(feat) predict spend

enterprise/utils.py

@@ -251,27 +251,30 @@ def _forecast_daily_cost(data: list):
     import requests
     from datetime import datetime, timedelta
 
-    # Get the last entry in the data
+    first_entry = data[0]
     last_entry = data[-1]
 
-    # Parse the date from the last entry
-    last_entry_date = datetime.strptime(last_entry["date"], "%Y-%m-%d").date()
-    # print("Last Entry Date:", last_entry_date)
+    # get the date today
+    today_date = datetime.today().date()
 
-    # Get the month of the last entry
-    last_entry_month = last_entry_date.month
-    # print("Last Entry Month:", last_entry_month)
+    today_day_month = today_date.month
+
+    # Parse the date from the first entry
+    first_entry_date = datetime.strptime(first_entry["date"], "%Y-%m-%d").date()
+    last_entry_date = datetime.strptime(last_entry["date"], "%Y-%m-%d")
+
+    print("last entry date", last_entry_date)
+
+    # Assuming today_date is a datetime object
+    today_date = datetime.now()
 
     # Calculate the last day of the month
-    last_day_of_month = (
-        datetime(last_entry_date.year, last_entry_date.month % 12 + 1, 1)
-        - timedelta(days=1)
-    ).day
-    # print("Last Day of Month:", last_day_of_month)
+    last_day_of_todays_month = datetime(
+        today_date.year, today_date.month % 12 + 1, 1
+    ) - timedelta(days=1)
 
     # Calculate the remaining days in the month
-    remaining_days = last_day_of_month - last_entry_date.day
-    # print("Remaining Days:", remaining_days)
+    remaining_days = (last_day_of_todays_month - last_entry_date).days + 1
 
     series = {}
     for entry in data:
@@ -279,6 +282,17 @@ def _forecast_daily_cost(data: list):
         spend = entry["spend"]
         series[date] = spend
 
+    if len(series) < 10:
+        num_items_to_fill = 11 - len(series)
+
+        # avg spend for all days in series
+        avg_spend = sum(series.values()) / len(series)
+        for i in range(num_items_to_fill):
+            # go backwards from the first entry
+            date = first_entry_date - timedelta(days=i)
+            series[date.strftime("%Y-%m-%d")] = avg_spend
+            series[date.strftime("%Y-%m-%d")] = avg_spend
+
     payload = {"series": series, "count": remaining_days}
     print("Prediction Data:", payload)
 
@@ -291,6 +305,8 @@ def _forecast_daily_cost(data: list):
         json=payload,
         headers=headers,
     )
+    # check the status code
+    response.raise_for_status()
 
     json_response = response.json()
     forecast_data = json_response["forecast"]
@@ -314,15 +330,6 @@ def _forecast_daily_cost(data: list):
 # _forecast_daily_cost(
 #     [
 #         {"date": "2022-01-01", "spend": 100},
-#         {"date": "2022-01-02", "spend": 200},
-#         {"date": "2022-01-03", "spend": 300},
-#         {"date": "2022-01-04", "spend": 400},
-#         {"date": "2022-01-05", "spend": 500},
-#         {"date": "2022-01-06", "spend": 600},
-#         {"date": "2022-01-07", "spend": 700},
-#         {"date": "2022-01-08", "spend": 800},
-#         {"date": "2022-01-09", "spend": 900},
-#         {"date": "2022-01-10", "spend": 1000},
-#         {"date": "2022-01-11", "spend": 50},
+
 #     ]
 # )

litellm/proxy/proxy_server.py

@@ -786,6 +786,7 @@ async def user_api_key_auth(
                 "/global/spend/logs",
                 "/global/spend/keys",
                 "/global/spend/models",
+                "/global/predict/spend/logs",
             ]
             # check if the current route startswith any of the allowed routes
             if (
@@ -4199,6 +4200,7 @@ async def global_predict_spend_logs(request: Request):
     from litellm.proxy.enterprise.utils import _forecast_daily_cost
 
     data = await request.json()
+    data = data.get("data")
     return _forecast_daily_cost(data)
 
 

ui/litellm-dashboard/src/components/networking.tsx

@@ -748,3 +748,42 @@ export const userUpdateUserCall = async (
     throw error;
   }
 };
+
+
+export const PredictedSpendLogsCall = async (accessToken: string, requestData: any) => {
+  try {
+    let url = proxyBaseUrl
+      ? `${proxyBaseUrl}/global/predict/spend/logs`
+      : `/global/predict/spend/logs`;
+
+    //message.info("Predicting spend logs request");
+
+    const response = await fetch(url, {
+      method: "POST",
+      headers: {
+        Authorization: `Bearer ${accessToken}`,
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify(
+        {
+          data: requestData
+        }
+      ),
+    });
+
+    if (!response.ok) {
+      const errorData = await response.text();
+      message.error(errorData);
+      throw new Error("Network response was not ok");
+    }
+
+    const data = await response.json();
+    console.log(data);
+    //message.success("Predicted Logs received");
+    return data;
+  } catch (error) {
+    console.error("Failed to create key:", error);
+    throw error;
+  }
+};
+

ui/litellm-dashboard/src/components/view_key_spend_report.tsx

@@ -21,7 +21,7 @@ import {
   BarList,
   Metric,
 } from "@tremor/react";
-import { keySpendLogsCall } from "./networking";
+import { keySpendLogsCall, PredictedSpendLogsCall } from "./networking";
 
 interface ViewKeySpendReportProps {
   token: string;
@@ -48,6 +48,9 @@ const ViewKeySpendReport: React.FC<ViewKeySpendReportProps> = ({
   const [data, setData] = useState<{ day: string; spend: number }[] | null>(
     null
   );
+  const [predictedSpend, setPredictedSpend] = useState<{ day: string; spend: number }[] | null>(
+    null
+  );
   const [userData, setUserData] = useState<
     { name: string; value: number }[] | null
   >(null);
@@ -79,6 +82,18 @@ const ViewKeySpendReport: React.FC<ViewKeySpendReportProps> = ({
       );
       console.log("Response:", response);
       setData(response);
+
+      // predict spend based on response
+      const predictedSpend = await PredictedSpendLogsCall(accessToken, response);
+      console.log("Response2:", predictedSpend);
+
+      // append predictedSpend to data
+      const combinedData = [...response, ...predictedSpend];
+      setData(combinedData);
+
+      console.log("Combined Data:", combinedData);
+      // setPredictedSpend(predictedSpend);
+      
     } catch (error) {
       console.error("There was an error fetching the data", error);
     }
@@ -110,9 +125,9 @@ const ViewKeySpendReport: React.FC<ViewKeySpendReportProps> = ({
             <BarChart
               className="mt-6"
               data={data}
-              colors={["blue"]}
+              colors={["blue", "amber"]}
               index="date"
-              categories={["spend"]}
+              categories={["spend", "predicted_spend"]}
               yAxisWidth={48}
             />
           )}