Create app.py

app.py

@@ -0,0 +1,165 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+
+st.set_page_config(page_title="Aviator Pattern Tracker Pro", layout="centered")
+
+# ---------------------------
+# Session State
+# ---------------------------
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+# ---------------------------
+# Header
+# ---------------------------
+st.title("🎯 Aviator Pattern Tracker Pro")
+st.caption("Pattern + Expected Multiplier + Risk Control (Bangla UI)")
+
+# ---------------------------
+# Input Section
+# ---------------------------
+st.subheader("📥 Multiplier Input")
+
+new_val = st.number_input("নতুন multiplier দিন (example: 1.45)", min_value=1.0, step=0.01)
+
+col1, col2 = st.columns(2)
+
+with col1:
+    if st.button("➕ Add"):
+        st.session_state.history.append(round(new_val, 2))
+
+with col2:
+    if st.button("🔁 Reset"):
+        st.session_state.history = []
+
+# ---------------------------
+# Data
+# ---------------------------
+data = st.session_state.history[-50:]
+
+st.subheader("📊 Last 50 Rounds")
+st.write(data)
+
+if len(data) < 5:
+    st.warning("কমপক্ষে 5টা multiplier দিন")
+    st.stop()
+
+# ---------------------------
+# Pattern Calculation
+# ---------------------------
+low = [x for x in data if x < 1.5]
+mid = [x for x in data if 1.5 <= x <= 3]
+high = [x for x in data if x > 3]
+
+low_ratio = len(low) / len(data)
+high_ratio = len(high) / len(data)
+
+mean = np.mean(data)
+std = np.std(data)
+
+# Chaos Index
+chaos = (std * high_ratio) / (low_ratio + 0.1)
+
+# Low streak
+low_streak = 0
+for x in reversed(data):
+    if x < 1.5:
+        low_streak += 1
+    else:
+        break
+
+# Confidence
+stability_score = max(0, 1 - std/5)
+distribution_score = 1 - abs(low_ratio - 0.5)
+confidence = ((low_streak/5) + stability_score + distribution_score) / 3
+confidence = max(0, min(confidence, 1))
+
+# ---------------------------
+# Expected Multiplier
+# ---------------------------
+arr = np.array(data)
+
+weights = np.linspace(1, 2, len(arr))
+weighted_mean = np.average(arr, weights=weights)
+
+median = np.median(arr)
+
+sorted_arr = np.sort(arr)
+trim = int(len(arr) * 0.1)
+trimmed = sorted_arr[trim: len(arr)-trim] if len(arr) > 10 else arr
+trimmed_mean = np.mean(trimmed)
+
+expected = (weighted_mean * 0.5) + (median * 0.3) + (trimmed_mean * 0.2)
+
+lower = expected - (std * 0.5)
+upper = expected + (std * 0.5)
+lower = max(1.01, lower)
+
+# ---------------------------
+# Decision Logic
+# ---------------------------
+if chaos > 1.2:
+    signal = "🔴 SKIP"
+    risk = "HIGH RISK"
+elif low_streak >= 3 and std < 1.5:
+    signal = "🟢 ENTER (SMALL)"
+    risk = "LOW RISK"
+else:
+    signal = "🟡 WAIT"
+    risk = "MEDIUM"
+
+# refine by expected
+if signal.startswith("🟢") and expected < 1.4:
+    signal = "🟡 WAIT (Expected Low)"
+
+if signal.startswith("🟢") and expected > 2.0:
+    signal = "🟢 ENTER (GOOD ZONE)"
+
+# ---------------------------
+# OUTPUT UI
+# ---------------------------
+st.subheader("📈 Analysis")
+
+c1, c2, c3 = st.columns(3)
+c1.metric("Mean", round(mean,2))
+c2.metric("Std Dev", round(std,2))
+c3.metric("Chaos", round(chaos,2))
+
+st.divider()
+
+st.subheader("🎯 Expected Multiplier")
+e1, e2 = st.columns(2)
+e1.metric("Expected", round(expected,2))
+e2.metric("Range", f"{round(lower,2)} - {round(upper,2)}")
+
+st.divider()
+
+st.subheader("🧠 Decision")
+st.markdown(f"### {signal}")
+st.markdown(f"**Confidence:** {round(confidence,2)}")
+st.markdown(f"**Risk Level:** {risk}")
+
+# ---------------------------
+# Strategy Guide
+# ---------------------------
+st.divider()
+st.subheader("📘 Strategy Guide")
+
+st.markdown("""
+**Entry Rule:**
+- ছোট bet only
+- auto cashout: 1.5x – 1.8x
+
+**Stop Rule:**
+- 2 loss in row → STOP
+- 1 big win (>3x) → next 2 round skip
+
+**Expected Guide:**
+- <1.4 → weak
+- 1.4–1.8 → safe
+- 1.8–2.5 → medium
+- >2.5 → volatile
+
+⚠️ এটি কোনো নিশ্চিত জেতার tool না
+""")