Phase 2 Sprint 2.2: Vision Isolation - Multimodal AI & Frontend Attachment Pipeline

config/prompts/wizardPrompt.txt

@@ -18,7 +18,12 @@ AVAILABLE PRODUCTS: bc, flyer, booklet, rollup, sticker, poster, invitation, pla
    - Put a natural Hebrew response in `answer_text` explicitly recommending products from the available list.
    - Return relevant product keys in the array `recommended_products` (e.g., ["invitation", "place_card", "sticker"]).
 
-4. **Output JSON format MUST be exactly**:
+5. **VISION ISOLATION**:
+   - When an image/file is uploaded, analyze ONLY the technical aspects (Resolution, DPI, Color Mode, Dimensions, Text elements).
+   - DO NOT decide if the file is "ready for print". That is the job of the Decision Kernel.
+   - Simply report the technical facts in `answer_text`.
+
+6. **Output JSON format MUST be exactly**:
 {
   "intent": "quote" | "consult" | "chat" | "remove" | "reset" | "update",
   "product": "product_key" | null,

db/usage.json

@@ -1,4 +1,4 @@
 {
-  "dailyCost": 5.1,
+  "dailyCost": 0,
   "lastReset": "2026-02-25"
 }

engine/classifier.js

@@ -1,6 +1,7 @@
 /** engine/classifier.js V98.0 - Data Integrity Fix */
 const { validateLLMResult } = require('./validator');
 const { routeWithLLM } = require('../services/llmService');
+const { downloadFile } = require('../services/storageService');
 
 const KEYWORDS = {
     reset: ['reset', 'התחל', 'איפוס', 'ריסט', 'תפריט'],
@@ -107,8 +108,25 @@ async function classify(text, session) {
 
     // 4. LLM Pipeline (למלל חופשי כמו "500")
     try {
-        const llmResult = await routeWithLLM(safeText, session);
-        console.log(`\x1b[35m🔍 [X-RAY CLASSIFIER] Intent: ${llmResult.intent || 'chat'} (LLM Inference)\x1b[0m`);
+        let imageBuffer = null;
+
+        // --- PHASE 2.2: Vision Attachment Handling ---
+        if (safeText.includes('[IMAGE_UPLOADED:')) {
+            const match = safeText.match(/\[IMAGE_UPLOADED:\s*([^\]]+)\]/);
+            if (match) {
+                const remotePath = match[1].trim();
+                console.log(`📸 [CLASSIFIER] Vision Request detected. Fetching: ${remotePath}`);
+                try {
+                    imageBuffer = await downloadFile(remotePath);
+                } catch (e) {
+                    console.error("Failed to fetch vision buffer:", e);
+                    return { intent: 'chat', aiResponse: 'הקובץ עלה אך לא הצלחתי לנתח אותו טכנית. תוכל לשלוח שוב?' };
+                }
+            }
+        }
+
+        const llmResult = await routeWithLLM(safeText, session, imageBuffer);
+        console.log(`\x1b[35m🔍 [X-RAY CLASSIFIER] Intent: ${llmResult.intent || 'chat'} (${imageBuffer ? 'Vision' : 'LLM'} Inference)\x1b[0m`);
         console.log(`🤖 [LLM RAW RESULT]:`, JSON.stringify(llmResult));
         const validated = validateLLMResult(llmResult, safeText, session);
 

public/index.html

@@ -417,7 +417,9 @@
 
             <div class="input-area">
                 <div class="icon-btn" onclick="toggleMenu()" title="תפריט מהיר"><i class="fas fa-bars"></i></div>
-                <div class="icon-btn"><i class="fas fa-paperclip"></i></div>
+                <div class="icon-btn" onclick="document.getElementById('file-input').click()"><i
+                        class="fas fa-paperclip"></i></div>
+                <input type="file" id="file-input" style="display:none" onchange="handleFileUpload(this)">
                 <div class="input-wrapper">
                     <input type="text" id="user-input" placeholder="הקלד הודעה..." autocomplete="off">
                 </div>
@@ -468,7 +470,8 @@
             const el = document.getElementById('connection-status');
             if (el) {
                 el.innerText = text;
-                el.style.color = text.includes('מקליד') ? 'var(--primary-color)' : '#667781';
+                el.innerText = text;
+                el.style.color = text.includes('מקליד') || text.includes('מעלה') ? 'var(--primary-color)' : '#667781';
             }
         }
 
@@ -568,11 +571,66 @@
         }
 
         // --- Core Functions ---
+        async function handleFileUpload(input) {
+            const file = input.files[0];
+            if (!file) return;
+
+            // 10MB client-side check
+            if (file.size > 10 * 1024 * 1024) {
+                alert("קובץ גדול מדי (מקסימום 10MB)");
+                input.value = '';
+                return;
+            }
+
+            isProcessing = true;
+            updateStatus('מעלה קובץ...');
+            addMsg(`מעלה קובץ: ${file.name}...`, 'user');
+
+            try {
+                // 1. Get Signed URL
+                const signedRes = await fetch(`${PDF_URL}/signed-url`, {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ userId: userId, fileName: file.name, fileSize: file.size })
+                });
+
+                if (!signedRes.ok) {
+                    const error = await signedRes.json();
+                    throw new Error(error.message || "נכשל בבקשת הכתובת להעלאה");
+                }
+
+                const { uploadUrl, remotePath } = await signedRes.json();
+
+                // 2. Direct upload to GCP
+                const uploadRes = await fetch(uploadUrl, {
+                    method: 'PUT',
+                    headers: { 'Content-Type': 'application/octet-stream' },
+                    body: file
+                });
+
+                if (!uploadRes.ok) throw new Error("העלאת הקובץ לשרת נכשלה");
+
+                addMsg("הקובץ עלה בהצלחה! 🖼️", "bot");
+                updateStatus('מכין ניתוח...');
+
+                // 3. Send system message to chat
+                await sendMessage(`[IMAGE_UPLOADED: ${remotePath}]`, "מנתח קובץ...");
+
+            } catch (e) {
+                console.error(e);
+                addMsg(`שגיאה בהעלאה: ${e.message}`, 'bot');
+            } finally {
+                input.value = '';
+                isProcessing = false;
+                updateStatus('מחובר');
+            }
+        }
+
         async function downloadPDF() {
             if (currentCart.length === 0) return alert('העגלה ריקה');
             addMsg("מפיק מסמך PDF... ⏳", "bot");
             try {
-                const res = await fetch(PDF_URL, {
+                const res = await fetch(`${PDF_URL}/quote`, {
                     method: 'POST',
                     headers: { 'Content-Type': 'application/json' },
                     body: JSON.stringify({ userId: userId, cart: currentCart })
@@ -611,7 +669,7 @@
 
         // --- TIKUN (FIX): Updated sendMessage to accept displayLabel ---
         async function sendMessage(text = null, displayLabel = null) {
-            if (isProcessing) return;
+            if (isProcessing && !text?.startsWith('[IMAGE_UPLOADED')) return;
 
             const inputField = document.getElementById('user-input');
             const valueToSend = text || inputField.value.trim(); // מה נשלח לשרת (קוד)
@@ -619,8 +677,10 @@
 
             if (!valueToSend) return;
 
-            // מציגים למשתמש את מה שהוא אמור לראות
-            addMsg(textToDisplay, 'user');
+            // מציגים למשתמש את מה שהוא אמור לראות (אלא אם זו הודעת מערכת פנימית של העלאה)
+            if (!text?.startsWith('[IMAGE_UPLOADED')) {
+                addMsg(textToDisplay, 'user');
+            }
 
             inputField.value = '';
             removeQuickReplies();

services/llmService.js

@@ -22,7 +22,7 @@ try {
     console.error("❌ Failed loading wizardPrompt.txt");
 }
 
-async function routeWithLLM(message, session) {
+async function routeWithLLM(message, session, imageBuffer = null) {
     if (isBudgetExceeded()) {
         console.warn("🛑 [BUDGET] Daily limit reached. Blocking LLM request.");
         return { intent: 'chat', answer_text: "המערכת בתחזוקה רגעית (נחזור לפעילות מלאה מחר בבוקר)", confidence: 0 };
@@ -39,19 +39,29 @@ async function routeWithLLM(message, session) {
         }
     });
 
-    const finalPrompt = SYSTEM_PROMPT
-        + `\n[CURRENT STATE]: Active Product: ${session.currentProduct || "None"}`
-        + `\n[USER SAYS]: "${message}"\nJSON Output:`;
-
-    console.log(`\n=================== AI REQUEST ===================`);
-    console.log(`📝 [LLM] Payload length: ${finalPrompt.length} chars (~${Math.round(finalPrompt.length / 4)} tokens)`);
+    // Multimodal Payload Preparation
+    const promptParts = [
+        { text: SYSTEM_PROMPT },
+        { text: `\n[CURRENT STATE]: Active Product: ${session.currentProduct || "None"}` },
+        { text: `\n[USER SAYS]: "${message}"\nJSON Output:` }
+    ];
+
+    if (imageBuffer) {
+        logAI("📷 Image detected in payload. Activating Multimodal Vision.");
+        promptParts.push({
+            inlineData: {
+                data: imageBuffer.toString('base64'),
+                mimeType: "image/jpeg"
+            }
+        });
+    }
 
     // PHASE 1.3 Anti-Hallucination: Retry Strategy & Token-Level Parsing
     let attempts = 0;
     while (attempts < 2) {
         try {
             console.time(`⏱️ [LLM] Response Time (Attempt ${attempts + 1})`);
-            const result = await model.generateContent(finalPrompt);
+            const result = await model.generateContent({ contents: [{ role: "user", parts: promptParts }] });
             console.timeEnd(`⏱️ [LLM] Response Time (Attempt ${attempts + 1})`);
 
             const usage = result.response.usageMetadata;
@@ -84,7 +94,7 @@ async function routeWithLLM(message, session) {
 
             // --- PHASE 1.3: Injecting Debug Metadata ---
             parsedObj._debug = {
-                source: "LLM",
+                source: imageBuffer ? "Vision" : "LLM",
                 tokens: tokenData,
                 apiCost: apiCostStr
             };

services/storageService.js

@@ -41,6 +41,22 @@ async function generateSignedUploadUrl(sessionId, fileName) {
     }
 }
 
+/**
+ * Downloads a file from GCP bucket as a Buffer.
+ */
+async function downloadFile(remotePath) {
+    try {
+        const file = storage.bucket(BUCKET_NAME).file(remotePath);
+        const [buffer] = await file.download();
+        console.log(`[STORAGE] Downloaded file buffer: ${remotePath}`);
+        return buffer;
+    } catch (error) {
+        console.error("Error downloading file from GCP:", error);
+        throw error;
+    }
+}
+
 module.exports = {
-    generateSignedUploadUrl
+    generateSignedUploadUrl,
+    downloadFile
 };