#!/usr/bin/env python3
# ╔══════════════════════════════════════════════════════════════════════╗
# ║  rutvik_prajapati.py  ·  AI Engineer  ·  AIDTM, Ahmedabad  🇮🇳        ║
# ╚══════════════════════════════════════════════════════════════════════╝
from __future__ import annotations
from dataclasses import dataclass, field
from typing import ClassVar, Final

@dataclass(frozen=True)
class RutvikPrajapati:
    """
    ⚡ AI Engineer who architects intelligent systems that think,
       reason, learn, and create — at production scale.
    """

    # ── Identity ────────────────────────────────────────────────────────
    name:      Final[str] = "Rutvik Prajapati"
    role:      Final[str] = "AI Engineer · ML Architect · Data Scientist"
    institute: Final[str] = "AIDTM — Adani Institute of Digital Technology Management"
    location:  Final[str] = "Ahmedabad, India 🇮🇳"

    # ── Arsenal ─────────────────────────────────────────────────────────
    STACK: ClassVar[dict[str, list[str]]] = {
        "🧠 Deep Learning" : ["PyTorch", "TensorFlow", "Keras", "JAX", "ONNX"],
        "🤖 Generative AI" : ["LangChain", "LlamaIndex", "HuggingFace 🤗", "OpenAI"],
        "📊 Data Science"  : ["Pandas", "NumPy", "Polars", "PySpark", "Plotly"],
        "👁  Vision"       : ["OpenCV", "YOLO", "Detectron2", "CLIP", "SAM"],
        "🗣  NLP"          : ["BERT", "Transformers", "spaCy", "NLTK", "Gensim"],
        "🚀 MLOps"         : ["FastAPI", "Docker", "Kubernetes", "Airflow", "MLflow"],
        "☁  Cloud"         : ["AWS SageMaker", "GCP Vertex AI", "Pinecone", "Redis"],
    }

    # ── Live Missions ───────────────────────────────────────────────────
    SHIPPING: ClassVar[list[str]] = [
        "🔥 Fine-tuning LLaMA-3.1 8B with QLoRA on domain corpus",
        "⚡ Multi-hop RAG: Pinecone + LangChain + Reranker + FastAPI",
        "🌐 Vision AI platform: YOLO · Django · TF Serving · React",
        "🧬 Autonomous AI Agents with memory, planning & tool-use",
        "📊 Real-time ML Dashboard: Streamlit + WebSockets + Kafka",
    ]

    def philosophy(self) -> str:
        return "Data is the new oil. AI is the refinery. 🛢️ → ⚡"

    def mantra(self) -> str:
        return "Code it. Train it. Deploy it. Improve it. Repeat. 🔄"

    def __str__(self) -> str:
        return (
            f"<AIEngineer name={self.name!r} "
            f"institute='AIDTM' status='Building intelligence 🚀'>"
        )

if __name__ == "__main__":
    me = RutvikPrajapati()
    print(me)
    # → <AIEngineer name='Rutvik Prajapati' institute='AIDTM' status='Building intelligence 🚀'>
    print(me.philosophy())
    # → Data is the new oil. AI is the refinery. 🛢️ → ⚡

# ── Rutvik's Full Data Science Engineering Pipeline ──────────────────────────
import pandas as pd, numpy as np, torch
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score
import optuna, mlflow, shap

def build_intelligence(raw_data: pd.DataFrame) -> dict:
    """
    ╔═══════════════════════════════════════════════════════════════════════╗
    ║  STEP 1 │ 🔍 EXPLORE     →  EDA · heatmaps · distributions · outliers║
    ║  STEP 2 │ 🧹 CLEAN       →  impute · encode · normalize · dedupe     ║
    ║  STEP 3 │ 🔧 ENGINEER    →  PCA · embeddings · domain features       ║
    ║  STEP 4 │ 🧠 MODEL       →  XGBoost · PyTorch · Transformer · BERT   ║
    ║  STEP 5 │ ⚡ OPTIMIZE    →  Optuna HPO · early stopping · scheduler  ║
    ║  STEP 6 │ 📏 EVALUATE    →  AUC · F1 · RMSE · confusion matrix       ║
    ║  STEP 7 │ 🔬 EXPLAIN     →  SHAP · LIME · attention weights          ║
    ║  STEP 8 │ 🐳 CONTAINERIZE→  Docker · FastAPI · health checks         ║
    ║  STEP 9 │ ☁  DEPLOY      →  AWS SageMaker / GCP Vertex AI            ║
    ║  STEP 10│ 📡 MONITOR     →  drift detection · latency · A/B test     ║
    ║  STEP 11│ 🔄 RETRAIN     →  scheduled retraining · data pipelines    ║
    ╚═══════════════════════════════════════════════════════════════════════╝
    """
    with mlflow.start_run():
        study = optuna.create_study(direction="maximize")
        study.optimize(lambda trial: cross_val_score(...).mean(), n_trials=100)
        mlflow.log_params(study.best_params)
        mlflow.log_metric("best_score", study.best_value)

    return {"status": "🚀 deployed", "accuracy": "state_of_the_art", "serving": "FastAPI + Docker"}

# philosophy: "Without data you're just another person with an opinion." — W.E. Deming