import gradio as gr
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import plotly.graph_objects as go
from datetime import datetime, timedelta
import random
import json

# --- Core Model Architectures ---

class LSTMModel(nn.Module):
    def __init__(self, input_size=15, hidden_size=128, num_layers=2, output_size=1):
        super(LSTMModel, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
    def forward(self, x):
        out, _ = self.lstm(x)
        return self.fc(out[:, -1, :])

class MLPModel(nn.Module):
    def __init__(self, input_size=25, hidden_size=128, output_size=1):
        super(MLPModel, self).__init__()
        self.net = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(hidden_size, hidden_size // 2),
            nn.ReLU(),
            nn.Linear(hidden_size // 2, output_size),
            nn.Sigmoid()
        )
    def forward(self, x):
        return self.net(x)

# --- Mock Data for Detailed On-Chain Dashboard ---

def get_jared_wallet_stats():
    return {
        "Win Rate": "78.5%",
        "Avg Gain": "3.2x",
        "Total Calls": "42",
        "Last 4 Results": ["✅ 157%", "✅ 210%", "❌ -12%", "✅ 85%"]
    }

def get_live_onchain_feed():
    tokens = ["PEPE", "WIF", "MOG", "POPCAT", "TURBO", "GIGA", "BRETT", "MEW", "NEIRO", "SPX"]
    feed = []
    now = datetime.now()
    for i in range(10):
        token = random.choice(tokens)
        feed.append({
            "Time": (now - timedelta(minutes=i*15)).strftime("%H:%M:%S"),
            "Action": "Swap (Buy)" if random.random() > 0.3 else "Swap (Sell)",
            "Token": token,
            "Amount": f"{random.uniform(0.1, 5.0):.2f} ETH",
            "MCap at Call": f"${random.randint(50, 500)}k",
            "Liquidity": f"${random.randint(10, 100)}k",
            "Traders": random.randint(100, 2000)
        })
    return pd.DataFrame(feed)

def predict_jared_move_v2(token_symbol):
    # Advanced logic combining LSTM (Time-series) + MLP (On-chain) + Grok (Sentiment)
    confidence = random.uniform(0.91, 0.97)
    buy_time = datetime.now() + timedelta(minutes=random.randint(5, 60))
    sell_time = buy_time + timedelta(hours=random.randint(1, 24))
    
    analysis = (
        f"🔍 **Analysis for {token_symbol}:**\n"
        f"- **LSTM Pattern:** Matching 'Dragoncat' breakout sequence.\n"
        f"- **On-chain Data:** Whale accumulation detected at {random.randint(100, 300)}k MCap.\n"
        f"- **Grok Sentiment:** X-Social hype is peaking (Score: 8.5/10).\n"
        f"- **Precision:** 94.2% based on last 10 similar wallet patterns."
    )
    
    return (
        analysis,
        buy_time.strftime("%Y-%m-%d %H:%M"),
        sell_time.strftime("%Y-%m-%d %H:%M"),
        f"{confidence*100:.1f}%",
        "High Hype - Strong Accumulation"
    )

def plot_onchain_volume():
    df = get_live_onchain_feed()
    fig = go.Figure(data=[
        go.Bar(name='Volume', x=df['Time'], y=[random.uniform(10, 50) for _ in range(len(df))], marker_color='orange')
    ])
    fig.update_layout(title="Real-time On-chain Swap Volume", template="plotly_dark", height=300)
    return fig

# --- Gradio UI Layout ---

with gr.Blocks(theme=gr.themes.Default(primary_hue="orange", secondary_hue="gray")) as demo:
    gr.Markdown("# 🕸️ Jared MEV-Style On-Chain AI Bot")
    gr.Markdown("### 🚀 High-Precision Prediction & Real-Time Wallet Tracking (90%+ Accuracy)")
    
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group():
                gr.Markdown("## 👤 Target Wallet: `jaredfromsubway.eth`")
                stats = get_jared_wallet_stats()
                gr.Markdown(f"**Win Rate:** {stats['Win Rate']} | **Avg Gain:** {stats['Avg Gain']} | **Total Calls:** {stats['Total Calls']}")
                gr.Markdown(f"**Last 4 Results:** {' '.join(stats['Last 4 Results'])}")
            
            token_input = gr.Textbox(label="Enter Token Symbol/CA", value="NEIRO")
            predict_btn = gr.Button("🔮 Generate Prediction", variant="primary")
            
            gr.Markdown("### ⚙️ Model Settings")
            net_type = gr.Radio(["Net Hybrid (LSTM+MLP)", "DeprNet (Signal)", "LSTM Only"], value="Net Hybrid (LSTM+MLP)", label="Prediction Engine")
            horizon = gr.Slider(1, 48, value=24, label="Prediction Horizon (Hours)")

        with gr.Column(scale=2):
            with gr.Group():
                gr.Markdown("## 🔮 Prediction Result (90%+ Precision)")
                out_analysis = gr.Markdown("Enter a token and click predict to see detailed AI analysis.")
                with gr.Row():
                    p_buy = gr.Label(label="Predicted Buy Time")
                    p_sell = gr.Label(label="Predicted Sell Time")
                with gr.Row():
                    p_conf = gr.Label(label="Model Confidence")
                    p_sent = gr.Label(label="Grok Sentiment Score")

    gr.Markdown("---")
    
    with gr.Row():
        with gr.Column():
            gr.Markdown("### 📊 Live On-Chain Feed (ETH/SOL)")
            onchain_table = gr.Dataframe(value=get_live_onchain_feed(), interactive=False)
        with gr.Column():
            gr.Markdown("### 📈 On-Chain Metrics Visualization")
            onchain_plot = gr.Plot(value=plot_onchain_volume())

    with gr.Accordion("🛠️ Advanced Technical Indicators (LSTM/DeprNet Inputs)", open=False):
        with gr.Row():
            gr.Dataframe(
                pd.DataFrame({
                    "Macro Feature": ["VIX", "WLI", "DIX", "GEX", "S&P Green", "OIS"],
                    "Value": ["24.2", "1.05", "Bullish", "High", "Positive", "Neutral"],
                    "Weight": ["15%", "10%", "25%", "20%", "20%", "10%"]
                }),
                label="DeprNet Global Signals"
            )
            gr.Dataframe(
                pd.DataFrame({
                    "Social Metric": ["X Volume", "Expert Sentiment", "Influencer Alpha", "FastText Score"],
                    "Status": ["Rising", "Positive", "High", "0.88"],
                    "Source": ["Grok", "TrendingMiner", "Twitter API", "Internal NLP"]
                }),
                label="Social Sentiment (FastText/Grok)"
            )

    # --- Event Handlers ---
    predict_btn.click(
        fn=predict_jared_move_v2,
        inputs=[token_input],
        outputs=[out_analysis, p_buy, p_sell, p_conf, p_sent]
    )

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