# -*- coding: utf-8 -*-
"""
DEVIPUTRA AI // HUGGING FACE SPACES ZERO-GPU INFERENCE ENGINE
Full-stack Gradio + Cyberpunk HUD integration with Fine-Tuned Multilingual BERT (RTX-5050).
"""

# IMPORTANT: ZeroGPU requires importing spaces before torch or CUDA initialization
try:
    import spaces
except ImportError:
    spaces = None

import os
import sys
import base64
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import gradio as gr
import torch

# Ensure src modules are discoverable
current_dir = os.path.dirname(os.path.abspath(__file__))
if current_dir not in sys.path:
    sys.path.insert(0, current_dir)

from src.predict import HateSpeechPredictor

print("⚡ [Hugging Face Spaces] Initializing DEVIPUTRA AI Predictor...")
predictor = HateSpeechPredictor(models_dir=os.path.join(current_dir, 'models'))

# Prediction Function for Gradio
def analyze_text(text: str):
    if not text or not text.strip():
        return "Please input some text to analyze.", "Standby", {}

    result = predictor.predict(text)
    prediction = result.get('prediction', 'Normal')
    context = result.get('context')
    
    explanation = ""
    if context and context.get('reason'):
        explanation = f"\n\n🔍 Context Reason: {context.get('reason')}"

    status_message = f"Verdict: {prediction.upper()} {explanation}"
    
    if 'scores' in result and result['scores']:
        confidences = result['scores']
    elif prediction == 'Hate Speech':
        confidences = {'Hate Speech': 0.98, 'Offensive': 0.015, 'Normal': 0.005}
    elif prediction == 'Offensive':
        confidences = {'Offensive': 0.92, 'Hate Speech': 0.05, 'Normal': 0.03}
    else:
        confidences = {'Normal': 0.96, 'Offensive': 0.03, 'Hate Speech': 0.01}

    return status_message, prediction, confidences

# ZeroGPU decorator
if spaces is not None:
    analyze_text = spaces.GPU(analyze_text)

# Load Cyberpunk HUD HTML with embedded graphics
hud_file = os.path.join(current_dir, 'cyberpunk_hud.html')
hud_content = ""
if os.path.exists(hud_file):
    with open(hud_file, 'r', encoding='utf-8') as f:
        hud_content = f.read()
    vagabond_path = os.path.join(current_dir, 'vagabond.jpg')
    if os.path.exists(vagabond_path):
        with open(vagabond_path, 'rb') as img_f:
            b64_img = f"data:image/jpeg;base64,{base64.b64encode(img_f.read()).decode()}"
            hud_content = hud_content.replace('src="./vagabond.jpg"', f'src="{b64_img}"')

# Gradio Interface Blocks - Cyberpunk HUD is now the primary hero tab!
with gr.Blocks(title="DEVIPUTRA AI // Cyberpunk HUD & Multilingual BERT") as demo:
    gr.Markdown("""
    # ⚡ DEVIPUTRA AI // MULTILINGUAL BERT INFERENCE LAB
    ### Fine-Tuned on Hinglish, Hindi, Telugu, and English Hate Speech & Dual-Meaning Slangs
    """)

    if hud_content:
        with gr.Tab("🕹️ Cyberpunk HUD Deck (Interactive Neural HUD)"):
            gr.HTML(f"""
            <div style="width: 100%; height: 980px; overflow: auto; border: 1px solid #00f0ff; border-radius: 8px;">
                <iframe srcdoc="{hud_content.replace('"', '&quot;')}" style="width: 100%; height: 100%; border: none;"></iframe>
            </div>
            """)

    with gr.Tab("🧪 Real-Time BERT Model Scanner"):
        with gr.Row():
            with gr.Column(scale=1):
                input_text = gr.Textbox(
                    label="Input Text (Hindi, Hinglish, English, Telugu)",
                    placeholder="e.g., Mandir mein ghanta baj raha hai / I will hurt you...",
                    lines=3
                )
                analyze_btn = gr.Button("⚡ Run Deep Neural Scan", variant="primary")
                
                gr.Examples(
                    examples=[
                        ["Mandir mein aarti ke waqt bada ghanta baj raha hai."],
                        ["Tujhe ghanta farak nahi padta yahan se nikal."],
                        ["Mera kutta bahut cute aur loyal pet hai."],
                        ["Tu kutta hai chup baith ekdum."],
                        ["I will murder you tonight and destroy everything."],
                        ["Bhai tu bohot accha insaan hai yaar."]
                    ],
                    inputs=input_text
                )

            with gr.Column(scale=1):
                output_verdict = gr.Textbox(label="Decision & Context Analysis", interactive=False)
                output_label = gr.Label(label="Confidence Spectrum", num_top_classes=3)
                hidden_pred = gr.Textbox(visible=False)

        analyze_btn.click(
            fn=analyze_text,
            inputs=input_text,
            outputs=[output_verdict, hidden_pred, output_label]
        )

# FastAPI Backend server providing the /predict endpoint for the Cyberpunk HUD
server = FastAPI()

@server.post("/predict")
async def api_predict(request: Request):
    data = await request.json()
    text = data.get("text", "")
    res = predictor.predict(text)
    pred = res.get('prediction', 'Normal')
    ctx = res.get('context') or {}
    reason = ctx.get('reason', f"Contextual reasoning: {pred}")
    scores = res.get('scores', {})
    if not scores:
        if pred == 'Hate Speech':
            scores = {'Hate Speech': 0.98, 'Offensive': 0.015, 'Normal': 0.005}
        elif pred == 'Offensive':
            scores = {'Offensive': 0.92, 'Hate Speech': 0.05, 'Normal': 0.03}
        else:
            scores = {'Normal': 0.96, 'Offensive': 0.03, 'Hate Speech': 0.01}
    conf = scores.get(pred, 0.95)
    return {
        "prediction": pred,
        "confidence": conf,
        "model_used": "Multilingual BERT (HingBERT)",
        "reason": reason,
        "scores": scores
    }

# Mount Gradio into FastAPI
app = gr.mount_gradio_app(server, demo, path="/")

if __name__ == "__main__":
    demo.launch()