the dynamic import is causing issue during build, but it's not an issue we can just put the var init inside the function

src/app/queries/predict.ts

@@ -1,13 +1,9 @@
 "use server"
 
 import { LLMEngine } from "@/types"
+import { predict as predictWithHuggingFace } from "./predictWithHuggingFace"
+import { predict as predictWithOpenAI } from "./predictWithOpenAI"
 
 const llmEngine = `${process.env.LLM_ENGINE || ""}` as LLMEngine
 
-export const predict = async () => {
-    if (llmEngine === "OPENAI")  {
-        return (await import("./predictWithOpenAI")).predictWithOpenAI
-    } else {
-        return (await import("./predictWithHuggingFace")).predictWithHuggingFace
-    }
-} 
+export const predict = llmEngine === "OPENAI" ? predictWithOpenAI : predictWithHuggingFace

src/app/queries/predictWithHuggingFace.ts

@@ -3,45 +3,45 @@
 import { HfInference, HfInferenceEndpoint } from "@huggingface/inference"
 import { LLMEngine } from "@/types"
 
-const hf = new HfInference(process.env.AUTH_HF_API_TOKEN)
+export async function predict(inputs: string): Promise<string> {
+  const hf = new HfInference(process.env.AUTH_HF_API_TOKEN)
 
-const llmEngine = `${process.env.LLM_ENGINE || ""}` as LLMEngine
-const inferenceEndpoint = `${process.env.LLM_HF_INFERENCE_ENDPOINT_URL || ""}`
-const inferenceModel = `${process.env.LLM_HF_INFERENCE_API_MODEL || ""}`
+  const llmEngine = `${process.env.LLM_ENGINE || ""}` as LLMEngine
+  const inferenceEndpoint = `${process.env.LLM_HF_INFERENCE_ENDPOINT_URL || ""}`
+  const inferenceModel = `${process.env.LLM_HF_INFERENCE_API_MODEL || ""}`
 
-let hfie: HfInferenceEndpoint = hf
+  let hfie: HfInferenceEndpoint = hf
 
-switch (llmEngine) {
-  case "INFERENCE_ENDPOINT":
-    if (inferenceEndpoint) {
-      console.log("Using a custom HF Inference Endpoint")
-      hfie = hf.endpoint(inferenceEndpoint)
-    } else {
-      const error = "No Inference Endpoint URL defined"
-      console.error(error)
-      throw new Error(error)
-    }
-    break;
-  
-  case "INFERENCE_API":
-    if (inferenceModel) {
-      console.log("Using an HF Inference API Model")
-    } else {
-      const error = "No Inference API model defined"
+  switch (llmEngine) {
+    case "INFERENCE_ENDPOINT":
+      if (inferenceEndpoint) {
+        console.log("Using a custom HF Inference Endpoint")
+        hfie = hf.endpoint(inferenceEndpoint)
+      } else {
+        const error = "No Inference Endpoint URL defined"
+        console.error(error)
+        throw new Error(error)
+      }
+      break;
+    
+    case "INFERENCE_API":
+      if (inferenceModel) {
+        console.log("Using an HF Inference API Model")
+      } else {
+        const error = "No Inference API model defined"
+        console.error(error)
+        throw new Error(error)
+      }
+      break;
+
+    default:
+      const error = "Please check your Hugging Face Inference API or Inference Endpoint settings"
       console.error(error)
       throw new Error(error)
-    }
-    break;
-
-  default:
-    const error = "Please check your Hugging Face Inference API or Inference Endpoint settings"
-    console.error(error)
-    throw new Error(error)
-}
-  
-const api = llmEngine === "INFERENCE_ENDPOINT" ? hfie : hf
+  }
+    
+  const api = llmEngine === "INFERENCE_ENDPOINT" ? hfie : hf
 
-export async function predictWithHuggingFace(inputs: string) {
   let instructions = ""
   try {
     for await (const output of api.textGenerationStream({

src/app/queries/predictWithOpenAI.ts

@@ -3,9 +3,8 @@
 import type { ChatCompletionMessage } from "openai/resources/chat"
 import OpenAI from "openai"
 
-const openaiApiKey = `${process.env.AUTH_OPENAI_API_KEY || ""}`
-
-export async function predictWithOpenAI(inputs: string) {
+export async function predict(inputs: string): Promise<string> {
+  const openaiApiKey = `${process.env.AUTH_OPENAI_API_KEY || ""}`
   const openaiApiBaseUrl = `${process.env.LLM_OPENAI_API_BASE_URL || "https://api.openai.com/v1"}`
   const openaiApiModel = `${process.env.LLM_OPENAI_API_MODEL || "gpt-3.5-turbo"}`
   
@@ -26,8 +25,9 @@ export async function predictWithOpenAI(inputs: string) {
       temperature: 0.8
     })
 
-    return res.choices[0].message.content
+    return res.choices[0].message.content || ""
   } catch (err) {
     console.error(`error during generation: ${err}`)
+    return ""
   }
 }