Update apps/viralcat.js

apps/viralcat.js

@@ -23,9 +23,7 @@ const getBase64Payload = (req) => {
     return null;
 };
 
-const sleep = (ms) => new Promise(r => setTimeout(r, ms));
-
-const handleMediaProcessing = async (req, num_frames = 15, get_transcript = true) => {
+const handleMediaProcessing = async (req, num_frames = 14, get_transcript = true) => {
     const video_base64 = getBase64Payload(req);
     if (!video_base64) throw new Error("No video data found in request");
 
@@ -40,78 +38,86 @@ const handleMediaProcessing = async (req, num_frames = 15, get_transcript = true
     return mediaData;
 };
 
-// ── SEQUENTIAL MAP-REDUCE WITH AURA EXTRACTION ──
+// ── PARALLEL MAP-REDUCE WITH OPENROUTER ──
 const performDeepAnalysis = async (frames, transcript, targetNiche = null) => {
-    console.log(`[DEEP ANALYZE] Starting Map-Reduce on ${frames.length} frames...`);
+    console.log(`[DEEP ANALYZE] Starting Parallel Map-Reduce on ${frames.length} frames...`);
     
-    const cappedFrames = frames.slice(0, 15); 
+    // 1. Cap at 14 frames max
+    const cappedFrames = frames.slice(0, 14); 
     const chunks =[];
-    for (let i = 0; i < cappedFrames.length; i += 3) {
-        chunks.push(cappedFrames.slice(i, i + 3));
+    
+    // 2. Chunk into batches of 7 (OpenRouter's max attachment limit)
+    for (let i = 0; i < cappedFrames.length; i += 7) {
+        chunks.push(cappedFrames.slice(i, i + 7));
     }
 
-    const sceneDescriptions =[];
-    
-    for (let i = 0; i < chunks.length; i++) {
-        console.log(`-> Processing Batch ${i + 1}/${chunks.length}`);
-        const prompt = `Analyze this 3-image sequence. Describe the action, camera movement, lighting, and subjects briefly.`;
+    // 3. Process all chunks AT THE SAME TIME (No more sleep delays!)
+    console.log(`-> Firing ${chunks.length} parallel worker instances to OpenRouter...`);
+    const scenePromises = chunks.map((chunk, index) => {
+        const prompt = `Analyze this ${chunk.length}-image sequence. Return ONLY a JSON object with a single key "description" containing a brief summary of the action, camera movement, lighting, and subjects.`;
         
-        const res = await generateCompletion({ 
-            model: "qwen", 
+        return generateCompletion({ 
+            model: "qwen", // Qwen is cheapest and fastest for this vision task
             prompt, 
-            system_prompt: "You are a precise video frame analyzer.", 
-            images: chunks[i] 
+            system_prompt: "You are a precise video frame analyzer. Output strictly JSON.", 
+            images: chunk 
         });
-        
-        sceneDescriptions.push(`[Scene Segment ${i + 1}]:\n${res.data}`);
-        if (i < chunks.length - 1) await sleep(1500); 
-    }
+    });
+
+    // Wait for all workers to finish simultaneously
+    const sceneResults = await Promise.all(scenePromises);
+    
+    const sceneDescriptions = sceneResults.map((res, i) => {
+        try {
+            const parsed = JSON.parse(res.data);
+            return `[Scene Segment ${i + 1}]:\n${parsed.description}`;
+        } catch (e) {
+            return `[Scene Segment ${i + 1}]:\n${res.data}`; // Fallback if JSON parse fails
+        }
+    });
 
     const combinedVisualData = sceneDescriptions.join("\n\n");
     console.log(`[DEEP ANALYZE] Map phase complete. Synthesizing output...`);
 
     if (targetNiche) {
-        // 🚨 PROMPT ENG: Force the AI to establish the psychological hooks first!
+        // 🚨 REDUCE PHASE: Custom Remix
         const finalPrompt = `
 TASK: Rewrite an existing viral video script for a NEW NICHE.
 [ORIGINAL VISUAL TIMELINE]
-${combinedVisualData}
-[ORIGINAL AUDIO TRANSCRIPT]
+${combinedVisualData}[ORIGINAL AUDIO TRANSCRIPT]
 ${transcript}
 [NEW TARGET NICHE]
 User's Niche: "${targetNiche}"
 
 INSTRUCTIONS:
-1. DECONSTRUCT THE AURA: Analyze exactly what made the original video go viral. Look at the total time, pacing, specific psychological triggers, and choice of words in the hook.
-2. Map those EXACT psychological triggers, pacing, and hooks onto the user's new niche.
-3. DO NOT summarize the original video. Write a BRAND NEW script.
-
-FORMAT EXACTLY LIKE THIS:
-## 🧠 The Viral Aura[Briefly explain the psychology, word choice, and timing that made the original work, and how you applied it to this new script.]
+1. DECONSTRUCT THE AURA: Analyze exactly what made the original video go viral.
+2. Map those EXACT psychological triggers onto the user's new niche.
+3. Return ONLY a JSON object with a single key "script_markdown" containing the fully formatted markdown script.
 
+Markdown format inside the JSON string should be:
+## 🧠 The Viral Aura
+[Explanation]
 ## 🎬 The Hook (0-3s)
-**Visual:**[What to show]
+**Visual:** [What to show]
 **Audio:** [What to say]
-
 ## 📖 The Body
-**Visual:**[What to show]
-**Audio:** [What to say]
+...etc`;
 
-## 💥 The Call to Action
-**Visual:** [What to show]
-**Audio:** [What to say]`;
-
-        return await generateCompletion({ model: "maverick", prompt: finalPrompt, system_prompt: "You are Viral Cat 📈🐈." });
+        const result = await generateCompletion({ model: "maverick", prompt: finalPrompt, system_prompt: "You are Viral Cat 📈🐈. Output strictly JSON." });
+        return { success: true, data: JSON.parse(result.data).script_markdown };
+        
     } else {
-        // 🚨 PROMPT ENG: Extract the Viral Factors for the Database Feed
+        // 🚨 REDUCE PHASE: Database Template Extraction
         const finalPrompt = `[VISUAL TIMELINE]\n${combinedVisualData}\n[TRANSCRIPT]\n${transcript}\n
-Format exactly:
-TRANSCRIPT:[Cleaned dialogue]
-ENVIRONMENT:[Setting/lighting]
-PACING:[Edit style]
-VIRAL_FACTORS:[Analyze the psychology, specific word choices, hooks, and timing that made this viral]`;
+Analyze the data and return ONLY a JSON object with these exact keys:
+{
+  "transcript": "Cleaned dialogue",
+  "environment": "Setting/lighting description",
+  "pacing": "Edit style and camera movement",
+  "viral_factors": "Analyze the psychology, specific word choices, hooks, and timing that made this viral"
+}`;
         
-        return await generateCompletion({ model: "maverick", prompt: finalPrompt, system_prompt: "Elite metadata extractor." });
+        return await generateCompletion({ model: "maverick", prompt: finalPrompt, system_prompt: "Elite metadata extractor. Output strictly JSON." });
     }
 };
 
@@ -145,32 +151,36 @@ router.post('/admin/template', upload.single('video_file'), async (req, res) =>
         const { title, video_url, platform, use_deep_analysis } = req.body;
         const isDeep = (use_deep_analysis === 'true' || use_deep_analysis === true);
 
-        const framesToExtract = isDeep ? 15 : 3;
+        // Max 14 frames for deep, 7 for fast (fits in 1 OpenRouter request)
+        const framesToExtract = isDeep ? 14 : 7;
         const mediaData = await handleMediaProcessing(req, framesToExtract, true);
 
         let aiResult;
         if (isDeep) {
             aiResult = await performDeepAnalysis(mediaData.frames, mediaData.transcript);
         } else {
-            // Fast extraction includes VIRAL_FACTORS now too
-            const fastFrames = mediaData.frames.slice(0, 3);
-            const aiPrompt = `Analyze transcript and frames.\nTRANSCRIPT:\n${mediaData.transcript}\n\nFormat exactly:\nTRANSCRIPT:[dialogue]\nENVIRONMENT:[visuals]\nPACING:[style]\nVIRAL_FACTORS:[Identify psychological hooks and word choices]`;
-            aiResult = await generateCompletion({ model: "maverick", prompt: aiPrompt, images: fastFrames, system_prompt: "Fast metadata extractor." });
+            const fastFrames = mediaData.frames.slice(0, 7);
+            const aiPrompt = `Analyze transcript and frames.\nTRANSCRIPT:\n${mediaData.transcript}\n
+Return ONLY a JSON object with these exact keys:
+{
+  "transcript": "Cleaned dialogue",
+  "environment": "Setting/lighting description",
+  "pacing": "Edit style and camera movement",
+  "viral_factors": "Identify psychological hooks and word choices"
+}`;
+            aiResult = await generateCompletion({ model: "maverick", prompt: aiPrompt, images: fastFrames, system_prompt: "Fast metadata extractor. Output strictly JSON." });
         }
 
-        // Parse 4 sections now
-        const transcriptMatch = aiResult.data.match(/TRANSCRIPT:\s*(.*?)(?=ENVIRONMENT:)/is);
-        const envMatch = aiResult.data.match(/ENVIRONMENT:\s*(.*?)(?=PACING:)/is);
-        const pacingMatch = aiResult.data.match(/PACING:\s*(.*?)(?=VIRAL_FACTORS:)/is);
-        const auraMatch = aiResult.data.match(/VIRAL_FACTORS:\s*(.*)/is);
+        // 🚨 CLEAN JSON PARSING (No more Regex!)
+        const parsedData = JSON.parse(aiResult.data);
 
         const { error } = await supabase.from('viral_cat_trending').insert([{
             platform, video_url, title,
             thumbnail_url: `data:image/jpeg;base64,${mediaData.thumbnail}`,
-            transcript: transcriptMatch ? transcriptMatch[1].trim() : mediaData.transcript,
-            ai_environment_data: envMatch ? envMatch[1].trim() : "N/A",
-            ai_scene_changes: pacingMatch ? pacingMatch[1].trim() : "N/A",
-            ai_viral_factors: auraMatch ? auraMatch[1].trim() : "N/A" // 🚨 Saving the Aura!
+            transcript: parsedData.transcript || mediaData.transcript,
+            ai_environment_data: parsedData.environment || "N/A",
+            ai_scene_changes: parsedData.pacing || "N/A",
+            ai_viral_factors: parsedData.viral_factors || "N/A"
         }]);
 
         if (error) throw error;
@@ -185,39 +195,31 @@ router.post('/remix', async (req, res) => {
     try {
         const { user_input, transcript, ai_environment_data, ai_scene_changes, ai_viral_factors } = req.body;
         
-        // Feed Remix also gets the Aura upgrade
         const aiPrompt = `TASK: Rewrite script for a NEW NICHE.
 [ORIGINAL DATA]
 Pacing: ${ai_scene_changes}
 Environment: ${ai_environment_data}
 Viral Psychology (The Aura): ${ai_viral_factors}
-Transcript:
-${transcript}[NEW NICHE]
+Transcript: ${transcript}
+[NEW NICHE]
 "${user_input}"
 
 INSTRUCTIONS: Map the original viral psychology and pacing onto the user's new niche.
-Format in Markdown:
-## 🧠 The Viral Aura
-[Explain the psychology you used]
-## 🎬 The Hook
-**Visual:**
-**Audio:**
-## 📖 The Body
-**Visual:**
-**Audio:**
-## 💥 The Call to Action
-**Visual:**
-**Audio:**`;
-
-        const result = await generateCompletion({ model: "maverick", prompt: aiPrompt, system_prompt: "You are Viral Cat 📈🐈. Adapt viral formats to new niches." });
-        res.json(result);
+Return ONLY a JSON object with a single key "script_markdown" containing the fully formatted markdown script.`;
+
+        const result = await generateCompletion({ model: "maverick", prompt: aiPrompt, system_prompt: "You are Viral Cat 📈🐈. Output strictly JSON." });
+        
+        // Extract the markdown string from the JSON response
+        const finalMarkdown = JSON.parse(result.data).script_markdown;
+        res.json({ success: true, data: finalMarkdown });
+        
     } catch (err) { res.status(500).json({ success: false, error: err.message }); }
 });
 
 router.post('/custom_remix', upload.single('video_file'), async (req, res) => {
     try {
         const { user_niche } = req.body;
-        const mediaData = await handleMediaProcessing(req, 15, true);
+        const mediaData = await handleMediaProcessing(req, 14, true);
         const result = await performDeepAnalysis(mediaData.frames, mediaData.transcript, user_niche);
         
         res.json({ success: true, data: result.data, thumbnail: mediaData.thumbnail });
@@ -232,7 +234,7 @@ router.delete('/admin/template/:id', async (req, res) => {
     res.json({ success: true });
 });
 
-// Admin Dashboard HTML
+// Admin Dashboard HTML (Unchanged)
 router.get('/admin', (req, res) => {
     res.send(`<!DOCTYPE html><html><head><title>Viral Cat Admin</title><script src="https://cdn.tailwindcss.com"></script></head><body class="bg-[#090A0F] text-white p-8"><h1 class="text-3xl font-bold text-[#12D8C3] mb-8">Viral Cat Admin</h1><div class="grid grid-cols-2 gap-8"><form id="f" class="space-y-4 bg-[#16181F] p-6 rounded-2xl"><input name="title" placeholder="Title" class="w-full p-2 bg-black border border-gray-700 rounded" required/><input name="video_url" placeholder="Embed URL" class="w-full p-2 bg-black border border-gray-700 rounded" required/><select name="platform" class="w-full p-2 bg-black border border-gray-700 rounded"><option value="tiktok">TikTok</option><option value="instagram">Instagram</option><option value="youtube">YouTube</option></select><div class="flex items-center gap-2 mt-2 mb-2"><input type="checkbox" id="deep" name="use_deep_analysis" value="true" checked class="w-5 h-5 accent-[#12D8C3]"><label for="deep" class="text-gray-300 text-sm">Use Map-Reduce Deep Analysis (Slower but 10x more accurate)</label></div><input type="file" id="video_file" accept="video/mp4" class="w-full" required/><button type="submit" id="submitBtn" class="w-full bg-[#12D8C3] text-black p-3 rounded font-bold">Process Video</button></form><div id="list" class="space-y-2"></div></div><script>const toB64 = f => new Promise((res,rej)=>{const r=new FileReader();r.readAsDataURL(f);r.onload=()=>res(r.result.split(',')[1]);});async function load(){const r=await fetch('/api/viralcat/trending');const j=await r.json();document.getElementById('list').innerHTML=j.data.map(t=>\`<div class="p-4 bg-gray-900 flex justify-between rounded-lg"><div><span class="text-xs bg-[#12D8C3] text-black px-2 py-1 rounded font-bold">\${t.platform.toUpperCase()}</span><p class="text-sm mt-1 font-bold">\${t.title}</p></div><button onclick="del('\${t.id}')" class="text-red-500">Delete</button></div>\`).join('');} async function del(id){await fetch('/api/viralcat/admin/template/'+id,{method:'DELETE'});load();} document.getElementById('f').onsubmit=async e=>{e.preventDefault();const b=e.target.querySelector('button'); const isDeep = document.getElementById('deep').checked; b.innerText = isDeep ? "Running Deep Map-Reduce... (Wait ~30s)" : "Processing... (Wait ~10s)"; b.disabled = true; try { const b64 = await toB64(document.getElementById('video_file').files[0]); const payload = { title: e.target.title.value, video_url: e.target.video_url.value, platform: e.target.platform.value, use_deep_analysis: isDeep, video_base64: b64 }; const res = await fetch('/api/viralcat/admin/template', { method: 'POST', headers: {'Content-Type':'application/json'}, body: JSON.stringify(payload) }); if(res.ok) { alert("Done!"); load(); } else { alert("Error: " + await res.text()); } } catch(err) { alert(err.message); } b.innerText="Process Video"; b.disabled=false;}; load();</script></body></html>`);
 });