Upload 10 files

src/services/gemini.js

@@ -1,12 +1,11 @@
 
-import { GoogleGenerativeAI } from "https://esm.run/@google/generative-ai@0.23.0";
+// import { GoogleGenerativeAI } from "https://esm.run/@google/generative-ai"; // Removing SDK to avoid version issues
 import { getPeerPrompts } from "./classroom.js";
 import { db } from "./firebase.js";
 import { doc, getDoc } from "https://www.gstatic.com/firebasejs/10.7.1/firebase-firestore.js";
 
-let genAI = null;
-let model = null;
 let apiKey = null;
+const MODEL_NAME = "gemini-1.5-flash"; // Use standard model name
 
 // System Instructions for the Socratic Tutor
 const TUTOR_INSTRUCTION = `
@@ -50,14 +49,51 @@ Example: { "rough": ["id1"], "precise": ["id2", "id5"], "spam": ["id3"] ... }
 export async function initGemini(key) {
     if (!key) return false;
     apiKey = key;
-    try {
-        genAI = new GoogleGenerativeAI(apiKey);
-        model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });
-        return true;
-    } catch (e) {
-        console.error("Gemini Init Failed", e);
-        return false;
+    return true;
+}
+
+/**
+ * Direct REST API Call Helper to avoid SDK version issues
+ */
+async function callGeminiAPI(messages, jsonMode = false) {
+    if (!apiKey) throw new Error("API Key not set");
+
+    const endpoint = `https://generativelanguage.googleapis.com/v1beta/models/${MODEL_NAME}:generateContent?key=${apiKey}`;
+
+    const payload = {
+        contents: messages,
+        generationConfig: {
+            temperature: 0.7,
+        }
+    };
+
+    if (jsonMode) {
+        payload.generationConfig.responseMimeType = "application/json";
+    }
+
+    const response = await fetch(endpoint, {
+        method: "POST",
+        headers: {
+            "Content-Type": "application/json"
+        },
+        body: JSON.stringify(payload)
+    });
+
+    if (!response.ok) {
+        const errText = await response.text();
+        throw new Error(`Gemini API Error ${response.status}: ${errText}`);
+    }
+
+    const data = await response.json();
+
+    // Safety check for empty response
+    if (data.promptFeedback && data.promptFeedback.blockReason) {
+        throw new Error(`Blocked: ${data.promptFeedback.blockReason}`);
     }
+
+    return data.candidates && data.candidates.length > 0
+        ? data.candidates[0].content.parts[0].text
+        : "";
 }
 
 /**
@@ -90,26 +126,20 @@ export function quickSpamCheck(prompt, problemDesc) {
  * @param {string} challengeId 
  */
 export async function askTutor(challengeDesc, studentPrompt, roomCode, challengeId) {
-    if (!model) throw new Error("AI not initialized");
+    if (!apiKey) throw new Error("AI not initialized");
 
     const spamStatus = quickSpamCheck(studentPrompt, challengeDesc);
     if (spamStatus === true) return "Duck says: Quack? (Too short or empty!)";
     if (spamStatus === 'parrot') return "Duck says: You're just repeating the question! Try telling me WHAT you want to change.";
 
     // 1. Fetch Context (Few-Shot)
-    // We want 'Starred' examples first, then random successful ones.
     let peers = await getPeerPrompts(roomCode, challengeId);
-
-    // Filter for instructor stars (assuming 'isStarred' or 'likes' > threshold)
-    // Since 'isStarred' isn't in DB yet, we use 'likes' as proxy for quality or randomness.
-    // Ideally, we add a 'starred' field in Instructor Dashboard later.
-    // For now, let's prioritize high likes.
     peers.sort((a, b) => b.likes - a.likes);
 
     // Take top 3 as examples
     const examples = peers.slice(0, 3).map(p => `- Example: "${p.prompt}"`).join('\n');
 
-    const prompt = `
+    const fullPrompt = `
 ${TUTOR_INSTRUCTION}
 
 [Context - Challenge Description]
@@ -125,8 +155,10 @@ ${examples || "No examples available yet."}
 `;
 
     try {
-        const result = await model.generateContent(prompt);
-        return result.response.text();
+        // Construct messages for Chat format (or simple text)
+        const messages = [{ role: "user", parts: [{ text: fullPrompt }] }];
+        const text = await callGeminiAPI(messages);
+        return text;
     } catch (e) {
         console.error("AI Request Failed", e);
         return "Duck is sleeping... zzz (API Error)";
@@ -139,13 +171,13 @@ ${examples || "No examples available yet."}
  * @param {string} challengeDesc 
  */
 export async function evaluatePrompts(submissions, challengeDesc) {
-    if (!model) throw new Error("AI not initialized");
+    if (!apiKey) throw new Error("AI not initialized");
     if (!submissions || submissions.length === 0) return {};
 
     // Prepare batch text
     const entries = submissions.map((s, i) => `ID_${s.userId}: "${s.prompt.replace(/\n/g, ' ')}"`).join('\n');
 
-    const prompt = `
+    const fullPrompt = `
 ${ANALYST_INSTRUCTION}
 
 [Challenge Description]
@@ -158,11 +190,9 @@ ${entries}
 `;
 
     try {
-        const result = await model.generateContent({
-            contents: [{ role: "user", parts: [{ text: prompt }] }],
-            generationConfig: { responseMimeType: "application/json" }
-        });
-        return JSON.parse(result.response.text());
+        const messages = [{ role: "user", parts: [{ text: fullPrompt }] }];
+        const text = await callGeminiAPI(messages, true); // JSON Mode = true
+        return JSON.parse(text);
     } catch (e) {
         console.error("AI Evaluation Failed", e);
         return {};