import gradio as gr
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import re

# Load AI detection model
MODEL_NAME = "roberta-base-openai-detector"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

# AI content detection function
def detect_ai_content(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    scores = torch.nn.functional.softmax(outputs.logits, dim=1)
    return scores[0][1].item()  # AI probability

# Writing style analysis
def stylometry_analysis(text):
    words = text.split()
    avg_word_length = sum(len(word) for word in words) / len(words) if words else 0
    complex_words_ratio = len([word for word in words if len(word) > 6]) / len(words) if words else 0
    passive_voice_count = len(re.findall(r'\b(is|was|were|has been|have been|had been)\b \w+ed', text))
    return avg_word_length, complex_words_ratio, passive_voice_count

# Semantic similarity analysis
def semantic_analysis(text):
    keywords = ["AI", "generated", "neural network", "LLM", "GPT", "transformer"]
    ai_patterns = sum([text.lower().count(keyword) for keyword in keywords])
    return ai_patterns / len(text.split()) if text.split() else 0

# Final AI Detection Logic
def analyze_text(text):
    ai_probability = detect_ai_content(text)
    avg_word_length, complex_words_ratio, passive_voice_count = stylometry_analysis(text)
    semantic_score = semantic_analysis(text)

    is_ai_generated = (
        ai_probability > 0.5 or 
        complex_words_ratio > 0.4 or 
        semantic_score > 0.2
    )

    result = "🟢 Human-Written" if not is_ai_generated else "🔴 AI-Generated"

    return {
        "Final Verdict": result,
        "AI Probability": round(ai_probability, 2),
        "Complex Words Ratio": round(complex_words_ratio, 2),
        "Passive Voice Count": passive_voice_count,
        "Semantic Score": round(semantic_score, 2)
    }

# Gradio UI
def create_interface():
    with gr.Blocks(title="AI Content Detector") as demo:
        # UI भाग
        gr.Markdown("# 🚀 Self-Learning AI Content Detector")
        
        with gr.Row():
            input_text = gr.Textbox(label="Enter Text", lines=5)
            analyze_btn = gr.Button("Analyze")
        
        with gr.Row():
            final_verdict = gr.Textbox(label="Final Verdict")
            ai_prob = gr.Number(label="AI Probability")
            complex_ratio = gr.Number(label="Complex Words Ratio")
            passive_voice = gr.Number(label="Passive Voice Count")
            semantic_score = gr.Number(label="Semantic Score")
        
        # API भाग
        gr.Markdown("## API Documentation")
        gr.HTML("""
            <div>
                <h3>API Usage:</h3>
                <p>POST to /api/analyze with JSON payload:</p>
                <pre>
{
    "text": "Your text here"
}
                </pre>
            </div>
        """)
        
        # इवेंट हैंडलिंग
        analyze_btn.click(
            fn=analyze_text,
            inputs=input_text,
            outputs=[final_verdict, ai_prob, complex_ratio, passive_voice, semantic_score],
            api_name="analyze"  # API एंडपॉइंट नाम
        )
    
    return demo

# मुख्य एप्लिकेशन
if __name__ == "__main__":
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        enable_api=True  # API एक्टिवेट करें
    )