import gradio as gr
import os
from detector import analyze_text, get_components

# Pre-load model
print("Starting AI Text Detector...")
try:
    get_components()
    model_status = "✅ Model loaded successfully!"
except Exception as e:
    model_status = f"⚠️ Model loading issue: {str(e)}"

print(model_status)

def analyze_text_interface(text, threshold):
    """
    Interface function for Gradio
    """
    if not text or not text.strip():
        return "❌ Please enter some text to analyze.", ""
    
    try:
        result = analyze_text(text, threshold=threshold, chunk_size=80)
        
        if "error" in result:
            return f"❌ Error: {result['error']}", ""
        
        # Overall result - with proper text colors for dark background
        overall_html = f"""
        <div style="padding: 20px; border-radius: 10px; background: #2d3748; border: 1px solid #4a5568; color: white;">
            <h2 style="color: white; margin-top: 0;">Analysis Result: {result['overall_type']}</h2>
            <p style="color: white;"><strong>Confidence:</strong> {result['overall_confidence']:.2%}</p>
            <p style="color: white;"><strong>AI Probability Score:</strong> {result['overall_score']:.3f}</p>
            <p style="color: white;"><strong>AI Artifacts Detected:</strong> {'Yes' if result['has_artifacts'] else 'No'}</p>
        </div>
        """
        
        # Raw data for download
        raw_data = {
            "overall_type": result['overall_type'],
            "overall_confidence": result['overall_confidence'],
            "overall_score": result['overall_score'],
            "has_artifacts": result['has_artifacts'],
            "text_length": len(text)
        }
        
        return overall_html, str(raw_data)
        
    except Exception as e:
        return f"❌ Analysis failed: {str(e)}", ""

# Example texts
examples = [
    ["This is a sample text written by a human. It contains natural variations in writing style and occasional imperfections that make it authentic."],
    ["The aforementioned textual content exhibits characteristics consistent with AI-generated material, including syntactic patterns and lexical choices commonly associated with large language models."],
    ["Hello world! This is a test. I hope this works correctly. The weather is nice today."]
]

# Create Gradio interface
with gr.Blocks(title="AI Text Detector") as demo:
    gr.Markdown(
        """
        # 🔍 AI Text Detector
        *Detect AI-generated text using advanced machine learning models*
        
        **Model Status:** {}
        """.format(model_status)
    )
    
    with gr.Row():
        with gr.Column():
            text_input = gr.Textbox(
                label="Input Text",
                placeholder="Paste or type the text you want to analyze here...",
                lines=8,
                max_lines=20
            )
            
            threshold = gr.Slider(
                minimum=0.1,
                maximum=0.9,
                value=0.5,
                step=0.05,
                label="Detection Threshold",
                info="Higher values = more strict AI detection"
            )
            
            analyze_btn = gr.Button("Analyze Text", variant="primary")
            
            gr.Examples(
                examples=examples,
                inputs=text_input,
                label="Try these examples:"
            )
        
        with gr.Column():
            overall_output = gr.HTML(label="Analysis Result")
            raw_output = gr.Textbox(
                label="Raw Data (for download)",
                lines=4,
                max_lines=10
            )
    
    # Footer
    gr.Markdown(
        """
        ---
        **How it works:**
        - Text is analyzed by the AI detection model
        - Returns overall classification (Human/AI) with confidence score
        - Built with `abhi099k/ai-text-detector-v-n4.0` model
        
        **Note:** This tool provides probabilistic estimates and should be used as one of several indicators when evaluating text authenticity.
        """
    )
    
    # Connect the function
    analyze_btn.click(
        fn=analyze_text_interface,
        inputs=[text_input, threshold],
        outputs=[overall_output, raw_output]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )