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Build a fully functional Aviator Predictor App powered by Spribe signals, designed for predicting the exact next Aviator crash point in real time across all popular betting websites in Africa. The app must be optimized for 100% prediction accuracy, utilizing advanced pattern recognition, machine learning, and real-time signal syncing. The app must be capable of running seamlessly on both Android and iOS, with support for Firebase backend integration, authentication, subscriptions, and real-time Firestore updates. 🔐 Core Features: User Authentication: Firebase Auth for secure login/register (email & password). Dashboard: Clean and minimal UI showing: Last predicted crash point. Current signal status (live). Real-time prediction graph (using FL Chart or similar). Upcoming prediction timer/countdown. Prediction Engine: Uses real-time signals from Spribe’s engine. Combines this with historical crash data to identify patterns. Applies AI/ML logic to detect cycles or recurring sequences. Includes fallback logic for edge cases (e.g., sudden spikes). Hosted on Python backend (Cloud Function or any backend service). Data Sync: Firebase Cloud Functions to listen to Spribe data source and update Firestore instantly. Firestore stores history of crash points, prediction confidence, timestamp. Subscription System: Users can unlock prediction access via payment/subscription. Integration with Google Play Billing / Stripe / Flutterwave. Unpaid users see demo data or limited access. Admin Panel (optional): Allow the developer (you) to post manual override predictions. See user metrics, active sessions, crash logs. ⚙️ Tech Stack: Flutter (Cross-platform frontend). Firebase: Firestore for real-time database. Firebase Auth for authentication. Firebase Functions for signal processing. Python (Backend): For ML-based prediction logic. Periodically pulls latest Spribe crash points from external sources or mirrors. Sends result to Firestore. --- 📊 ML & Pattern Recognition Engine: The engine should: Continuously analyze past 50–100 rounds of Spribe crash data. Identify cycles such as 1.00–1.20 sequences, big jumps (>10x), and patterns between rounds. Use models like: Moving average. Naive Bayes or Decision Tree for small cycles. Optional: Lightweight LSTM if latency allows. Return: Next predicted crash point. Confidence score (0–100). Suggested bet action (e.g., "Bet below 2.0x in next 3 rounds"). Example output: { "nextPrediction": 1.87, "confidence": 93, "suggestion": "Safe to bet below 2.0x in next round" } --- 🔄 Real-Time Data Flow: 1. User opens app, sees the dashboard. 2. Firebase Function receives signal from Spribe feed (manually or auto-pulled every X seconds). 3. Function processes signal and sends to Firestore (predictions collection). 4. Flutter app listens to Firestore in real time and updates dashboard UI with: Crash point. Prediction result. Visual chart (recent 10–20 rounds). 5. Optional: Notify user via in-app message when high-confidence prediction is available. --- 🔐 Security & Access Control: Paid user logic: If user.subscriptionStatus = active, allow full access. Else show blurred prediction or countdown-only access. Firestore rules: Allow read/write access only to authenticated users. Limit users to read only their own data. Admin users can: Trigger overrides. Post global notices or predictions. View logs. --- 🎨 UI/UX Requirements: Light and fast, using minimal color palette (deep blue, white, red highlights). Dashboard has: Real-time chart at top. Prediction card with value + confidence. Countdown timer to next signal. Button to upgrade to full version. Login and register pages are clean, with error handling. Admin panel (if included) should be simple, just for basic controls. --- 🌍 Target Audience: Bettors in Africa using Spribe-powered games (e.g., Aviator on 1xBet, Bet9ja, BetPawa, MSport). Needs to be fast, accurate, and easy to use even on low-end phones. --- 🔧 Developer Requirements: Full access to source code and dashboard. Ability to update the logic (manually or via backend). Able to toggle "prediction mode" from manual to auto. --- ✅ Output: A fully functional, cross-platform mobile app, ready to: Predict Aviator crash points in real-time. Sync with Firestore backend. Restrict content to paying users. Run prediction logic with highest possible accuracy using AI + signal processing. Be deployed to Play Store or other channels. Include passcode for access: joeljossaih Take note if the following 1. Real-Time Signal Handling: Use Firebase Cloud Functions to instantly process and react to incoming Spribe signals.live Connect to real-time Firestore triggers to handle live predictions and push them to your app UI instantly. 2. Historical Learning System: Store all game results in Firestore with timestamps and outcome details. Create a Python-based ML model (hosted on Google Cloud or Cloud Run) that: Learns over time from outcome sequences Detects recurring game patterns Recommends the next probable result with confidence scores Use scheduled Cloud Functions (e.g., every minute or after every round) to invoke the model and update predictions. 3. Confidence-Based Predictions: Combine: Real-time logic (e.g., burst pattern detection, timing) Historical analysis (ML-based trend forecasting) Show the user a prediction with a confidence rating, e.g., 94% sure next round will peak at 2.0x+. 4. Fast Feedback Loop After every round, compare actual result to prediction and store outcome. Let this feedback improve future predictions (retrain model weekly or dynamically). 5. Security + Performance: Use Firebase Security Rules to restrict access based on subscription/login status. Optimize queries and function triggers to avoid Firestore cost spikes. This approach will give you: Scalability High accuracy (with tuning) Future-proof architecture (can plug in more AI later) Seamless updates without app reinstallations it should generate diagram or even generate the backend logic to start with? And finally add payment details via momo and Airtel only - Initial Deployment
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