Revert "Jiayu-fix bugs in LLM (#109)"

This reverts commit 1bcc8d0.

Author: Turquoise-T

Stanford360/Protein/Model/PromptTemplate.swift

@@ -15,13 +15,7 @@ class ProteinPromptConstructor: ObservableObject {
     func constructPrompt(mealName: String) -> String {
         let prompt = """
         You are an expert in nutritional science with a focus on dietary needs for children aged 10-15.
-        Here are some important information that you can refer:
-        1. Those belong to Meats, Poultry, and Fish usually around 6-10 grams.
-        2. Those belong to Soy and Vegetable Protein usually around 3-13 grams.
-        3. Those belong to Legumes and Nuts usually around 5-10 grams.
-        4. Those belong to Milk and Dairy usually around 10 grams.
-        5. Those belong to Vegetables usually around 2 grams.
-        6. Those belong to Grains usually around 5-10 grams.
+
         Task:
         1. Analyze the meal name: "\(mealName)"
         2. Determine the appropriate protein content (in grams) based on nutritional standards for this age group.

Stanford360/Protein/View/AddMealView.swift

@@ -350,23 +350,24 @@ extension AddMealView {
             self.proteinAmount = ""
         }
         let prompt = promptTemplate.constructPrompt(mealName: meal)
-        
-        let llmSchema = LLMLocalSchema(
-            // model: .custom(id: <#T##String#>),
-            model: .llama3_2_1B_4bit,
-            parameters: .init(
-                systemPrompt: prompt
+        let llmSession: LLMLocalSession = runner(
+            with: LLMLocalSchema(
+                model: .llama3_8B_4bit,
+                parameters: .init(
+                    systemPrompt: prompt
+                )
             )
         )
-        let llmSession = runner(with: llmSchema)
-        var output = ""
-        
+//        let llmSession: LLMLocalSession = runner(
+//            with: LLMLocalSchema(
+//                model: .llama3_8B_4bit
+//            )
+//        )
         do {
             for try await token in try await llmSession.generate() {
-                output.append(token)
-            }
-            await MainActor.run {
-                self.proteinAmount = output
+                await MainActor.run {
+                    self.proteinAmount.append(token)
+                }
             }
             print("Protein extracted is ", proteinAmount)
         } catch {

Stanford360/Protein/View/LLMLocalOnboardingDownloadView.swift

@@ -23,8 +23,8 @@ struct LLMLocalOnboardingDownloadView: View {
 
     var body: some View {
         LLMLocalDownloadView(
-            model: .llama3_2_1B_4bit,
-            downloadDescription: "The Llama3 1B model will be downloaded"
+            model: .llama3_8B_4bit,
+            downloadDescription: "The Llama3 8B model will be downloaded"
         ) {
             onboardingNavigationPath.nextStep()
         }

Stanford360/Resources/Localizable.xcstrings

@@ -332,10 +332,6 @@
 
     },
     "Hydration Recommendations" : {
-
-    },
-    "image" : {
-
     },
     "image" : {
 
@@ -707,7 +703,7 @@
     "Task completed for day %lld" : {
 
     },
-    "The Llama3 1B model will be downloaded" : {
+    "The Llama3 8B model will be downloaded" : {
 
     },
     "The Spezi Framework" : {

Stanford360Tests/ProteinTest/ProteinPromptConstructorTest.swift

@@ -0,0 +1,55 @@
+//
+//  ProteinPromptConstructorTest.swift
+//  Stanford360
+//
+//  Created by jiayu chang on 3/11/25.
+//
+// This source file is part of the Stanford 360 based on the Stanford Spezi Template Application project
+//
+// SPDX-FileCopyrightText: 2025 Stanford University
+//
+// SPDX-License-Identifier: MIT
+//
+
+@testable import Stanford360
+import XCTest
+
+final class ProteinPromptConstructorTests: XCTestCase {
+    var promptConstructor = ProteinPromptConstructor()
+
+    // Test if constructPrompt() generates the correct format
+    func testConstructPrompt() {
+        let mealName = "Grilled Chicken"
+        let expectedPrompt = """
+        You are an expert in nutritional science with a focus on dietary needs for children aged 10-15.
+
+        Task:
+        1. Analyze the meal name: "\(mealName)"
+        2. Determine the appropriate protein content (in grams) based on nutritional standards for this age group.
+        3. Respond with a single numeric value representing the protein content in grams.
+        4. Do not include any additional text in your response.
+        """
+
+        let result = promptConstructor.constructPrompt(mealName: mealName)
+        
+        XCTAssertEqual(result, expectedPrompt, "Prompt output does not match expected format.")
+    }
+    
+    // Test if constructPrompt() handles an empty meal name correctly
+    func testConstructPromptWithEmptyMealName() {
+        let mealName = ""
+        let expectedPrompt = """
+        You are an expert in nutritional science with a focus on dietary needs for children aged 10-15.
+
+        Task:
+        1. Analyze the meal name: "\(mealName)"
+        2. Determine the appropriate protein content (in grams) based on nutritional standards for this age group.
+        3. Respond with a single numeric value representing the protein content in grams.
+        4. Do not include any additional text in your response.
+        """
+
+        let result = promptConstructor.constructPrompt(mealName: mealName)
+        
+        XCTAssertEqual(result, expectedPrompt, "Prompt should handle an empty meal name correctly.")
+    }
+}