Update app.py

app.py

@@ -1,31 +1,25 @@
-import streamlit as st
 import ccxt
 import pandas as pd
 import numpy as np
 import xgboost as xgb
+import time
 import gc
-import requests
-import plotly.graph_objects as go
+import threading
+import gradio as gr
+from utils import get_live_usdt_dominance, send_email_alert
 
-# --- Global Macro Data ---
-def get_live_usdt_dominance():
-    try:
-        url = "https://api.coingecko.com/api/v3/global"
-        response = requests.get(url, timeout=5).json()
-        usdt_dom = response['data']['market_cap_percentage']['usdt']
-        btc_dom = response['data']['market_cap_percentage']['btc']
-        return usdt_dom, btc_dom
-    except Exception as e:
-        return 0.0, 0.0
+# --- 1. Your Credentials ---
+SENDER_EMAIL = "your_email@gmail.com"
+SENDER_PASSWORD = "paste_your_16_digit_app_password_here" 
+RECEIVER_EMAIL = "your_email@gmail.com" 
 
-# --- 1. Cloud Compute Backend ---
-@st.cache_resource
-def train_spot_model(symbol):
+# --- 2. The Core AI Engine ---
+def train_and_evaluate(symbol):
     exchange = ccxt.bitget()
+    
+    # Train Model (Historical Data)
     ohlcv = exchange.fetch_ohlcv(symbol, '1h', limit=2000)
     df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
-    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
-    
     df['buy_vol_proxy'] = np.where(df['close'] > df['open'], df['volume'] * 0.7, df['volume'] * 0.3)
     df['sell_vol_proxy'] = df['volume'] - df['buy_vol_proxy']
     df['order_book_imbalance'] = (df['buy_vol_proxy'] - df['sell_vol_proxy']) / (df['volume'] + 1e-9)
@@ -34,8 +28,7 @@ def train_spot_model(symbol):
     tp_pct, sl_pct, max_candles = 0.02, 0.01, 24
     for i in range(len(df)):
         if i + max_candles >= len(df):
-            targets.append(0)
-            continue
+            targets.append(0); continue
         entry = df['close'].iloc[i]
         tp, sl = entry * (1 + tp_pct), entry * (1 - sl_pct)
         success = 0
@@ -55,141 +48,88 @@ def train_spot_model(symbol):
     df = df.dropna()
     
     features = ['open', 'high', 'low', 'close', 'volume', 'order_book_imbalance', 'SMA_10', 'SMA_50', 'ATR', 'Regime', 'returns']
-    X = df[features]
-    y = df['target']
+    X, y = df[features], df['target']
     
-    scale_weight = (y == 0).sum() / ((y == 1).sum() + 1e-9)
-    model = xgb.XGBClassifier(n_estimators=300, max_depth=5, learning_rate=0.05, scale_pos_weight=scale_weight)
+    model = xgb.XGBClassifier(n_estimators=300, max_depth=5, learning_rate=0.05, scale_pos_weight=(y == 0).sum() / ((y == 1).sum() + 1e-9))
     model.fit(X, y)
     
-    del df, X, y
-    gc.collect()
-    
-    return model, features
-
-# --- 2. Live Market Execution ---
-def analyze_live_market(symbol, model, features):
-    exchange = ccxt.bitget()
-    
-    ohlcv = exchange.fetch_ohlcv(symbol, '1h', limit=100) 
-    df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
-    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    # Live Market Check
+    ohlcv_live = exchange.fetch_ohlcv(symbol, '1h', limit=60)
+    df_live = pd.DataFrame(ohlcv_live, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
     
     orderbook = exchange.fetch_order_book(symbol, limit=50)
     bids_vol = sum([bid[1] for bid in orderbook['bids']])
     asks_vol = sum([ask[1] for ask in orderbook['asks']])
     live_imbalance = (bids_vol - asks_vol) / (bids_vol + asks_vol + 1e-9)
     
-    df['order_book_imbalance'] = live_imbalance
-    df['SMA_10'] = df['close'].rolling(10).mean()
-    df['SMA_50'] = df['close'].rolling(50).mean()
-    df['ATR'] = df['high'].rolling(14).max() - df['low'].rolling(14).min()
-    df['Regime'] = df['close'].rolling(20).std() / (df['ATR'] + 1e-9)
-    df['returns'] = df['close'].pct_change()
+    df_live['order_book_imbalance'] = live_imbalance
+    df_live['SMA_10'] = df_live['close'].rolling(10).mean()
+    df_live['SMA_50'] = df_live['close'].rolling(50).mean()
+    df_live['ATR'] = df_live['high'].rolling(14).max() - df_live['low'].rolling(14).min()
+    df_live['Regime'] = df_live['close'].rolling(20).std() / (df_live['ATR'] + 1e-9)
+    df_live['returns'] = df_live['close'].pct_change()
     
-    latest = df.dropna().iloc[-1:]
-    X_live = latest[features]
+    latest = df_live.dropna().iloc[-1:]
+    conf = model.predict_proba(latest[features])[0][1]
     
-    confidence = model.predict_proba(X_live)[0][1]
+    price = latest['close'].iloc[0]
+    atr = latest['ATR'].iloc[0]
+    regime = latest['Regime'].iloc[0]
     
-    current_price = latest['close'].iloc[0]
-    atr_val = latest['ATR'].iloc[0]
-    regime_val = latest['Regime'].iloc[0]
+    limit_entry = price - (atr * 0.5)
+    stop = limit_entry * 0.99
+    tp = limit_entry * 1.02
     
-    market_state = "🔥 Trending (Favorable)" if regime_val > 1.2 else "⚠️ Choppy/Ranging (High Risk)"
-    limit_entry = current_price - (atr_val * 0.5)
-    invalidation_level = limit_entry * 0.99
-    take_profit = limit_entry * 1.02
+    del df, X, y, df_live, model
+    gc.collect()
     
-    return current_price, confidence, limit_entry, invalidation_level, take_profit, market_state, live_imbalance, df
+    return price, conf, limit_entry, stop, tp, regime, live_imbalance
 
-# --- 3. Streamlit UI ---
-st.set_page_config(page_title="AI Spot Sniper", layout="wide")
-st.title("🎯 AI Spot Sniper Terminal")
+# --- 3. The Infinite Background Loop ---
+def run_scanner():
+    TOP_PAIRS = ['BTC/USDT', 'ETH/USDT', 'SOL/USDT', 'BNB/USDT', 'XRP/USDT', 'DOGE/USDT', 'PEPE/USDT', 'BONK/USDT', 'PI/USDT', 'WIF/USDT']
+    
+    print("Initializing scanner thread...")
+    send_email_alert(SENDER_EMAIL, SENDER_PASSWORD, RECEIVER_EMAIL, "SYSTEM", "🟢 AI Spot Sniper Bot Online. Scanning markets from Hugging Face...")
+
+    while True:
+        try:
+            usdt_dom = get_live_usdt_dominance()
+            if usdt_dom >= 6.50:
+                print(f"Macro Kill Switch Active (USDT.D: {usdt_dom:.2f}%). Sleeping 15 mins.")
+                time.sleep(900) 
+                continue
+                
+            for symbol in TOP_PAIRS:
+                price, conf, entry, stop, tp, regime, imbalance = train_and_evaluate(symbol)
+                
+                if regime > 1.2 and conf >= 0.60 and 0.30 <= imbalance <= 0.65:
+                    alert_msg = (
+                        f"PRIME SETUP DETECTED: {symbol}\n\n"
+                        f"Current Price: ${price:.4f}\n"
+                        f"AI Confidence: {conf:.2%}\n"
+                        f"Order Book Imbalance: {imbalance:.2f}\n\n"
+                        f"LIMIT BUY: ${entry:.4f}\n"
+                        f"TAKE PROFIT: ${tp:.4f}\n"
+                        f"STOP LOSS: ${stop:.4f}"
+                    )
+                    send_email_alert(SENDER_EMAIL, SENDER_PASSWORD, RECEIVER_EMAIL, symbol, alert_msg)
+                    
+                time.sleep(2) 
+            time.sleep(900) 
+            
+        except Exception as e:
+            print(f"Error in main loop: {e}")
+            time.sleep(60)
 
-TOP_50_PAIRS = [
-    'BTC/USDT', 'ETH/USDT', 'SOL/USDT', 'BNB/USDT', 'XRP/USDT', 'ADA/USDT', 
-    'AVAX/USDT', 'DOGE/USDT', 'DOT/USDT', 'TRX/USDT', 'LINK/USDT', 'MATIC/USDT', 
-    'SHIB/USDT', 'LTC/USDT', 'BCH/USDT', 'UNI/USDT', 'NEAR/USDT', 'ICP/USDT', 
-    'APT/USDT', 'FIL/USDT', 'XLM/USDT', 'STX/USDT', 'HBAR/USDT', 'ATOM/USDT', 
-    'GRT/USDT', 'OP/USDT', 'LDO/USDT', 'INJ/USDT', 'TIA/USDT', 'SUI/USDT', 
-    'AR/USDT', 'RENDER/USDT', 'FET/USDT', 'SEI/USDT', 'RUNE/USDT', 'FTM/USDT', 
-    'PEPE/USDT', 'BONK/USDT', 'ORDI/USDT', 'PI/USDT', 'WIF/USDT', 'JUP/USDT', 
-    'PYTH/USDT', 'ONDO/USDT', 'ARB/USDT'
-]
+# --- 4. The Gradio Web Interface ---
+thread = threading.Thread(target=run_scanner)
+thread.daemon = True
+thread.start()
 
-symbol = st.selectbox("Select Asset", TOP_50_PAIRS)
+with gr.Blocks() as demo:
+    gr.Markdown("# 🟢 Email Spot Sniper is Live!")
+    gr.Markdown("Your AI is currently scanning the markets in the background. Emails will be sent automatically when a high-probability setup is found.")
+    gr.Markdown("**Note:** Ensure you are using a ping service (like UptimeRobot) to keep this Space from going to sleep.")
 
-if st.button(f"Initialize AI & Analyze {symbol}"):
-    with st.spinner("Fetching Macro Cash Flow Data..."):
-        usdt_dom, btc_dom = get_live_usdt_dominance()
-        
-    with st.spinner(f"Training XGBoost on heavy {symbol} data..."):
-        model, features = train_spot_model(symbol)
-        
-    with st.spinner("Fetching live order book & charting..."):
-        price, conf, entry, stop, tp, state, imbalance, chart_df = analyze_live_market(symbol, model, features)
-        
-        # --- UI DISPLAY ---
-        col1, col2, col3, col4 = st.columns(4)
-        col1.metric("Current Price", f"${price:,.4f}")
-        col2.metric("AI Probability", f"{conf:.2%}")
-        col3.metric("Order Book", f"{imbalance:.2f}")
-        col4.metric("USDT Dominance", f"{usdt_dom:.2f}%", help="High = People holding cash (Bearish). Low = People buying crypto (Bullish).")
-        
-        st.divider()
-        
-        # --- LIVE INTERACTIVE CHART ---
-        st.markdown("### 📈 Live Price Action & Targets")
-        fig = go.Figure(data=[go.Candlestick(
-            x=chart_df['timestamp'],
-            open=chart_df['open'], high=chart_df['high'],
-            low=chart_df['low'], close=chart_df['close'],
-            increasing_line_color='#2ebd85', decreasing_line_color='#f6465d',
-            name='Price'
-        )])
-        
-        # Draw AI Tactical Levels on the Chart
-        fig.add_hline(y=entry, line_dash="dash", line_color="#f0b90b", annotation_text="Limit Entry", annotation_position="top left")
-        fig.add_hline(y=tp, line_dash="solid", line_color="#2ebd85", annotation_text="Take Profit", annotation_position="top left")
-        fig.add_hline(y=stop, line_dash="solid", line_color="#f6465d", annotation_text="Stop Loss", annotation_position="bottom left")
-        
-        fig.update_layout(
-            template='plotly_dark',
-            height=500,
-            margin=dict(l=0, r=0, t=30, b=0),
-            xaxis_rangeslider_visible=False,
-            paper_bgcolor='#0e1117',
-            plot_bgcolor='#0e1117'
-        )
-        st.plotly_chart(fig, use_container_width=True)
-        
-        st.divider()
-        st.markdown(f"### 🛡️ Market Condition: {state}")
-        st.error(f"**DO NOT MARKET BUY.** Place a Limit Order at or below: **${entry:,.4f}**")
-        st.success(f"**Take Profit Target:** **${tp:,.4f}**")
-        st.warning(f"**Invalidation Level (Hard Stop Loss):** **${stop:,.4f}**")
-        
-        st.divider()
-        st.markdown("### 🤖 AI Final Verdict")
-        
-        is_trending = "Trending" in state
-        is_confident = conf >= 0.60
-        is_good_imbalance = 0.30 <= imbalance <= 0.65
-        is_manipulated = imbalance > 0.65
-        is_safe_macro = usdt_dom < 6.50 
-        
-        if is_trending and is_confident and is_good_imbalance and is_safe_macro:
-            st.success("✅ **PRIME SETUP DETECTED!** All metrics and macro cash flows align perfectly.")
-        else:
-            st.warning("⚠️ **SETUP REJECTED. Do not trade.** See failure reasons below:")
-            
-            if not is_trending:
-                st.info("❌ Market is ranging/choppy.")
-            if not is_confident:
-                st.info(f"❌ AI Confidence is only {conf:.1%} (Requires 60.0%+).")
-            if imbalance < 0.30:
-                st.info(f"❌ Buy pressure is too weak. Imbalance is {imbalance:.2f}.")
-            if not is_safe_macro:
-                st.error(f"🚨 **MACRO KILL SWITCH ACTIVATED:** USDT Dominance is extremely high ({usdt_dom:.2f}%). Traders are fleeing to cash.")
-                
+demo.launch(server_name="0.0.0.0", server_port=7860)