import gradio as gr
from ai_text_detector_valid_final import detect_text

def analyze_text(user_text):
    return detect_text(user_text)

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🤖 AI vs Human Text Detector
        Paste any text below. Our system will analyze it using **three advanced models** to detect if the text is AI-generated or human-written.
        
        ### 📝 Formatting Preservation
        - Your **line breaks, spacing, and Markdown syntax** will be preserved exactly as you paste them.
        - You can view it as **raw text** or **rendered Markdown**.
        - Perfect for analyzing **code snippets, poetry, or Markdown documents**.
        
        Click **"🚀 Run Detection"** to start.
        """
    )

    with gr.Row():
        with gr.Column(scale=2):
            user_input = gr.Textbox(
                label="✍️ Enter Text",
                placeholder="Paste text here...",
                lines=12,
                type="text"
            )
            analyze_btn = gr.Button("🚀 Run Detection", variant="primary")

        with gr.Column(scale=1):
            final_output = gr.JSON(label="📊 Final Results")

    with gr.Row():
        with gr.Accordion("🔬 Detailed Model Results", open=False):
            model_output = gr.JSON(label="All Model Scores")

    # ✅ Two-tab preview: raw + markdown
    with gr.Tab("📄 Raw Text (Exact Preservation)"):
        raw_preview = gr.Textbox(
            label="📝 Raw Input",
            interactive=False,
            lines=20,
            show_copy_button=True
        )

    with gr.Tab("✨ Rendered Markdown"):
        md_preview = gr.Markdown()

    def run_analysis(user_text):
        results = analyze_text(user_text)
        return results, results, user_text, user_text

    analyze_btn.click(
        fn=run_analysis,
        inputs=user_input,
        outputs=[final_output, model_output, raw_preview, md_preview]
    )

demo.launch()