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gpt-oss-RAG
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import gradio as gr

# Custom CSS for gradient background and styling
custom_css = """
.gradio-container {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 25%, #f093fb 50%, #4facfe 75%, #00f2fe 100%);
    background-size: 400% 400%;
    animation: gradient-animation 15s ease infinite;
    min-height: 100vh;
}

@keyframes gradient-animation {
    0% { background-position: 0% 50%; }
    50% { background-position: 100% 50%; }
    100% { background-position: 0% 50%; }
}

.dark .gradio-container {
    background: linear-gradient(135deg, #1a1a2e 0%, #16213e 25%, #0f3460 50%, #533483 75%, #e94560 100%);
    background-size: 400% 400%;
    animation: gradient-animation 15s ease infinite;
}

/* Style for the main content area */
.main-container {
    background-color: rgba(255, 255, 255, 0.95);
    backdrop-filter: blur(10px);
    border-radius: 20px;
    padding: 20px;
    box-shadow: 0 8px 32px 0 rgba(31, 38, 135, 0.37);
    border: 1px solid rgba(255, 255, 255, 0.18);
}

.dark .main-container {
    background-color: rgba(30, 30, 30, 0.95);
    border: 1px solid rgba(255, 255, 255, 0.1);
}

/* Sidebar styling */
.sidebar {
    background-color: rgba(255, 255, 255, 0.9);
    backdrop-filter: blur(10px);
    border-radius: 15px;
    padding: 20px;
    margin: 10px;
}

.dark .sidebar {
    background-color: rgba(40, 40, 40, 0.9);
}
"""

def load_model(model_name, signed_in):
    """Function to load different models based on selection"""
    if not signed_in:
        return gr.Info("Please sign in to use the models")
    
    # Here you would implement the actual model loading logic
    # For now, we'll return a placeholder
    return f"Model {model_name} loaded successfully!"

with gr.Blocks(fill_height=True, theme="Nymbo/Nymbo_Theme", css=custom_css) as demo:
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group(elem_classes="sidebar"):
                gr.Markdown("# 🚀 Inference Provider")
                gr.Markdown(
                    "This Space showcases OpenAI GPT-OSS models, served by the Cerebras API. "
                    "Sign in with your Hugging Face account to use this API."
                )
                
                # Model selection dropdown
                model_dropdown = gr.Dropdown(
                    choices=[
                        "openai/gpt-oss-120b",
                        "openai/gpt-oss-20b"
                    ],
                    value="openai/gpt-oss-120b",
                    label="Select Model",
                    info="Choose between different model sizes"
                )
                
                # Login button
                button = gr.LoginButton("Sign in with Hugging Face", size="lg")
                
                # Additional options
                with gr.Accordion("⚙️ Advanced Options", open=False):
                    temperature = gr.Slider(
                        minimum=0,
                        maximum=2,
                        value=0.7,
                        step=0.1,
                        label="Temperature"
                    )
                    max_tokens = gr.Slider(
                        minimum=1,
                        maximum=4096,
                        value=512,
                        step=1,
                        label="Max Tokens"
                    )
        
        with gr.Column(scale=3):
            with gr.Group(elem_classes="main-container"):
                # Dynamic model loading based on selection
                @gr.render(inputs=[model_dropdown, button])
                def render_model_interface(selected_model, login_status):
                    if selected_model == "openai/gpt-oss-120b":
                        gr.load(
                            "models/openai/gpt-oss-120b",
                            accept_token=login_status,
                            provider="fireworks-ai"
                        )
                    elif selected_model == "openai/gpt-oss-20b":
                        gr.load(
                            "models/openai/gpt-oss-20b",
                            accept_token=login_status,
                            provider="fireworks-ai"
                        )

demo.launch()