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- Interactive routine generation wizard with AI refinement loop (generate-draft, refine, save-draft endpoints + RoutineWizard modal component) - Replace +/- stepper buttons with slider controls for reps/weight during workout - Persist user-modified reps/weight across sets of the same exercise - Add pause/resume by tapping timer dials, with back-button confirmation Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
102 lines
4.7 KiB
Python
102 lines
4.7 KiB
Python
import dspy
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from pydantic import BaseModel, Field
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class ExerciseBlock(BaseModel):
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order: int = Field(description="Position in workout sequence")
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name: str = Field(description="Exercise name")
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description: str = Field(description="Brief description of the movement")
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sets: int = Field(description="Number of sets")
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reps: int = Field(description="Number of reps per set (0 if timed)")
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duration_seconds: int = Field(description="Duration in seconds per set (0 if rep-based)")
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weight_kg: float = Field(description="Prescribed weight in kg")
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rest_seconds: int = Field(description="Rest time between sets in seconds")
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coaching_tip: str = Field(description="Key coaching cue for this exercise")
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class KettlebellSessionOutput(BaseModel):
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reasoning: str = Field(description="Step-by-step reasoning for session design choices")
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title: str = Field(description="Session title")
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focus: str = Field(description="Session focus e.g. strength, conditioning, mobility")
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total_duration_min: int = Field(description="Estimated total workout duration in minutes")
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difficulty: str = Field(description="Difficulty level: beginner, intermediate, advanced")
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exercises: list[ExerciseBlock] = Field(description="Ordered list of exercises in the session")
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notes: str = Field(description="Coaching notes and any special instructions for the session")
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class GenerateKettlebellSession(dspy.Signature):
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"""Generate a personalized kettlebell workout session based on user profile and preferences.
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Think step-by-step: assess user fitness level, pick movements appropriate to the focus and
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difficulty, assign weights respecting progressive overload principles from available weights,
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sequence exercises for proper warm-up and fatigue management, and ensure total work time
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(sets × reps/duration + rest periods) fits within the requested duration.
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"""
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user_profile: str = dspy.InputField(desc="User details including age, weight, fitness level, and goals")
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available_weights_kg: str = dspy.InputField(desc="Comma-separated list of available kettlebell weights in kg")
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focus: str = dspy.InputField(desc="Session focus: strength, conditioning, mobility, fat loss, etc.")
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duration_minutes: int = dspy.InputField(desc="Target session duration in minutes")
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session: KettlebellSessionOutput = dspy.OutputField(desc="Complete structured kettlebell session")
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class KettlebellModule(dspy.Module):
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def __init__(self):
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super().__init__()
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self.generate = dspy.ChainOfThought(GenerateKettlebellSession)
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def forward(self, user_profile: str, available_weights_kg: str, focus: str, duration_minutes: int):
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return self.generate(
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user_profile=user_profile,
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available_weights_kg=available_weights_kg,
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focus=focus,
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duration_minutes=duration_minutes,
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)
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kettlebell_module = KettlebellModule()
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class RefineKettlebellSession(dspy.Signature):
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"""Refine an existing kettlebell workout session based on user feedback.
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You are given the current session and the user's feedback. Apply the requested changes
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while keeping the rest of the session intact. Maintain proper exercise sequencing,
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warm-up/cooldown structure, and ensure total work time still fits the target duration.
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"""
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current_session: str = dspy.InputField(desc="JSON representation of the current session")
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user_feedback: str = dspy.InputField(desc="User's requested changes to the session")
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user_profile: str = dspy.InputField(desc="User details including age, weight, fitness level, and goals")
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available_weights_kg: str = dspy.InputField(desc="Comma-separated list of available kettlebell weights in kg")
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focus: str = dspy.InputField(desc="Session focus: strength, conditioning, mobility, fat loss, etc.")
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duration_minutes: int = dspy.InputField(desc="Target session duration in minutes")
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session: KettlebellSessionOutput = dspy.OutputField(desc="Refined structured kettlebell session")
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class KettlebellRefineModule(dspy.Module):
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def __init__(self):
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super().__init__()
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self.refine = dspy.ChainOfThought(RefineKettlebellSession)
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def forward(
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self,
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current_session: str,
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user_feedback: str,
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user_profile: str,
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available_weights_kg: str,
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focus: str,
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duration_minutes: int,
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):
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return self.refine(
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current_session=current_session,
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user_feedback=user_feedback,
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user_profile=user_profile,
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available_weights_kg=available_weights_kg,
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focus=focus,
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duration_minutes=duration_minutes,
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)
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kettlebell_refine_module = KettlebellRefineModule()
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