Update app.py

app.py

@@ -1,42 +1,3 @@
-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):
     """
@@ -50,11 +11,12 @@ def calculate_food_endpoint(activity: int, weight: int):
         A JSON object containing the recommended amount of food in cups.
     """
 
-    result = calculate_food(activity, weight)
-    return result
+    # Check if the activity and weight parameters are present in the API request.
+    if activity is None or weight is None:
+        return {"error": "Please provide both activity level and weight."}
 
+    # Calculate the recommended amount of food.
+    result = calculate_food(activity, weight)
 
-if __name__ == "__main__":
-    import uvicorn
-
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    # Respond with the result as JSON.
+    return result