mirror of
https://github.com/escalante29/healthy-fit.git
synced 2026-03-21 14:08:48 +01:00
bd91eb4171
Converted frontend codebase from JavaScript to TypeScript, including pages, components, and context. Added new layout and UI kit components. Updated backend user model and schemas to support profile fields (firstname, lastname, age, gender, height, weight, unit_preference) and added endpoints for reading/updating current user. Introduced food log listing endpoint and migration script for user table. Updated dependencies and build configs for TypeScript and Tailwind v4.
104 lines
4.1 KiB
Python
104 lines
4.1 KiB
Python
import base64
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import dspy
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from pydantic import BaseModel, Field
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from app.config import settings
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class NutritionalInfo(BaseModel):
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reasoning: str = Field(description="Step-by-step reasoning for the nutritional estimates")
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name: str = Field(description="Name of the food item")
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calories: float = Field(description="Estimated calories")
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protein: float = Field(description="Estimated protein in grams")
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carbs: float = Field(description="Estimated carbohydrates in grams")
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fats: float = Field(description="Estimated fats in grams")
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micros: dict | None = None
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class ExtractNutrition(dspy.Signature):
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"""Extract nutritional information from a food description.
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You must first provide a detailed step-by-step reasoning analysis of the ingredients,
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portions, AND preparation methods (cooking oils, butter, sauces) before estimating values.
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Verify if the caloric totals match the sum of macros (multiplying protein/carbs by 4, fats by 9).
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"""
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description: str = dspy.InputField(desc="Description of the food or meal")
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nutritional_info: NutritionalInfo = dspy.OutputField(desc="Nutritional information with reasoning")
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class AnalyzeFoodImage(dspy.Signature):
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"""Analyze the food image to estimate nutritional content.
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1. Identify all food items and estimated portion sizes.
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2. CRITICAL: Account for hidden calories from cooking fats, oils, and sauces (searing, frying).
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3. Reason step-by-step about the total composition before summing macros.
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"""
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image: dspy.Image = dspy.InputField(desc="The food image")
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description: str = dspy.InputField(desc="Additional user description", default="")
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nutritional_info: NutritionalInfo = dspy.OutputField(desc="Nutritional information with reasoning")
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class NutritionModule(dspy.Module):
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def __init__(self):
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super().__init__()
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self.extract = dspy.ChainOfThought(ExtractNutrition)
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self.analyze_image = dspy.ChainOfThought(AnalyzeFoodImage)
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# Load optimized prompts if available
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import os
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compiled_path = os.path.join(os.path.dirname(__file__), "nutrition_compiled.json")
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if os.path.exists(compiled_path):
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self.load(compiled_path)
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print(f"Loaded optimized DSPy prompts from {compiled_path}")
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else:
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print("No optimized prompts found, using default zero-shot.")
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def forward(self, description: str):
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pred = self.extract(description=description)
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# Assertion: Check Macro Consistency
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# calc_cals calculation removed as dspy.Suggest is disabled
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# dspy.Suggest is not available in dspy>=3.1.0
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# dspy.Suggest(
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# abs(calc_cals - pred.nutritional_info.calories) < (pred.nutritional_info.calories * 0.20),
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# f"The sum of macros ({calc_cals:.1f}) should match the total calories "
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# f"({pred.nutritional_info.calories}). Check your math.",
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# )
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return pred
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def forward_image(self, image_url: str, description: str = ""):
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image = dspy.Image(image_url)
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pred = self.analyze_image(image=image, description=description)
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# Assertion: Check Macro Consistency
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# calc_cals calculation removed as dspy.Suggest is disabled
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# dspy.Suggest is not available in dspy>=3.1.0
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# dspy.Suggest(
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# abs(calc_cals - pred.nutritional_info.calories) < (pred.nutritional_info.calories * 0.20),
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# f"The sum of macros ({calc_cals:.1f}) should match the total calories "
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# f"({pred.nutritional_info.calories}). Check your math.",
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# )
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return pred
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nutrition_module = NutritionModule()
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def analyze_nutrition_from_image(image_bytes: bytes, description: str = "") -> NutritionalInfo:
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if not settings.OPENAI_API_KEY:
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raise ValueError("OpenAI API Key not set")
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# Convert to base64 data URI
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base64_image = base64.b64encode(image_bytes).decode("utf-8")
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image_url = f"data:image/jpeg;base64,{base64_image}"
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# Use DSPy module
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result = nutrition_module.forward_image(image_url=image_url, description=description)
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return result.nutritional_info
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