Update app.py

app.py

@@ -9,12 +9,52 @@ app = FastAPI(docs_url="/")
 pipe = pipeline("text2text-generation", model="google/flan-t5-small")
 
 
-@app.get("/generate")
-def generate(text: str):
+def calculate_food(activity, weight):
     """
-    Using the text2text-generation pipeline from `transformers`, generate text
-    from the given input text. The model used is `google/flan-t5-small`, which
-    can be found [here](https://huggingface.co/google/flan-t5-small).
+    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):
     """
-    output = pipe(text)
-    return {"output": output[0]["generated_text"]}
+    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)