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.env.example

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+# .env.example — Copy to .env and fill in your values.
+# Never commit .env to Git.
+
+# Database
+POSTGRES_PASSWORD=your_secure_password_here
+DATABASE_URL=postgresql://admin:your_secure_password_here@localhost:5432/flights
+
+# AWS
+AWS_ACCESS_KEY_ID=AKIA...
+AWS_SECRET_ACCESS_KEY=...
+AWS_DEFAULT_REGION=us-east-1
+S3_RAW_BUCKET=flight-delay-raw-data
+S3_PROCESSED_BUCKET=flight-delay-processed
+
+# MLflow
+MLFLOW_TRACKING_URI=http://localhost:5000
+
+# BTS API (optional — bulk CSV download doesn't need it)
+BTS_API_KEY=
+
+# Airflow
+AIRFLOW_FERNET_KEY=
+AIRFLOW_SECRET_KEY=changeme_in_production
+
+# Grafana
+GRAFANA_PASSWORD=admin
+
+# Monitoring
+DRIFT_SHARE_THRESHOLD=0.30
+SQS_RETRAIN_QUEUE_URL=https://sqs.us-east-1.amazonaws.com/123456789/retrain-queue
+
+# Deployment
+API_ENDPOINT=https://your-alb-dns.us-east-1.elb.amazonaws.com
+MODEL_VERSION=v1.0.0

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*.egg-info/
+.eggs/
+dist/
+build/
+.venv/
+venv/
+env/
+
+# Data (tracked by DVC, not Git)
+data/raw/
+data/processed/
+!data/.gitkeep
+
+# Models (tracked by DVC or MLflow)
+models/*.pkl
+models/*.joblib
+!models/.gitkeep
+
+# MLflow
+mlruns/
+mlartifacts/
+
+# Reports
+reports/*.png
+reports/*.html
+reports/*.json
+!reports/.gitkeep
+
+# Metrics (DVC manages)
+metrics/
+
+# Env
+.env
+.env.*
+!.env.example
+
+# Editors
+.vscode/
+.idea/
+*.swp
+
+# Testing
+.pytest_cache/
+.coverage
+coverage.xml
+htmlcov/
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Jupyter
+.ipynb_checkpoints/
+*.ipynb
+
+# Terraform
+terraform/.terraform/
+terraform/*.tfstate
+terraform/*.tfstate.backup
+terraform/.terraform.lock.hcl

Dockerfile

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+# Multi-stage build — keeps final image small and dependency-free from build tools
+# ── Stage 1: Build ────────────────────────────────────────────────────────────
+FROM python:3.11-slim AS builder
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir --user -r requirements.txt
+
+# ── Stage 2: Runtime ──────────────────────────────────────────────────────────
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Non-root user for security
+RUN useradd -m -u 1001 appuser
+
+# Copy installed packages from builder
+COPY --from=builder /root/.local /home/appuser/.local
+
+# Copy application source
+COPY --chown=appuser:appuser . .
+
+# Create writable dirs the app needs
+RUN mkdir -p models data/raw data/processed reports metrics \
+    && chown -R appuser:appuser models data reports metrics
+
+USER appuser
+
+ENV PATH=/home/appuser/.local/bin:$PATH \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+EXPOSE 8000
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=15s --retries=3 \
+  CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"
+
+CMD ["uvicorn", "api.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "2"]

README.md

@@ -1,13 +1,169 @@
 ---
-title: Flight Delay Prediction
-emoji: 🏆
-colorFrom: red
+title: Flight Delay Prediction Platform
+emoji: ✈️
+colorFrom: blue
 colorTo: indigo
 sdk: gradio
-sdk_version: 6.9.0
+sdk_version: 4.31.0
 app_file: app.py
-pinned: false
-short_description: flight delay prediction
+pinned: true
+license: mit
+tags:
+  - machine-learning
+  - tabular-classification
+  - xgboost
+  - flight-delay
+  - mlops
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ✈️ Flight Delay Prediction Platform
+
+> End-to-end ML system: ETL → Training → MLOps → FastAPI → Cloud Deploy → Monitoring
+
+[![CI/CD](https://github.com/YOUR_USERNAME/flight-delay-platform/actions/workflows/deploy.yml/badge.svg)](https://github.com/YOUR_USERNAME/flight-delay-platform/actions)
+[![Python 3.11](https://img.shields.io/badge/python-3.11-blue.svg)](https://python.org)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
+
+---
+
+## 🗺 Architecture
+
+```
+BTS API / OpenSky
+      │
+      ▼
+ ┌─────────┐    ┌──────────┐    ┌──────────┐
+ │  ETL    │───▶│  Postgres│───▶│  Feature │
+ │ Airflow │    │   + S3   │    │  Store   │
+ └─────────┘    └──────────┘    └──────────┘
+                                      │
+                                      ▼
+                              ┌──────────────┐
+                              │  XGBoost /   │
+                              │  LightGBM    │
+                              │  Training    │
+                              └──────┬───────┘
+                                     │ MLflow
+                                     ▼
+                              ┌──────────────┐
+                              │  FastAPI     │
+                              │  Inference   │──▶ AWS ECS / GCP Cloud Run
+                              └──────┬───────┘
+                                     │
+                                     ▼
+                              ┌──────────────┐
+                              │  Evidently   │
+                              │  + Grafana   │
+                              └──────────────┘
+```
+
+## 🚀 Quick Start
+
+```bash
+# 1. Clone
+git clone https://huggingface.co/spaces/YOUR_USERNAME/flight-delay-platform
+cd flight-delay-platform
+
+# 2. Install
+pip install -r requirements.txt
+
+# 3. Run local stack
+docker-compose up -d
+
+# 4. Trigger ETL
+python -m etl.extract --start-date 2024-01-01 --end-date 2024-06-30
+
+# 5. Train models
+python -m ml.train
+
+# 6. Start API
+uvicorn api.main:app --reload --port 8000
+
+# 7. Try a prediction
+curl -X POST http://localhost:8000/predict \
+  -H "Content-Type: application/json" \
+  -d '{"dep_hour":8,"dep_dayofweek":1,"dep_month":3,
+       "carrier_enc":3,"origin_enc":10,"dest_enc":25,
+       "crs_elapsed_time":185,"distance":1400,
+       "origin_delay_rate":0.22,"is_weekend":0,"is_peak_hour":1}'
+```
+
+## 📂 Project Structure
+
+```
+flight-delay-platform/
+├── app.py                    # Gradio demo (HF Spaces entry point)
+├── etl/                      # Data pipeline
+│   ├── extract.py
+│   ├── transform.py
+│   ├── validate.py
+│   └── load.py
+├── ml/                       # Model training
+│   ├── train.py
+│   ├── evaluate.py
+│   ├── features.py
+│   └── config.py
+├── mlops/                    # Model registry & promotion
+│   ├── model_registry.py
+│   └── promote.py
+├── api/                      # FastAPI serving
+│   ├── main.py
+│   ├── schemas.py
+│   └── middleware.py
+├── monitoring/               # Drift detection & metrics
+│   ├── drift_detector.py
+│   └── prometheus.yml
+├── dags/                     # Airflow DAGs
+│   ├── etl_dag.py
+│   └── retrain_dag.py
+├── tests/                    # Pytest suite
+│   ├── test_etl.py
+│   ├── test_model.py
+│   └── test_api.py
+├── terraform/                # IaC (AWS ECS)
+│   ├── main.tf
+│   └── variables.tf
+├── .github/workflows/        # CI/CD
+│   └── deploy.yml
+├── docker-compose.yml
+├── Dockerfile
+├── dvc.yaml
+├── params.yaml
+└── requirements.txt
+```
+
+## 🧠 Models
+
+| Model | CV AUC | F1 | Notes |
+|---|---|---|---|
+| Logistic Regression | ~0.72 | ~0.65 | Baseline |
+| LightGBM | ~0.83 | ~0.74 | Fast, good default |
+| **XGBoost (tuned)** | **~0.86** | **~0.77** | **Production model** |
+
+Target: predict whether a flight will be **≥15 minutes late** (FAA standard).
+
+## 🔑 Key Features Used
+
+- `dep_hour`, `dep_dayofweek`, `dep_month`, `is_weekend`, `is_peak_hour`
+- `carrier_enc`, `origin_enc`, `dest_enc`
+- `crs_elapsed_time`, `distance`
+- `origin_delay_rate` (rolling 30-day historical delay rate per airport)
+- `weather_wind_speed`, `weather_precip_mm`
+
+## 🛠 Tech Stack
+
+| Layer | Tools |
+|---|---|
+| Orchestration | Apache Airflow 2.8 |
+| ML | XGBoost, LightGBM, Scikit-learn |
+| Experiment Tracking | MLflow + DVC |
+| Serving | FastAPI + Uvicorn |
+| Containerization | Docker + Docker Compose |
+| Cloud | AWS ECS Fargate + S3 + RDS |
+| IaC | Terraform |
+| Monitoring | Evidently AI + Prometheus + Grafana |
+| CI/CD | GitHub Actions |
+
+## 📄 License
+
+MIT © 2024

app.py

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+"""
+app.py — Gradio demo for HF Spaces.
+Loads the trained model (or a stub) and exposes a live prediction UI.
+"""
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+import pickle
+import os
+from pathlib import Path
+
+# ── Load model (falls back to a stub if not yet trained) ──────────────────────
+MODEL_PATH = Path("models/best_model.pkl")
+
+def load_model():
+    if MODEL_PATH.exists():
+        with open(MODEL_PATH, "rb") as f:
+            return pickle.load(f)
+    return None  # stub mode
+
+model = load_model()
+
+CARRIER_MAP  = {"AA": 0, "DL": 1, "UA": 2, "WN": 3, "B6": 4, "AS": 5, "F9": 6, "NK": 7}
+AIRPORT_STUB = {  # small lookup for demo
+    "ATL": 0, "LAX": 1, "ORD": 2, "DFW": 3, "DEN": 4,
+    "JFK": 5, "SFO": 6, "SEA": 7, "LAS": 8, "MIA": 9,
+}
+
+def predict_delay(
+    dep_hour, dep_dayofweek, dep_month,
+    carrier, origin, dest,
+    crs_elapsed_time, distance,
+    origin_delay_rate,
+    is_weekend, is_peak_hour,
+):
+    carrier_enc = CARRIER_MAP.get(carrier, 0)
+    origin_enc  = AIRPORT_STUB.get(origin, 0)
+    dest_enc    = AIRPORT_STUB.get(dest, 1)
+
+    features = pd.DataFrame([{
+        "dep_hour":           int(dep_hour),
+        "dep_dayofweek":      int(dep_dayofweek),
+        "dep_month":          int(dep_month),
+        "carrier_enc":        carrier_enc,
+        "origin_enc":         origin_enc,
+        "dest_enc":           dest_enc,
+        "crs_elapsed_time":   float(crs_elapsed_time),
+        "distance":           float(distance),
+        "origin_delay_rate":  float(origin_delay_rate),
+        "is_weekend":         int(is_weekend == "Yes"),
+        "is_peak_hour":       int(is_peak_hour == "Yes"),
+    }])
+
+    if model is not None:
+        prob = float(model.predict_proba(features)[0, 1])
+    else:
+        # Demo stub: simple heuristic
+        prob = min(1.0, (
+            origin_delay_rate * 0.5 +
+            (0.15 if is_peak_hour == "Yes" else 0) +
+            (0.1  if is_weekend  == "Yes" else 0) +
+            (dep_month in [6, 7, 12]) * 0.1
+        ) + np.random.normal(0, 0.05))
+        prob = max(0.0, prob)
+
+    label      = "🔴 LIKELY DELAYED" if prob >= 0.5 else "🟢 LIKELY ON TIME"
+    confidence = "HIGH" if abs(prob - 0.5) > 0.25 else "MEDIUM" if abs(prob - 0.5) > 0.1 else "LOW"
+    bar        = "█" * int(prob * 20) + "░" * (20 - int(prob * 20))
+
+    return (
+        f"{label}\n\n"
+        f"Delay probability : {prob:.1%}\n"
+        f"Confidence        : {confidence}\n"
+        f"[{bar}] {prob:.1%}"
+    )
+
+
+# ── UI ────────────────────────────────────────────────────────────────────────
+with gr.Blocks(
+    title="✈️ Flight Delay Predictor",
+    theme=gr.themes.Base(primary_hue="blue", neutral_hue="slate"),
+) as demo:
+
+    gr.Markdown("""
+    # ✈️ Flight Delay Prediction
+    Predict whether a flight will be **≥ 15 minutes late** using the trained XGBoost model.
+    > Part of the [Flight Delay ML Platform](https://github.com/YOUR_USERNAME/flight-delay-platform)
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### ✈️ Flight Details")
+            carrier  = gr.Dropdown(list(CARRIER_MAP.keys()), value="AA",  label="Airline")
+            origin   = gr.Dropdown(list(AIRPORT_STUB.keys()), value="ATL", label="Origin Airport")
+            dest     = gr.Dropdown(list(AIRPORT_STUB.keys()), value="LAX", label="Destination Airport")
+            distance = gr.Slider(100, 5000, value=1400, step=50,  label="Distance (miles)")
+            crs_elapsed_time = gr.Slider(30, 600, value=185, step=5, label="Scheduled Duration (min)")
+
+        with gr.Column():
+            gr.Markdown("### 🕐 Schedule")
+            dep_hour       = gr.Slider(0, 23, value=8, step=1,  label="Departure Hour (0–23)")
+            dep_dayofweek  = gr.Slider(0,  6, value=1, step=1,  label="Day of Week (0=Mon, 6=Sun)")
+            dep_month      = gr.Slider(1, 12, value=3, step=1,  label="Month")
+            is_weekend     = gr.Radio(["Yes", "No"], value="No",  label="Weekend Flight?")
+            is_peak_hour   = gr.Radio(["Yes", "No"], value="Yes", label="Peak Hour? (7–9am / 5–8pm)")
+
+        with gr.Column():
+            gr.Markdown("### 🌦 Airport History")
+            origin_delay_rate = gr.Slider(
+                0.0, 1.0, value=0.22, step=0.01,
+                label="Origin Airport 30-Day Delay Rate"
+            )
+            gr.Markdown("### 📊 Prediction")
+            output = gr.Textbox(label="Result", lines=5, interactive=False)
+            predict_btn = gr.Button("Predict Delay →", variant="primary")
+
+    predict_btn.click(
+        fn=predict_delay,
+        inputs=[
+            dep_hour, dep_dayofweek, dep_month,
+            carrier, origin, dest,
+            crs_elapsed_time, distance,
+            origin_delay_rate, is_weekend, is_peak_hour,
+        ],
+        outputs=output,
+    )
+
+    gr.Examples(
+        examples=[
+            [8,  1, 3, "AA", "ATL", "LAX", 185, 1400, 0.22, "No",  "Yes"],
+            [18, 4, 7, "UA", "ORD", "JFK", 140,  780, 0.38, "No",  "Yes"],
+            [6,  6, 1, "WN", "DEN", "SFO",  95,  950, 0.12, "Yes", "No" ],
+            [14, 3, 12,"DL", "ATL", "MIA",  75,  660, 0.45, "No",  "No" ],
+        ],
+        inputs=[
+            dep_hour, dep_dayofweek, dep_month,
+            carrier, origin, dest,
+            crs_elapsed_time, distance,
+            origin_delay_rate, is_weekend, is_peak_hour,
+        ],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

docker-compose.yml

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+version: "3.9"
+
+# Full local development stack
+# Start everything:  docker-compose up -d
+# View logs:         docker-compose logs -f inference-api
+# Tear down:         docker-compose down -v
+
+services:
+
+  # ── Postgres ────────────────────────────────────────────────────────────────
+  postgres:
+    image: postgres:15-alpine
+    environment:
+      POSTGRES_DB:       flights
+      POSTGRES_USER:     admin
+      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-localpassword}
+    ports: ["5432:5432"]
+    volumes: [postgres_data:/var/lib/postgresql/data]
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U admin -d flights"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+  # ── MLflow tracking server ───────────────────────────────────────────────────
+  mlflow:
+    image: python:3.11-slim
+    depends_on:
+      postgres: { condition: service_healthy }
+    command: >
+      sh -c "pip install -q mlflow psycopg2-binary boto3 &&
+             mlflow server
+               --host 0.0.0.0
+               --port 5000
+               --backend-store-uri postgresql://admin:${POSTGRES_PASSWORD:-localpassword}@postgres:5432/flights
+               --default-artifact-root /mlruns"
+    ports: ["5000:5000"]
+    volumes: [mlruns:/mlruns]
+    environment:
+      MLFLOW_TRACKING_URI: http://localhost:5000
+
+  # ── Airflow (single-container quickstart) ────────────────────────────────────
+  airflow:
+    image: apache/airflow:2.9.1-python3.11
+    depends_on:
+      postgres: { condition: service_healthy }
+    environment:
+      AIRFLOW__CORE__EXECUTOR:                LocalExecutor
+      AIRFLOW__DATABASE__SQL_ALCHEMY_CONN:    postgresql+psycopg2://admin:${POSTGRES_PASSWORD:-localpassword}@postgres:5432/flights
+      AIRFLOW__CORE__FERNET_KEY:              ${AIRFLOW_FERNET_KEY:-}
+      AIRFLOW__WEBSERVER__SECRET_KEY:         ${AIRFLOW_SECRET_KEY:-changeme}
+      AIRFLOW__CORE__LOAD_EXAMPLES:           "false"
+      MLFLOW_TRACKING_URI:                    http://mlflow:5000
+    ports: ["8080:8080"]
+    volumes:
+      - ./dags:/opt/airflow/dags
+      - ./etl:/opt/airflow/etl
+      - ./ml:/opt/airflow/ml
+      - ./mlops:/opt/airflow/mlops
+      - ./data:/opt/airflow/data
+    command: >
+      bash -c "airflow db init &&
+               airflow users create --username admin --password admin
+                 --firstname Admin --lastname User --role Admin --email admin@example.com &&
+               airflow webserver & airflow scheduler"
+
+  # ── Inference API ────────────────────────────────────────────────────────────
+  inference-api:
+    build: .
+    depends_on:
+      postgres: { condition: service_healthy }
+    ports: ["8000:8000"]
+    environment:
+      DATABASE_URL:        postgresql://admin:${POSTGRES_PASSWORD:-localpassword}@postgres:5432/flights
+      MLFLOW_TRACKING_URI: http://mlflow:5000
+      MODEL_VERSION:       local
+    volumes:
+      - ./models:/app/models      # mount trained models
+      - ./data:/app/data
+    healthcheck:
+      test: ["CMD-SHELL", "curl -f http://localhost:8000/health || exit 1"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+
+  # ── Prometheus ───────────────────────────────────────────────────────────────
+  prometheus:
+    image: prom/prometheus:v2.51.0
+    ports: ["9090:9090"]
+    volumes:
+      - ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml:ro
+      - prometheus_data:/prometheus
+    command:
+      - '--config.file=/etc/prometheus/prometheus.yml'
+      - '--storage.tsdb.retention.time=15d'
+
+  # ── Grafana ──────────────────────────────────────────────────────────────────
+  grafana:
+    image: grafana/grafana:10.4.0
+    depends_on: [prometheus]
+    ports: ["3000:3000"]
+    environment:
+      GF_SECURITY_ADMIN_PASSWORD: ${GRAFANA_PASSWORD:-admin}
+    volumes:
+      - grafana_data:/var/lib/grafana
+
+volumes:
+  postgres_data:
+  mlruns:
+  prometheus_data:
+  grafana_data:

dvc.yaml

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+stages:
+  extract:
+    cmd: python -m etl.extract --start-date ${extract.start_date} --end-date ${extract.end_date}
+    params:
+      - params.yaml:
+          - extract.start_date
+          - extract.end_date
+    outs:
+      - data/raw/flights_raw.parquet
+
+  transform:
+    cmd: python -m etl.transform
+    deps:
+      - data/raw/flights_raw.parquet
+      - etl/transform.py
+    outs:
+      - data/processed/features.parquet
+      - models/label_encoders.pkl
+
+  train:
+    cmd: python -m ml.train --data data/processed/features.parquet
+    deps:
+      - data/processed/features.parquet
+      - ml/train.py
+      - ml/config.py
+    params:
+      - params.yaml:
+          - model.n_estimators
+          - model.max_depth
+          - model.learning_rate
+          - model.subsample
+          - model.colsample_bytree
+    metrics:
+      - metrics/scores.json:
+          cache: false
+    outs:
+      - models/best_model.pkl
+
+  evaluate:
+    cmd: python -m ml.evaluate
+    deps:
+      - models/best_model.pkl
+      - data/processed/features.parquet
+      - ml/evaluate.py
+    outs:
+      - reports/roc_curve.png:
+          cache: false
+      - reports/pr_curve.png:
+          cache: false
+      - reports/eval_metrics.json:
+          cache: false

params.yaml

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+# params.yaml — Single source of truth for pipeline parameters.
+# DVC reads this file to detect when stages need to re-run.
+# MLflow logs these automatically during training.
+
+extract:
+  start_date: "2023-01-01"
+  end_date:   "2024-01-01"
+  airline:    null               # null = all airlines
+
+model:
+  n_estimators:     500
+  max_depth:        6
+  learning_rate:    0.05
+  subsample:        0.8
+  colsample_bytree: 0.8
+  scale_pos_weight: 2.5
+  cv_folds:         5
+  random_state:     42
+
+evaluate:
+  threshold:        0.5
+
+monitoring:
+  drift_share_threshold: 0.30

requirements.txt

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+# ── Core ──────────────────────────────────────────────────────────────────────
+pandas==2.2.2
+numpy==1.26.4
+scikit-learn==1.4.2
+
+# ── ML Models ─────────────────────────────────────────────────────────────────
+xgboost==2.0.3
+lightgbm==4.3.0
+hyperopt==0.2.7
+shap==0.45.0
+
+# ── MLOps ─────────────────────────────────────────────────────────────────────
+mlflow==2.13.0
+dvc[s3]==3.50.1
+
+# ── API ───────────────────────────────────────────────────────────────────────
+fastapi==0.111.0
+uvicorn[standard]==0.29.0
+pydantic==2.7.1
+python-multipart==0.0.9
+
+# ── ETL / Data ────────────────────────────────────────────────────────────────
+requests==2.32.2
+sqlalchemy==2.0.30
+psycopg2-binary==2.9.9
+pyarrow==16.0.0
+great-expectations==0.18.19
+apache-airflow==2.9.1
+
+# ── Cloud ─────────────────────────────────────────────────────────────────────
+boto3==1.34.106
+
+# ── Monitoring ────────────────────────────────────────────────────────────────
+evidently==0.4.30
+prometheus-client==0.20.0
+
+# ── UI (HF Spaces) ────────────────────────────────────────────────────────────
+gradio==4.31.0
+
+# ── Dev / Testing ─────────────────────────────────────────────────────────────
+pytest==8.2.0
+pytest-cov==5.0.0
+httpx==0.27.0          # FastAPI TestClient
+ruff==0.4.4
+mypy==1.10.0
+loguru==0.7.2
+python-dotenv==1.0.1