Spaces:
Sleeping
Sleeping
File size: 1,656 Bytes
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from fastapi import FastAPI
from transformers import pipeline
# NOTE - we configure docs_url to serve the interactive Docs at the root path
# of the app. This way, we can use the docs as a landing page for the app on Spaces.
app = FastAPI(docs_url="/")
pipe = pipeline("text2text-generation", model="google/flan-t5-small")
def calculate_food(activity, weight):
"""
Calculates the recommended amount of dog food based on activity level and weight.
Args:
activity: The dog's activity level, as a number from 1 to 5.
weight: The dog's weight in kilograms.
Returns:
A dictionary containing the recommended amount of food in cups.
"""
# Calculate the resting energy requirement (RER).
rer = 70 * weight ** 0.75
# Multiply the RER by the appropriate factor to account for the dog's activity level.
activity_factor = {
1: 1.2,
2: 1.4,
3: 1.6,
4: 1.8,
5: 2.0,
}
recommended_food = rer * activity_factor[activity] / weight
return {"recommendedFood": round(recommended_food, 2)}
@app.get("/calculate-food")
def calculate_food_endpoint(activity: int, weight: int):
"""
Calculates the recommended amount of dog food based on activity level and weight.
Args:
activity: The dog's activity level, as a number from 1 to 5.
weight: The dog's weight in kilograms.
Returns:
A JSON object containing the recommended amount of food in cups.
"""
result = calculate_food(activity, weight)
return result
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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