    except Exception as e:
+        print(f"Error creating CSV: {e}")
+        return None
+
+def get_base_filename(filepath):
+    if not filepath:
+        return ""
+    basename = os.path.basename(filepath)
+    filename = os.path.splitext(basename)[0]
+    return filename
+
+# Define the Gradio interface
+def create_demo():
+    # --- Updated CSS with model info styling ---
+    custom_css = """
+    /* Container for the image component (#current-image-display is the elem_id of gr.Image) */
+        #current-image-display {
+            height: 600px;           /* Define container height */
+            width: 100%;             /* Define container width (takes column width) */
+            display: flex;           /* Use flexbox for alignment */
+            justify-content: center; /* Center content horizontally */
+            align-items: center;     /* Center content vertically */
+            overflow: hidden;        /* Hide any potential overflow from container */
+        }
+    
+        /* The actual <img> element inside the container */
+        #current-image-display img {
+            object-fit: contain !important;    /* Scale keeping aspect ratio, within bounds */
+            max-width: 100%;        /* Prevent image exceeding container width */
+            max-height: 600px !important;       /* Prevent image exceeding container height */
+            width: auto;            /* Use natural width unless constrained by max-width */
+            height: auto;           /* Use natural height unless constrained by max-height */
+            display: block;         /* Ensure image behaves predictably in flex */
+        }
+        
+        /* Custom style for model info display */
+        #model-info-display {
+            font-size: 0.85rem;     /* Small font size */
+            color: #666;            /* Subtle color */
+            margin-top: 0.5rem;     /* Small top margin */
+            margin-bottom: 1rem;    /* Bottom margin before next element */
+            padding-left: 0.5rem;   /* Slight indentation */
+        }
+    """
+    # --- Pass css to gr.Blocks ---
+    with gr.Blocks(theme=gr.themes.Monochrome(), css=custom_css) as demo:
+        with gr.Row():
+            with gr.Column(scale=3):
+                gr.Markdown("# MATCHA: Museum Alt-Text for Cultural Heritage with AI 🍵 🌿")
+                gr.Markdown("Upload one or more images to generate accessible alternative text (designed to meet WCAG Guidelines)")
+                gr.Markdown("Developed by the Natural History Museum in Partnership with National Museums Liverpool. Funded by the DCMS Pilot Scheme")
+            with gr.Column(scale=1):
+                with gr.Row():
+                    gr.Image("images/nhm_logo.png", show_label=False, height=120,
+                             interactive=False, show_download_button=False,
+                             show_share_button=False, show_fullscreen_button=False,
+                             container=False, elem_id="nhm-logo")
+                    gr.Image("images/nml_logo.png", show_label=False, height=120,
+                             interactive=False, show_download_button=False,
+                             show_share_button=False, show_fullscreen_button=False,
+                             container=False, elem_id="nml-logo")
+
+        # Store model choices and state
+        show_all_models_state = gr.State(False)
+
+        # Define preferred and additional models directly in the function
+        preferred_models = [
+            ("Gemini 2.0 Flash (cheap)", "google/gemini-2.0-flash-001"),
+            ("GPT-4.1 Mini", "gpt-4.1-mini"),
+            ("GPT-4.1 (Recommended)", "gpt-4.1"),
+            ("Claude 3.7 Sonnet", "anthropic/claude-3.7-sonnet"),
+            ("Gemini 2.5 Pro", "google/gemini-2.5-pro-preview-03-25"),
+            ("Gemini 2.5 Flash Thinking (Recommended)", "google/gemini-2.5-flash-preview:thinking")
+        ]
+        
+        additional_models = [
+            ("GPT-4.1 Nano", "gpt-4.1-nano"),
+            ("ChatGPT Latest", "openai/chatgpt-4o-latest"),
+            ("Llama 4 Maverick", "meta-llama/llama-4-maverick")
+        ]
+        
+        # Calculate all models once
+        all_models_list = preferred_models + additional_models
+        
+        # Default model value
+        default_model = "google/gemini-2.0-flash-001"
+        
+        with gr.Row():
+            # Left column: Controls and uploads
+            with gr.Column(scale=1):
+                upload_button = gr.UploadButton(
+                    "Click to Upload Images",
+                    file_types=["image"],
+                    file_count="multiple"
+                )
+                
+                # Model dropdown
+                model_choice = gr.Dropdown(
+                    choices=preferred_models, 
+                    label="Select Model",
+                    value=default_model
+                )
+                
+                length_choice = gr.Radio(
+                    choices=["short", "medium", "long"], 
+                    label="Response Length",
+                    value="medium", 
+                    info="Short: max 130 chars | Medium: 250-300 chars | Long: max 450 chars"
+                )
+                
+                # Advanced settings accordion
+                with gr.Accordion("Advanced Settings", open=False):
+                    show_all_models = gr.Checkbox(
+                        label="Show Additional Models", 
+                        value=False,
+                        info="Display additional model options in the dropdown above"
+                    )
+                    
+                    content_type = gr.Radio(
+                        choices=["Museum Object", "Photography"], 
+                        label="Content Type",
+                        value="Museum Object"
+                    )
+                
+                # Find the default model's display name
+                default_model_name = "Unknown Model"
+                for name, value in preferred_models:
+                    if value == default_model:
+                        default_model_name = name
+                        break
+                
+                model_info = gr.Markdown(
+                    f"""**Current Model**: {default_model_name}  
+                    **Estimated cost per 100 Images**: {MODEL_PRICING[default_model]}""",
+                    elem_id="model-info-display"
+                )
+                
+                gr.Markdown("### Uploaded Images")
+                input_gallery = gr.Gallery(
+                    label="Uploaded Image Previews", columns=3, height=150,
+                    object_fit="contain", show_label=False
+                )
+                analyze_button = gr.Button("Generate Alt-Text", variant="primary", size="lg")
+                image_state = gr.State([])
+                filename_state = gr.State([])
+                csv_download = gr.File(label="Download CSV Results")
+
+            # Right column: Display area
+            with gr.Column(scale=2):
+                current_image = gr.Image(
+                    label="Current Image",
+                    type="filepath",
+                    elem_id="current-image-display",
+                    show_fullscreen_button=True,
+                    show_download_button=False,
+                    show_share_button=False,
+                    show_label=False
+                 )
+
+                with gr.Row():
+                    prev_button = gr.Button("← Previous", size="sm")
+                    image_counter = gr.Markdown("0 of 0", elem_id="image-counter")
+                    next_button = gr.Button("Next →", size="sm")
+
+                gr.Markdown("### Generated Alt-text")
+                analysis_text = gr.Textbox(
+                    label="Generated Text",
+                    value="Upload images and click 'Generate Alt-Text'.",
+                    lines=6, max_lines=10, interactive=True, show_label=False
+                )
+                current_index = gr.State(0)
+                all_images = gr.State([])
+                all_results = gr.State([])
+        
+        # Handle checkbox change to update model dropdown - modern version
+        def toggle_models(show_all, current_model):
+            # Make a fresh copy of the models lists to avoid any reference issues
+            preferred_choices = list(preferred_models)
+            all_choices = list(all_models_list)
+            
+            if show_all:
+                # When showing all models, use the fresh copy of all models
+                return gr.Dropdown(choices=all_choices, value=current_model)
+            else:
+                # Check if current model is in preferred models list
+                preferred_values = [value for _, value in preferred_choices]
+                
+                if current_model in preferred_values:
+                    # Keep the current model if it's in preferred models
+                    return gr.Dropdown(choices=preferred_choices, value=current_model)
+                else:
+                    # Reset to default model if current model is not in preferred models
+                    return gr.Dropdown(choices=preferred_choices, value=default_model)
+
+        # Update model info when model selection changes
+        def update_model_info(model_value):
+            # Find display name
+            model_name = "Unknown Model"
+            for name, value in all_models_list:
+                if value == model_value:
+                    model_name = name
+                    break
+            
+            # Get cost
+            cost = MODEL_PRICING.get(model_value, "Unknown")
+            
+            # Create markdown
+            return f"""**Current Model**: {model_name}  
+                   **Estimated cost per 100 Images**: {cost}"""
+                
+        # Connect checkbox to toggle model choices
+        show_all_models.change(
+            fn=toggle_models,
+            inputs=[show_all_models, model_choice],
+            outputs=[model_choice]
+        )
+        
+        # Connect model selection to update info
+        model_choice.change(
+            fn=update_model_info,
+            inputs=[model_choice],
+            outputs=[model_info]
+        )
+        
+        # Handle file uploads
+        def handle_upload(files, current_paths, current_filenames):
+            file_paths = []
+            file_names = []
+            if files:
+                for file in files:
+                    file_paths.append(file.name)
+                    file_names.append(get_base_filename(file.name))
+            return file_paths, file_paths, file_names, 0, None, "0 of 0", "Upload images and click 'Generate Alt-Text'."
+
+        upload_button.upload(
+            fn=handle_upload,
+            inputs=[upload_button, image_state, filename_state],
+            outputs=[input_gallery, image_state, filename_state,
+                     current_index, current_image, image_counter, analysis_text]
+        )
+
+        # Analyze images
+        def analyze_images(image_paths, model_choice, length_choice, filenames, content_type_choice):
+            if not image_paths:
+                return [], [], 0, None, "0 of 0", "No images uploaded to analyze.", None
+
+            is_photography = content_type_choice == "Photography"
+            sys_prompt = get_sys_prompt(length_choice, photograph=is_photography)
+            image_results = []
+            analysis_progress = gr.Progress(track_tqdm=True)
+
+            for i, image_path in enumerate(analysis_progress.tqdm(image_paths, desc="Analyzing Images")):
+                image_id = filenames[i] if i < len(filenames) and filenames[i] else f"Image_{i+1}_{os.path.basename(image_path)}"
+                try:
+                    img = Image.open(image_path)
+                    prompt0 = prompt_new()
+                    model_name = model_choice
+                    client_to_use = OR # Default client
+
+                    result = client_to_use.generate_caption(
+                        img, model=model_name, max_image_size=512,
+                        prompt=prompt0, prompt_dev=sys_prompt, temperature=1
+                    )
+                    image_results.append({"image_id": image_id, "content": result.strip()})
+                except FileNotFoundError:
+                     error_message = f"Error: File not found at path '{image_path}'"
+                     print(error_message)
+                     image_results.append({"image_id": image_id, "content": error_message})
+                except Exception as e:
+                    error_message = f"Error processing {image_id}: {str(e)}"
+                    print(error_message)
+                    image_results.append({"image_id": image_id, "content": error_message})
+
+            csv_path = create_csv_file_simple(image_results)
+            initial_image = image_paths[0] if image_paths else None
+            initial_counter = f"1 of {len(image_paths)}" if image_paths else "0 of 0"
+            initial_text = image_results[0]["content"] if image_results else "Analysis complete, but no results generated."
+
+            return (image_paths, image_results, 0, initial_image, initial_counter,
+                    initial_text, csv_path)
+
+        # Navigate previous
+        def go_to_prev(current_idx, images, results):
+            if not images or not results or len(images) == 0:
+                return current_idx, None, "0 of 0", ""
+            new_idx = (current_idx - 1 + len(images)) % len(images)
+            counter_text = f"{new_idx + 1} of {len(images)}"
+            result_content = results[new_idx]["content"] if new_idx < len(results) else "Error: Result not found"
+            return (new_idx, images[new_idx], counter_text, result_content)
+
+        # Navigate next
+        def go_to_next(current_idx, images, results):
+            if not images or not results or len(images) == 0:
+                return current_idx, None, "0 of 0", ""
+            new_idx = (current_idx + 1) % len(images)
+            counter_text = f"{new_idx + 1} of {len(images)}"
+            result_content = results[new_idx]["content"] if new_idx < len(results) else "Error: Result not found"
+            return (new_idx, images[new_idx], counter_text, result_content)
+
+        # Connect analyze button
+        analyze_button.click(
+            fn=analyze_images,
+            inputs=[image_state, model_choice, length_choice, filename_state, content_type],
+            outputs=[all_images, all_results, current_index, current_image, image_counter,
+                     analysis_text, csv_download]
+        )
+
+        # Connect navigation buttons
+        prev_button.click(
+            fn=go_to_prev, inputs=[current_index, all_images, all_results],
+            outputs=[current_index, current_image, image_counter, analysis_text], queue=False
+        )
+        next_button.click(
+            fn=go_to_next, inputs=[current_index, all_images, all_results],
+            outputs=[current_index, current_image, image_counter, analysis_text], queue=False
+        )
+
+        # About section
+        with gr.Accordion("About", open=False):
+             gr.Markdown("""
+            ## About MATCHA 🍵:
+            
+            This demo generates alternative text for images.
+            
+            - Upload one or more images using the upload button
+            - Choose a model and response length for generation
+            - Navigate through the images with the Previous and Next buttons
+            - Download CSV with all results
+            
+            Developed by the Natural History Museum in Partnership with National Museums Liverpool.
+            
+            If you find any bugs/have any problems/have any suggestions please feel free to get in touch:
+            chris.addis@nhm.ac.uk
+            """)
+
+    return demo
+
+# Launch the app
+if __name__ == "__main__":
+    app = create_demo()
+    app.launch()

app.py

@@ -1,425 +1,107 @@
 import gradio as gr
-import numpy as np
-from PIL import Image
-import io
 import os
-import requests
-import json
-from dotenv import load_dotenv
-import openai
-import base64
-import csv
-import tempfile
-import datetime
 
-# Load environment variables from .env file if it exists (for local development)
-# On Hugging Face Spaces, the secrets are automatically available as environment variables
-if os.path.exists(".env"):
-    load_dotenv()
-
-from io import BytesIO
-import numpy as np
-import requests
-from PIL import Image
-
-# import libraries
-from library.utils_model import *
-from library.utils_html import *
-from library.utils_prompt import *
-
-OR = OpenRouterAPI()
-
-# Path for storing user preferences
-PREFERENCES_FILE = "data/user_preferences.csv"
-
-# Ensure directory exists
-os.makedirs(os.path.dirname(PREFERENCES_FILE), exist_ok=True)
-
-# Define model pricing information (approximate costs per 100 image API calls)
-MODEL_PRICING = {
-    "google/gemini-2.0-flash-001": "$0.03",
-    "gpt-4.1-mini": "$0.07",
-    "gpt-4.1": "$0.35",
-    "anthropic/claude-3.7-sonnet": "$0.70",
-    "google/gemini-2.5-pro-preview-03-25": "$1.20",
-    "google/gemini-2.5-flash-preview:thinking": "$0.35",
-    "gpt-4.1-nano": "$0.02",
-    "openai/chatgpt-4o-latest": "$0.75",
-    "meta-llama/llama-4-maverick": "$0.04"
-}
-
-def get_sys_prompt(length="medium", photograph=False):
-    extra_prompt = ""
-
-    if photograph:
-        object_type = "wildlife photography"
-        extra_prompt = " Do not guess the exact species of the animals in the photograph unless you are certain - simply use a broader terms e.g. the genus or family to make less errors, "
-    else:
-        object_type = "museum objects"
-        
-    dev_prompt = f"""You are a museum curator tasked with generating long descriptions (as defined by W3C) of {object_type} for visually impaired and blind users from images. Use British English and follow museum accessibility best practices. Do not start with phrases like 'The image shows', 'This is an image of', 'The photograph'. Be precise, concise and avoid filler and subjective statements."""
-    
-    if length == "short":
-        dev_prompt = f"""You are a museum curator tasked with generating alt-text (as defined by W3C) of {object_type} for visually impaired and blind users from images. Use British English and follow museum accessibility best practices. Do not start with phrases like 'The image shows' or 'This is an image of'. Be precise, concise and avoid filler and subjective statements. Repsonses should be a maximum of 130 characters."""
-    elif length == "medium":
-        dev_prompt += " Repsonses should be a maximum of 250-300 characters."
-    else: # long
-        dev_prompt += " Repsonses should be a maximum of 450 characters."
-    return dev_prompt + extra_prompt
-
-
-def create_csv_file_simple(results):
-    """Create a CSV file from the results and return the path"""
-    try:
-        with tempfile.NamedTemporaryFile(mode='w', suffix='.csv', delete=False, newline='', encoding='utf-8') as f:
-            path = f.name
-            writer = csv.writer(f)
-            writer.writerow(['image_id', 'content'])
-            for result in results:
-                writer.writerow([
-                    result.get('image_id', ''),
-                    result.get('content', '')
-                ])
-        return path
-    except Exception as e:
-        print(f"Error creating CSV: {e}")
-        return None
-
-def get_base_filename(filepath):
-    if not filepath:
-        return ""
-    basename = os.path.basename(filepath)
-    filename = os.path.splitext(basename)[0]
-    return filename
-
-# Define the Gradio interface
-def create_demo():
-    # --- Updated CSS with model info styling ---
-    custom_css = """
-    /* Container for the image component (#current-image-display is the elem_id of gr.Image) */
-        #current-image-display {
-            height: 600px;           /* Define container height */
-            width: 100%;             /* Define container width (takes column width) */
-            display: flex;           /* Use flexbox for alignment */
-            justify-content: center; /* Center content horizontally */
-            align-items: center;     /* Center content vertically */
-            overflow: hidden;        /* Hide any potential overflow from container */
-        }
-    
-        /* The actual <img> element inside the container */
-        #current-image-display img {
-            object-fit: contain !important;    /* Scale keeping aspect ratio, within bounds */
-            max-width: 100%;        /* Prevent image exceeding container width */
-            max-height: 600px !important;       /* Prevent image exceeding container height */
-            width: auto;            /* Use natural width unless constrained by max-width */
-            height: auto;           /* Use natural height unless constrained by max-height */
-            display: block;         /* Ensure image behaves predictably in flex */
-        }
-        
-        /* Custom style for model info display */
-        #model-info-display {
-            font-size: 0.85rem;     /* Small font size */
-            color: #666;            /* Subtle color */
-            margin-top: 0.5rem;     /* Small top margin */
-            margin-bottom: 1rem;    /* Bottom margin before next element */
-            padding-left: 0.5rem;   /* Slight indentation */
-        }
+def check_access(request: gr.Request):
     """
-    # --- Pass css to gr.Blocks ---
-    with gr.Blocks(theme=gr.themes.Monochrome(), css=custom_css) as demo:
-        with gr.Row():
-            with gr.Column(scale=3):
-                gr.Markdown("# MATCHA: Museum Alt-Text for Cultural Heritage with AI 🍵 🌿")
-                gr.Markdown("Upload one or more images to generate accessible alternative text (designed to meet WCAG Guidelines)")
-                gr.Markdown("Developed by the Natural History Museum in Partnership with National Museums Liverpool. Funded by the DCMS Pilot Scheme")
-            with gr.Column(scale=1):
-                with gr.Row():
-                    gr.Image("images/nhm_logo.png", show_label=False, height=120,
-                             interactive=False, show_download_button=False,
-                             show_share_button=False, show_fullscreen_button=False,
-                             container=False, elem_id="nhm-logo")
-                    gr.Image("images/nml_logo.png", show_label=False, height=120,
-                             interactive=False, show_download_button=False,
-                             show_share_button=False, show_fullscreen_button=False,
-                             container=False, elem_id="nml-logo")
-
-        # Store model choices and state
-        show_all_models_state = gr.State(False)
-
-        # Define preferred and additional models directly in the function
-        preferred_models = [
-            ("Gemini 2.0 Flash (cheap)", "google/gemini-2.0-flash-001"),
-            ("GPT-4.1 Mini", "gpt-4.1-mini"),
-            ("GPT-4.1 (Recommended)", "gpt-4.1"),
-            ("Claude 3.7 Sonnet", "anthropic/claude-3.7-sonnet"),
-            ("Gemini 2.5 Pro", "google/gemini-2.5-pro-preview-03-25"),
-            ("Gemini 2.5 Flash Thinking (Recommended)", "google/gemini-2.5-flash-preview:thinking")
-        ]
-        
-        additional_models = [
-            ("GPT-4.1 Nano", "gpt-4.1-nano"),
-            ("ChatGPT Latest", "openai/chatgpt-4o-latest"),
-            ("Llama 4 Maverick", "meta-llama/llama-4-maverick")
-        ]
-        
-        # Calculate all models once
-        all_models_list = preferred_models + additional_models
-        
-        # Default model value
-        default_model = "google/gemini-2.0-flash-001"
-        
-        with gr.Row():
-            # Left column: Controls and uploads
-            with gr.Column(scale=1):
-                upload_button = gr.UploadButton(
-                    "Click to Upload Images",
-                    file_types=["image"],
-                    file_count="multiple"
-                )
-                
-                # Model dropdown
-                model_choice = gr.Dropdown(
-                    choices=preferred_models, 
-                    label="Select Model",
-                    value=default_model
-                )
-                
-                length_choice = gr.Radio(
-                    choices=["short", "medium", "long"], 
-                    label="Response Length",
-                    value="medium", 
-                    info="Short: max 130 chars | Medium: 250-300 chars | Long: max 450 chars"
-                )
-                
-                # Advanced settings accordion
-                with gr.Accordion("Advanced Settings", open=False):
-                    show_all_models = gr.Checkbox(
-                        label="Show Additional Models", 
-                        value=False,
-                        info="Display additional model options in the dropdown above"
-                    )
-                    
-                    content_type = gr.Radio(
-                        choices=["Museum Object", "Photography"], 
-                        label="Content Type",
-                        value="Museum Object"
-                    )
-                
-                # Find the default model's display name
-                default_model_name = "Unknown Model"
-                for name, value in preferred_models:
-                    if value == default_model:
-                        default_model_name = name
-                        break
-                
-                # Model info outside the accordion
-                model_info = gr.Markdown(
-                    f"""**Current Model**: {default_model_name}  
-                    **Cost per 100 Images**: {MODEL_PRICING[default_model]}""",
-                    elem_id="model-info-display"
-                )
-                
-                gr.Markdown("### Uploaded Images")
-                input_gallery = gr.Gallery(
-                    label="Uploaded Image Previews", columns=3, height=150,
-                    object_fit="contain", show_label=False
-                )
-                analyze_button = gr.Button("Generate Alt-Text", variant="primary", size="lg")
-                image_state = gr.State([])
-                filename_state = gr.State([])
-                csv_download = gr.File(label="Download CSV Results")
-
-            # Right column: Display area
-            with gr.Column(scale=2):
-                current_image = gr.Image(
-                    label="Current Image",
-                    type="filepath",
-                    elem_id="current-image-display",
-                    show_fullscreen_button=True,
-                    show_download_button=False,
-                    show_share_button=False,
-                    show_label=False
-                 )
-
-                with gr.Row():
-                    prev_button = gr.Button("← Previous", size="sm")
-                    image_counter = gr.Markdown("0 of 0", elem_id="image-counter")
-                    next_button = gr.Button("Next →", size="sm")
-
-                gr.Markdown("### Generated Alt-text")
-                analysis_text = gr.Textbox(
-                    label="Generated Text",
-                    value="Upload images and click 'Generate Alt-Text'.",
-                    lines=6, max_lines=10, interactive=True, show_label=False
-                )
-                current_index = gr.State(0)
-                all_images = gr.State([])
-                all_results = gr.State([])
-        
-        # Handle checkbox change to update model dropdown - modern version
-        def toggle_models(show_all, current_model):
-            # Make a fresh copy of the models lists to avoid any reference issues
-            preferred_choices = list(preferred_models)
-            all_choices = list(all_models_list)
-            
-            if show_all:
-                # When showing all models, use the fresh copy of all models
-                return gr.Dropdown(choices=all_choices, value=current_model)
-            else:
-                # Check if current model is in preferred models list
-                preferred_values = [value for _, value in preferred_choices]
-                
-                if current_model in preferred_values:
-                    # Keep the current model if it's in preferred models
-                    return gr.Dropdown(choices=preferred_choices, value=current_model)
-                else:
-                    # Reset to default model if current model is not in preferred models
-                    return gr.Dropdown(choices=preferred_choices, value=default_model)
-
-        # Update model info when model selection changes
-        def update_model_info(model_value):
-            # Find display name
-            model_name = "Unknown Model"
-            for name, value in all_models_list:
-                if value == model_value:
-                    model_name = name
-                    break
-            
-            # Get cost
-            cost = MODEL_PRICING.get(model_value, "Unknown")
-            
-            # Create markdown
-            return f"""**Current Model**: {model_name}  
-                   **Cost per 100 Images**: {cost}"""
-                
-        # Connect checkbox to toggle model choices
-        show_all_models.change(
-            fn=toggle_models,
-            inputs=[show_all_models, model_choice],
-            outputs=[model_choice]
-        )
-        
-        # Connect model selection to update info
-        model_choice.change(
-            fn=update_model_info,
-            inputs=[model_choice],
-            outputs=[model_info]
-        )
-        
-        # Handle file uploads
-        def handle_upload(files, current_paths, current_filenames):
-            file_paths = []
-            file_names = []
-            if files:
-                for file in files:
-                    file_paths.append(file.name)
-                    file_names.append(get_base_filename(file.name))
-            return file_paths, file_paths, file_names, 0, None, "0 of 0", "Upload images and click 'Generate Alt-Text'."
-
-        upload_button.upload(
-            fn=handle_upload,
-            inputs=[upload_button, image_state, filename_state],
-            outputs=[input_gallery, image_state, filename_state,
-                     current_index, current_image, image_counter, analysis_text]
-        )
-
-        # Analyze images
-        def analyze_images(image_paths, model_choice, length_choice, filenames, content_type_choice):
-            if not image_paths:
-                return [], [], 0, None, "0 of 0", "No images uploaded to analyze.", None
-
-            is_photography = content_type_choice == "Photography"
-            sys_prompt = get_sys_prompt(length_choice, photograph=is_photography)
-            image_results = []
-            analysis_progress = gr.Progress(track_tqdm=True)
-
-            for i, image_path in enumerate(analysis_progress.tqdm(image_paths, desc="Analyzing Images")):
-                image_id = filenames[i] if i < len(filenames) and filenames[i] else f"Image_{i+1}_{os.path.basename(image_path)}"
-                try:
-                    img = Image.open(image_path)
-                    prompt0 = prompt_new()
-                    model_name = model_choice
-                    client_to_use = OR # Default client
-
-                    result = client_to_use.generate_caption(
-                        img, model=model_name, max_image_size=512,
-                        prompt=prompt0, prompt_dev=sys_prompt, temperature=1
-                    )
-                    image_results.append({"image_id": image_id, "content": result.strip()})
-                except FileNotFoundError:
-                     error_message = f"Error: File not found at path '{image_path}'"
-                     print(error_message)
-                     image_results.append({"image_id": image_id, "content": error_message})
-                except Exception as e:
-                    error_message = f"Error processing {image_id}: {str(e)}"
-                    print(error_message)
-                    image_results.append({"image_id": image_id, "content": error_message})
-
-            csv_path = create_csv_file_simple(image_results)
-            initial_image = image_paths[0] if image_paths else None
-            initial_counter = f"1 of {len(image_paths)}" if image_paths else "0 of 0"
-            initial_text = image_results[0]["content"] if image_results else "Analysis complete, but no results generated."
-
-            return (image_paths, image_results, 0, initial_image, initial_counter,
-                    initial_text, csv_path)
-
-        # Navigate previous
-        def go_to_prev(current_idx, images, results):
-            if not images or not results or len(images) == 0:
-                return current_idx, None, "0 of 0", ""
-            new_idx = (current_idx - 1 + len(images)) % len(images)
-            counter_text = f"{new_idx + 1} of {len(images)}"
-            result_content = results[new_idx]["content"] if new_idx < len(results) else "Error: Result not found"
-            return (new_idx, images[new_idx], counter_text, result_content)
-
-        # Navigate next
-        def go_to_next(current_idx, images, results):
-            if not images or not results or len(images) == 0:
-                return current_idx, None, "0 of 0", ""
-            new_idx = (current_idx + 1) % len(images)
-            counter_text = f"{new_idx + 1} of {len(images)}"
-            result_content = results[new_idx]["content"] if new_idx < len(results) else "Error: Result not found"
-            return (new_idx, images[new_idx], counter_text, result_content)
-
-        # Connect analyze button
-        analyze_button.click(
-            fn=analyze_images,
-            inputs=[image_state, model_choice, length_choice, filename_state, content_type],
-            outputs=[all_images, all_results, current_index, current_image, image_counter,
-                     analysis_text, csv_download]
-        )
-
-        # Connect navigation buttons
-        prev_button.click(
-            fn=go_to_prev, inputs=[current_index, all_images, all_results],
-            outputs=[current_index, current_image, image_counter, analysis_text], queue=False
-        )
-        next_button.click(
-            fn=go_to_next, inputs=[current_index, all_images, all_results],
-            outputs=[current_index, current_image, image_counter, analysis_text], queue=False
+    Checks if the user is logged in via HF OAuth and if they are authorized
+    for full access.
+    """
+    user_info = request.auth # request.auth contains user info if hf_oauth is enabled
+
+    if user_info is None:
+        # User is not logged in
+        return (
+            gr.update(visible=True),  # Show login prompt
+            gr.update(visible=False), # Hide full content
+            gr.update(visible=True),  # Show limited content
+            "Please sign in to check your access.",
+            "Not Logged In"
         )
-
-        # About section
-        with gr.Accordion("About", open=False):
-             gr.Markdown("""
-            ## About MATCHA 🍵:
-            
-            This demo generates alternative text for images.
-            
-            - Upload one or more images using the upload button
-            - Choose a model and response length for generation
-            - Navigate through the images with the Previous and Next buttons
-            - Download CSV with all results
-            
-            Developed by the Natural History Museum in Partnership with National Museums Liverpool.
-            
-            If you find any bugs/have any problems/have any suggestions please feel free to get in touch:
-            chris.addis@nhm.ac.uk
-            """)
-
-    return demo
-
-# Launch the app
-if __name__ == "__main__":
-    app = create_demo()
-    app.launch()
+    else:
+        # User is logged in, get their HF user ID
+        user_id = user_info.get('sub') # 'sub' is the standard claim for user ID in OpenID Connect
+        username = user_info.get('preferred_username', 'N/A') # Or use other claims like 'name', 'email'
+
+        print(f"Logged in user: Username={username}, ID={user_id}") # Log for debugging
+
+        if user_id in AUTHORIZED_USER_IDS:
+            # User is authorized for full access
+            return (
+                gr.update(visible=False), # Hide login prompt
+                gr.update(visible=True),  # Show full content
+                gr.update(visible=False), # Hide limited content
+                f"Welcome, {username}! You have Full Access.",
+                f"Logged in as: {username} (ID: {user_id})"
+            )
+        else:
+            # User is logged in but not authorized for full access
+            return (
+                gr.update(visible=False), # Hide login prompt
+                gr.update(visible=False), # Hide full content
+                gr.update(visible=True),  # Show limited content
+                f"Welcome, {username}. You have Limited Access.",
+                f"Logged in as: {username} (ID: {user_id})"
+            )
+
+# --- Gradio Interface ---
+
+with gr.Blocks() as demo:
+    # We'll use state to manage visibility based on access
+    login_prompt = gr.Markdown("### Sign in with Hugging Face to check your access.")
+    login_status_text = gr.Textbox(label="Status", interactive=False)
+
+    # The gr.LoginButton component initiates the HF OAuth flow
+    # This button itself doesn't need a click handler in this pattern;
+    # the check_access function runs on page load or other events.
+    # However, we can add a dummy event or rely on the initial load.
+    # For demonstration, we'll have the check happen on page load
+    # and re-check maybe with a button click if needed, but the
+    # primary check should handle the redirect return.
+    # A common pattern is to just rely on the state after redirect.
+
+    with gr.Column(visible=False) as limited_content:
+        gr.Markdown("## Limited Access Content")
+        gr.Textbox(value="This is content available to all users (or non-authorized logged-in users).", interactive=False)
+        # Add other limited features here
+
+    with gr.Column(visible=False) as full_content:
+        gr.Markdown("## Full Access Content")
+        gr.Textbox(value="🥳 This is special content for authorized users! 🥳", interactive=False)
+        # Add other full features here
+
+    # Initial check on page load is tricky with Blocks and redirects.
+    # A more reliable way is often to have a button that triggers the check
+    # after the potential redirect or on first load, or use a mechanism
+    # that runs logic on startup. For a simple example, let's add a check button.
+    # In a real app, the check_access logic might be called by a function
+    # that runs on initial page load or tied to a state change after login redirect.
+
+    # Let's simulate the check happening after potential login on page load
+    # by linking it to a component that is present from the start, or by
+    # calling the check function and updating outputs.
+    # Gradio often re-runs parts of the app logic after OAuth redirect.
+    # The outputs of check_access will update the visibility of components.
+
+    # A more robust pattern in Gradio might involve using the `load` event
+    # or triggering `check_access` via a component interaction that happens
+    # after the page loads and the request object is populated.
+    # For simplicity in a runnable example, let's trigger check_access
+    # with a button after the page is loaded and the user *might* have just logged in.
+    # In a production app, you'd want this check to happen automatically.
+
+    check_access_button = gr.Button("Check My Access Level") # Button to manually trigger check
+
+    check_access_button.click(
+        fn=check_access,
+        inputs=None, # The request object is implicitly available
+        outputs=[login_prompt, full_content, limited_content, login_status_text, login_status_text], # Update multiple outputs
+        api_name="check_access" # Give it an API name if needed
+    )
+
+    # A more seamless approach in a real space would involve using Gradio's state
+    # and potentially calling check_access when the space state indicates a user is present.
+    # The gr.LoginButton handles the *initiation* of the login.
+    # The *result* of the login populates the request.auth object on subsequent interactions
+    # or potentially on page reload after redirect.
+
+demo.launch()