Claude/workout program designer sike9

.gitignore

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+__pycache__/
+*.pyc
+*.pyo
+*.egg-info/
+dist/
+build/
+.env

README.md

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-# Complete-Python-3-Bootcamp
-Course Files for Complete Python 3 Bootcamp Course on Udemy
+# Ironforge — Evidence-Based Workout Program Designer
+
+A Python CLI tool that generates personalized training programs based on peer-reviewed exercise science (18 papers, 2023-2025).
+
+## How It Works
+
+1. **Intake** — Answers structured questions about goals, training history, schedule, equipment, and individual factors
+2. **Algorithm** — Applies evidence-based rules for volume, frequency, exercise selection, load/RIR, and periodization
+3. **Output** — Generates a complete mesocycle with per-session exercises, sets, reps, RIR, rest periods, progression, and deload protocols
+
+## Quick Start
+
+```bash
+python -m ironforge
+```
+
+Or with JSON output:
+
+```bash
+python -m ironforge --format json
+```
+
+## What It Generates
+
+1. **Training Level Assessment** — Per movement pattern (squat, bench, row, etc.)
+2. **Volume Targets** — Weekly fractional sets per muscle group (MEV → MRV)
+3. **Split Structure** — Full Body / Upper-Lower / PPL based on available days
+4. **Weekly Program** — Each session with exercises, sets × reps, RIR, rest, coaching notes
+5. **Progression Instructions** — Linear / Double Progression / Autoregulated based on level
+6. **Deload Protocol** — Active deload with reactive triggers
+7. **Mesocycle Overview** — 5-week block structure (4 training + 1 deload)
+
+## Key Algorithm Features
+
+- **Fractional set accounting** — Compound exercises give partial credit to secondary muscles
+- **Per-pattern classification** — You can be intermediate on bench but beginner on squat
+- **97 approved exercises** — Only evidence-backed movements with proper muscle targeting
+- **A/B session variation** — Different exercises between repeated session types
+- **Antagonist supersets** — Optional time-saving pairing (reduces session time ~36%)
+- **Sex-adjusted rep ranges** — Females biased toward higher rep ranges (more Type I fiber)
+- **Equipment filtering** — Works for full gyms, limited gyms, and home setups
+
+## Project Structure
+
+```
+ironforge/
+├── data/           # Exercise database, muscle groups, algorithm constants
+├── intake/         # Questionnaire flow and user profile
+├── engine/         # Classifier, volume, frequency, selection, load, supersets, periodization
+├── program/        # Data models and builder (orchestrator)
+└── output/         # Terminal formatter
+```
+
+## Requirements
+
+- Python 3.11+
+- Zero external dependencies
+
+## Tests
+
+```bash
+python -m unittest tests.test_program -v
+```

api/index.py

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+"""Vercel serverless entry point — wraps the Flask app."""
+
+import sys
+import os
+
+# Add project root to path so ironforge package is importable
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from ironforge.web import app
+
+# Vercel expects a WSGI-compatible `app` object

ironforge/__main__.py

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+from ironforge.cli import main
+
+if __name__ == "__main__":
+    main()

ironforge/cli.py

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+"""CLI entry point for Ironforge."""
+
+import argparse
+import json
+from dataclasses import asdict
+
+from ironforge.intake.flow import run_intake
+from ironforge.program.builder import build_program
+from ironforge.output.formatter import render
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Ironforge — Evidence-Based Workout Program Designer",
+    )
+    parser.add_argument(
+        "--format", choices=["text", "json"], default="text",
+        help="Output format (default: text)",
+    )
+    args = parser.parse_args()
+
+    # Run intake questionnaire
+    profile = run_intake()
+
+    # Build program
+    program = build_program(profile)
+
+    # Output
+    if args.format == "json":
+        # Convert enums to strings for JSON serialization
+        def _convert(obj):
+            if hasattr(obj, "name") and hasattr(obj, "value"):
+                return obj.name
+            raise TypeError(f"Cannot serialize {type(obj)}")
+
+        data = asdict(program)
+        print(json.dumps(data, indent=2, default=_convert))
+    else:
+        output = render(program)
+        print(output)
+
+
+if __name__ == "__main__":
+    main()

ironforge/data/constants.py

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+"""Algorithm constants: volume landmarks, rep ranges, RIR targets, rest periods."""
+
+from ironforge.data.muscle_groups import VolumeMuscle, Tier, TrainingLevel
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class VolumeLandmarks:
+    mev_low: float
+    mev_high: float
+    mav_low: float
+    mav_high: float
+    mrv: float
+
+
+# Volume landmarks in fractional sets per week
+VOLUME_LANDMARKS: dict[VolumeMuscle, VolumeLandmarks] = {
+    VolumeMuscle.QUADS:          VolumeLandmarks(4, 6, 6, 14, 18),
+    VolumeMuscle.CHEST:          VolumeLandmarks(10, 10, 12, 20, 22),
+    VolumeMuscle.BACK:           VolumeLandmarks(8, 8, 10, 20, 25),
+    VolumeMuscle.BICEPS:         VolumeLandmarks(8, 8, 14, 20, 20),
+    VolumeMuscle.SIDE_REAR_DELTS: VolumeLandmarks(8, 8, 16, 22, 26),
+    VolumeMuscle.HAMSTRINGS:     VolumeLandmarks(4, 6, 6, 12, 16),
+    VolumeMuscle.TRICEPS:        VolumeLandmarks(4, 4, 8, 18, 18),
+    VolumeMuscle.CALVES:         VolumeLandmarks(12, 12, 12, 18, 20),
+    VolumeMuscle.GLUTES:         VolumeLandmarks(4, 6, 6, 14, 18),
+    VolumeMuscle.ABS:            VolumeLandmarks(2, 4, 4, 10, 14),
+    VolumeMuscle.FOREARMS:       VolumeLandmarks(2, 2, 4, 8, 10),
+}
+
+
+# Rep ranges by tier — all capped to 4-10
+REP_RANGES: dict[Tier, tuple[int, int]] = {
+    Tier.T1: (4, 6),
+    Tier.T2: (6, 8),
+    Tier.T3: (8, 10),
+}
+
+# Female bias: slightly higher within the 4-10 window
+REP_RANGES_FEMALE: dict[Tier, tuple[int, int]] = {
+    Tier.T1: (5, 8),
+    Tier.T2: (6, 10),
+    Tier.T3: (8, 10),
+}
+
+
+# RIR targets by week of mesocycle (1-indexed)
+RIR_BY_WEEK: dict[int, int] = {
+    1: 3,
+    2: 2,
+    3: 2,
+    4: 1,
+}
+
+RIR_DELOAD: int = 5
+
+# Beginner RIR override (their calibration is off by 3-5 reps)
+RIR_BEGINNER: int = 3
+
+# Rest periods in seconds (min, max)
+REST_PERIODS: dict[Tier, tuple[int, int]] = {
+    Tier.T1: (120, 180),
+    Tier.T2: (120, 150),
+    Tier.T3: (90, 120),
+}
+
+# Per-session ceiling: direct hard sets per muscle
+PER_SESSION_DIRECT_CEILING = 8
+PER_SESSION_FRACTIONAL_CEILING = 11
+
+# Maximum sets per exercise per session
+MAX_SETS_PER_EXERCISE = 3
+
+# Compound spillover contributions (fractional sets)
+# Maps from exercise muscle group categories to secondary contributions
+COMPOUND_SPILLOVER = {
+    "bench_press": {
+        "primary": "chest",
+        "secondary": {"triceps": 0.5, "front_delt": 0.5},
+    },
+    "overhead_press": {
+        "primary": "front_delt",
+        "secondary": {"triceps": 0.5, "side_delt": 0.3},
+    },
+    "row": {
+        "primary": "back",
+        "secondary": {"biceps": 0.5, "rear_delt": 0.3},
+    },
+    "pulldown": {
+        "primary": "lats",
+        "secondary": {"biceps": 0.5},
+    },
+    "squat": {
+        "primary": "quads",
+        "secondary": {"glutes": 0.5},
+    },
+    "rdl": {
+        "primary": "hamstrings",
+        "secondary": {"glutes": 0.5, "spinal_erectors": 0.5},
+    },
+    "hip_thrust": {
+        "primary": "glutes",
+        "secondary": {},
+    },
+    "dip": {
+        "primary": "chest",
+        "secondary": {"triceps": 0.5},
+    },
+}
+
+
+# Split templates by days per week
+SPLIT_TEMPLATES = {
+    3: "full_body",
+    4: "upper_lower",
+    5: "ul_ppl_hybrid",
+    6: "ppl",
+}
+
+# Deload frequency by training level (weeks between deloads)
+DELOAD_FREQUENCY: dict[TrainingLevel, tuple[int, int]] = {
+    TrainingLevel.BEGINNER: (8, 12),
+    TrainingLevel.INTERMEDIATE: (4, 6),
+    TrainingLevel.ADVANCED: (3, 5),
+}
+
+# Mesocycle length (training weeks only — no forced deload)
+MESO_LENGTH = 4