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Introduces DSPy-based nutrition and plan generation modules, including image analysis for nutritional info and personalized diet/exercise plans. Adds new API endpoints for health metrics/goals, nutrition image analysis, and plan management. Updates models, schemas, and backend structure to support these features, and includes initial training data and configuration for prompt optimization.
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
import dspy
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from pydantic import BaseModel, Field
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class PlanOutput(BaseModel):
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reasoning: str = Field(description="Reasoning behind the selected plan based on user goals")
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title: str = Field(description="Title of the plan")
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summary: str = Field(description="Brief summary of the plan")
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diet_plan: list[str] = Field(description="List of daily diet recommendations")
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exercise_plan: list[str] = Field(description="List of daily exercise routines")
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tips: list[str] = Field(description="Additional health tips")
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class GeneratePlan(dspy.Signature):
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"""Generate a personalized diet and exercise plan based on user goal and details.
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Analyze the user's profile and goal, explain your reasoning, and then generate the plan.
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"""
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user_profile: str = dspy.InputField(desc="User details (age, weight, height, etc)")
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goal: str = dspy.InputField(desc="Specific user goal")
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plan: PlanOutput = dspy.OutputField(desc="Structured plan with reasoning")
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class PlanModule(dspy.Module):
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def __init__(self):
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super().__init__()
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self.generate = dspy.ChainOfThought(GeneratePlan)
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def forward(self, user_profile: str, goal: str):
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return self.generate(user_profile=user_profile, goal=goal)
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plan_module = PlanModule()
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