import gradio as gr
from huggingface_hub import InferenceClient
import spaces
import os

# Dummy handshake for Zero-GPU boot scanner validation
@spaces.GPU
def zero_gpu_handshake():
    pass

def generate_text(prompt, system_instruction, temperature, user_token):
    if not prompt.strip():
        return "Please input a prompt to generate text."
        
    # Check for authorization credentials
    hf_token = user_token.strip() if user_token.strip() else os.environ.get("HF_TOKEN")
    
    try:
        # Initialize client targeting the latest Z.ai flagship: zai-org/GLM-5.2
        client = InferenceClient(model="zai-org/GLM-5.2", token=hf_token)
        
        messages = []
        if system_instruction.strip():
            messages.append({"role": "system", "content": system_instruction.strip()})
        messages.append({"role": "user", "content": prompt.strip()})
        
        # Max out parameters to fully utilize GLM-5.2's massive context capabilities
        response = client.chat_completion(
            messages=messages,
            max_tokens=16384, # Maximum supported token extraction payload
            temperature=float(temperature),
            top_p=0.95
        )
        
        return response.choices[0].message.content
        
    except Exception as e:
        error_msg = str(e)
        if "api_key" in error_msg or "Authorization" in error_msg or "401" in error_msg:
            return "❌ Token Required: GLM-5.2 requires a Hugging Face token via Serverless endpoints.\n\n👉 Set an 'HF_TOKEN' secret inside your Space's Settings tab, or paste your token into the field below."
        return f"Error executing request: {error_msg}"

def apply_logic_preset():
    return 0.1  # Set ultra-low temp configuration for strict, reasoning, and dataset syntax architectures

# --- GRADIO INTERFACE CONFIGURATION ---
with gr.Blocks(title="GLM-5.2 Agent Workspace") as demo:
    gr.Markdown("# 🚀 GLM-5.2 Flagship Agent Workspace")
    gr.Markdown("Direct interface for Z.ai's latest **GLM-5.2** model. Optimized for long-horizon reasoning, dataset synthesis, and codebase tasks.")
    
    with gr.Row():
        with gr.Column(scale=2):
            system_prompt = gr.Textbox(
                label="⚙️ System Persona / Core Instructions",
                placeholder="e.g., You are an advanced programming assistant. Generate code cleanly without conversational filler.",
                lines=2
            )
            user_prompt = gr.Textbox(
                label="📝 Context / Input Prompt",
                placeholder="Enter repository code, logical prompts, or structural guidelines...",
                lines=12
            )
            
            with gr.Row():
                submit_btn = gr.Button("🚀 Execute Generation", variant="primary")
                preset_btn = gr.Button("📊 Force Logic/Dataset Preset (Temp 0.1)")
                
        with gr.Column(scale=3):
            output_display = gr.Textbox(
                label="📥 Generated Output Stream",
                lines=16,
                interactive=False
            )
            
            with gr.Accordion("🛠️ Settings & Hyperparameters", open=True):
                token_input = gr.Textbox(
                    label="🔑 Optional HF Access Token", 
                    placeholder="hf_...", 
                    type="password"
                )
                temp_slider = gr.Slider(
                    minimum=0.01, 
                    maximum=1.5, 
                    value=0.1, 
                    step=0.05, 
                    label="Temperature"
                )

    # Component Connections
    submit_btn.click(
        fn=generate_text, 
        inputs=[user_prompt, system_prompt, temp_slider, token_input], 
        outputs=output_display
    )
    
    preset_btn.click(
        fn=apply_logic_preset,
        inputs=[],
        outputs=temp_slider
    )

demo.launch(theme=gr.themes.Soft())