Update server.js

server.js

@@ -1,225 +1,160 @@
 const express = require('express');
 const cors = require('cors');
-const axios = require('axios');
-const fs = require('fs').promises;
-const path = require('path');
+const http = require('http');
 require('dotenv').config();
 
 const app = express();
-
-// Security middleware
-app.use(cors({
-  origin: process.env.ALLOWED_ORIGINS?.split(',') || ['http://localhost', 'http://127.0.0.1', 'null'],
-  credentials: true
-}));
-
+app.use(cors());
 app.use(express.json({ limit: '50mb' }));
-app.use(express.static('public'));
 
 const PORT = process.env.PORT || 5000;
 
-// Tera V2 Configuration
-const AI_CONFIG = {
-  url: process.env.AI_MODEL_URL || 'http://localhost:11434/api/generate',
-  model: process.env.AI_MODEL_NAME || 'qwen3.5:0.8b',
-  displayName: process.env.DISPLAY_NAME || 'Tera V2'
-};
+// Ollama Configuration
+const OLLAMA_URL = process.env.OLLAMA_URL || 'http://localhost:11434';
+const MODEL = process.env.MODEL || 'qwen3.5:0.8b';
 
-// Tera V2 System Prompt - Customizes the AI behavior
-const TERAV2_PROMPT = `You are Tera V2, an advanced AI document intelligence assistant created by Tera Labs.
+// Tera V2 System Prompt
+const SYSTEM_PROMPT = `You are Tera V2, an advanced AI document intelligence assistant.
 
 Your capabilities:
 - Analyze complex documents, contracts, and research papers
-- Extract key insights and summarize content
+- Extract key insights and summarize content  
 - Answer questions based on provided documents
 - Generate podcast scripts and audio content
-- Support multiple document formats (PDF, TXT, MD, images)
 
 Response Style:
 - Professional yet approachable
 - Clear and concise
-- Always cite specific parts of documents when answering
-- Format responses for readability (bullet points, sections)
-- If information isn't in the documents, say so clearly
-
-When analyzing documents:
-1. Identify the document type and purpose
-2. Extract key information and data points
-3. Highlight important clauses or findings
-4. Flag any potential issues or concerns
-5. Provide actionable recommendations
-
-Remember: You are Tera V2. You are helpful, accurate, and efficient.`;
-
-// Dataset collection for future training
-const DATASET_PATH = path.join(__dirname, 'dataset');
-const LOGS_PATH = path.join(__dirname, 'logs');
-
-// Ensure directories exist
-async function ensureDirectories() {
-  try {
-    await fs.mkdir(DATASET_PATH, { recursive: true });
-    await fs.mkdir(LOGS_PATH, { recursive: true });
-  } catch (e) {}
-}
-ensureDirectories();
-
-// Logging function
-async function logConversation(data) {
-  try {
-    const date = new Date().toISOString().split('T')[0];
-    const logFile = path.join(LOGS_PATH, `conversations-${date}.jsonl`);
-    await fs.appendFile(logFile, JSON.stringify(data) + '\n');
-  } catch (e) {}
-}
+- Cite specific parts of documents when answering
+- Format responses for readability`;
 
 console.log('╔════════════════════════════════════════╗');
-console.log('║         Tera V2 - Production           ║');
+console.log('║         Tera V2 Starting...            ║');
 console.log('╚════════════════════════════════════════╝');
-console.log(`Model: ${AI_CONFIG.displayName} (${AI_CONFIG.model})`);
-console.log(`Status: Initializing...\n`);
+console.log(`Model: ${MODEL}`);
+console.log(`Ollama: ${OLLAMA_URL}\n`);
 
 // Health check
 app.get('/health', async (req, res) => {
   try {
-    const response = await axios.get('http://localhost:11434/api/tags', { timeout: 5000 });
-    const models = response.data.models || [];
-    const hasModel = models.some(m => m.name.includes(AI_CONFIG.model));
+    const request = http.get(`${OLLAMA_URL}/api/tags`, (response) => {
+      let data = '';
+      response.on('data', chunk => data += chunk);
+      response.on('end', () => {
+        try {
+          const models = JSON.parse(data).models || [];
+          const hasModel = models.some(m => m.name.includes(MODEL));
+          res.json({ 
+            status: 'ok', 
+            service: 'Tera V2',
+            model: MODEL,
+            modelLoaded: hasModel
+          });
+        } catch (e) {
+          res.status(503).json({ status: 'error', message: 'Ollama not responding' });
+        }
+      });
+    });
     
-    res.json({ 
-      status: 'ok', 
-      service: 'Tera V2 Backend',
-      version: '2.0.0',
-      provider: 'Ollama (Local)',
-      model: AI_CONFIG.model,
-      displayName: AI_CONFIG.displayName,
-      modelLoaded: hasModel,
-      mode: 'production'
+    request.on('error', () => {
+      res.status(503).json({ 
+        status: 'error', 
+        message: 'Ollama not running',
+        solution: `Run: ollama run ${MODEL}`
+      });
     });
-  } catch (error) {
-    res.status(503).json({ 
-      status: 'error', 
-      message: 'Ollama not running',
-      solution: `Run: ollama run ${AI_CONFIG.model}`
+    
+    request.setTimeout(5000, () => {
+      request.destroy();
+      res.status(503).json({ status: 'error', message: 'Ollama timeout' });
     });
+    
+  } catch (error) {
+    res.status(503).json({ status: 'error', message: error.message });
   }
 });
 
-// Get Tera V2 info
-app.get('/api/info', (req, res) => {
-  res.json({
-    name: 'Tera V2',
-    version: '2.0.0',
-    model: AI_CONFIG.model,
-    capabilities: ['document-analysis', 'chat', 'audio-generation', 'multimodal']
-  });
-});
-
 // Chat endpoint with streaming
 app.post('/api/chat', async (req, res) => {
-  const startTime = Date.now();
-  
   try {
-    const { messages, sources, docId } = req.body;
+    const { messages, sources } = req.body;
     
     // Build context from sources
     let context = '';
     if (sources && sources.length > 0) {
       context = '\n\nDOCUMENTS:\n' + sources.map((s, i) => 
-        `[${i + 1}] ${s.name}:\n${s.content?.substring(0, 3000) || '[Image content]'}`
+        `[${i + 1}] ${s.name}:\n${s.content?.substring(0, 3000) || '[Image]'}`
       ).join('\n\n');
     }
 
     const lastMessage = messages[messages.length - 1];
-    const prompt = `${TERAV2_PROMPT}${context}\n\nUser: ${lastMessage.content}\n\nTera V2:`;
-
-    // Log for dataset collection
-    logConversation({
-      timestamp: new Date().toISOString(),
-      type: 'chat',
-      docId,
-      hasSources: sources?.length > 0,
-      query: lastMessage.content
-    });
+    const prompt = `${SYSTEM_PROMPT}${context}\n\nUser: ${lastMessage.content}\n\nTera V2:`;
 
     res.setHeader('Content-Type', 'text/event-stream');
     res.setHeader('Cache-Control', 'no-cache');
-    res.setHeader('Connection', 'keep-alive');
 
-    const response = await axios.post(
-      AI_CONFIG.url,
-      {
-        model: AI_CONFIG.model,
-        prompt: prompt,
-        stream: true,
-        options: {
-          temperature: 0.7,
-          num_predict: 4096,
-          top_p: 0.9
-        }
-      },
-      {
-        responseType: 'stream',
-        timeout: 120000
-      }
-    );
+    const requestData = JSON.stringify({
+      model: MODEL,
+      prompt: prompt,
+      stream: true,
+      options: { temperature: 0.7, num_predict: 4096 }
+    });
 
-    let fullResponse = '';
+    const options = {
+      hostname: 'localhost',
+      port: 11434,
+      path: '/api/generate',
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+        'Content-Length': Buffer.byteLength(requestData)
+      },
+      timeout: 120000
+    };
 
-    response.data.on('data', (chunk) => {
-      const lines = chunk.toString().split('\n');
-      for (const line of lines) {
-        if (line.trim()) {
-          try {
-            const data = JSON.parse(line);
-            if (data.response) {
-              fullResponse += data.response;
-              res.write(`data: {"content": ${JSON.stringify(data.response)}}\n\n`);
-            }
-            if (data.done) {
-              const duration = Date.now() - startTime;
-              logConversation({
-                timestamp: new Date().toISOString(),
-                type: 'response',
-                docId,
-                responseLength: fullResponse.length,
-                duration
-              });
-              res.write('data: {"done": true}\n\n');
-            }
-          } catch (e) {}
+    const proxyReq = http.request(options, (proxyRes) => {
+      proxyRes.on('data', (chunk) => {
+        const lines = chunk.toString().split('\n');
+        for (const line of lines) {
+          if (line.trim()) {
+            try {
+              const data = JSON.parse(line);
+              if (data.response) {
+                res.write(`data: {"content": ${JSON.stringify(data.response)}}\n\n`);
+              }
+              if (data.done) {
+                res.write('data: {"done": true}\n\n');
+              }
+            } catch (e) {}
+          }
         }
-      }
-    });
-
-    response.data.on('end', () => {
-      res.write('data: {"done": true}\n\n');
-      res.end();
+      });
+      
+      proxyRes.on('end', () => {
+        res.write('data: {"done": true}\n\n');
+        res.end();
+      });
     });
 
-    response.data.on('error', (error) => {
-      console.error('Stream error:', error);
+    proxyReq.on('error', (error) => {
+      console.error('Ollama error:', error.message);
       res.write(`data: {"error": "${error.message}"}\n\n`);
       res.end();
     });
 
+    proxyReq.write(requestData);
+    proxyReq.end();
+
   } catch (error) {
     console.error('Chat error:', error.message);
-    if (error.code === 'ECONNREFUSED') {
-      res.status(503).json({ 
-        error: 'Tera V2 AI is not running',
-        solution: `Run: ollama run ${AI_CONFIG.model}`
-      });
-    } else {
-      res.status(500).json({ error: error.message });
-    }
+    res.status(500).json({ error: error.message });
   }
 });
 
 // Audio script generation
 app.post('/api/generate-audio-script', async (req, res) => {
   try {
-    const { sources, format, instructions, docId } = req.body;
+    const { sources, format, instructions } = req.body;
     
     let content = '';
     if (sources && sources.length > 0) {
@@ -227,146 +162,68 @@ app.post('/api/generate-audio-script', async (req, res) => {
     }
 
     const formatPrompts = {
-      deepdive: 'Create an in-depth podcast discussion between two hosts (Host 1: Alex, Host 2: Sam) analyzing the content. Include different perspectives and detailed insights.',
+      deepdive: 'Create an in-depth podcast discussion between two hosts analyzing the content.',
       briefing: 'Create a brief news-style summary of the key points.',
-      story: 'Create a narrative-driven exploration of the content, telling it as an engaging story.'
+      story: 'Create a narrative-driven exploration of the content.'
     };
 
-    const customInstr = instructions ? ` Additional instructions: ${instructions}` : '';
-    
-    const prompt = `${TERAV2_PROMPT}
-
-You are creating a podcast script. ${formatPrompts[format] || formatPrompts.deepdive}${customInstr}
+    const customInstr = instructions ? ` ${instructions}` : '';
+    const prompt = `${SYSTEM_PROMPT}\n\nYou are creating a podcast script. ${formatPrompts[format] || formatPrompts.deepdive}${customInstr}\n\nDocuments:\n${content}\n\nScript:`;
 
-Format each speaker line as "Host 1: text" or "Host 2: text".
-
-Documents to analyze:
-${content}
-
-Generate a compelling podcast script:`;
-
-    // Log
-    logConversation({
-      timestamp: new Date().toISOString(),
-      type: 'audio-generation',
-      docId,
-      format,
-      hasCustomInstructions: !!instructions
+    const requestData = JSON.stringify({
+      model: MODEL,
+      prompt: prompt,
+      stream: false,
+      options: { temperature: 0.8, num_predict: 4096 }
     });
 
-    const response = await axios.post(
-      AI_CONFIG.url,
-      {
-        model: AI_CONFIG.model,
-        prompt: prompt,
-        stream: false,
-        options: {
-          temperature: 0.8,
-          num_predict: 4096
-        }
-      },
-      { timeout: 120000 }
-    );
+    const options = {
+      hostname: 'localhost',
+      port: 11434,
+      path: '/api/generate',
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+        'Content-Length': Buffer.byteLength(requestData)
+      }
+    };
 
-    res.json({
-      script: response.data.response,
-      model: AI_CONFIG.displayName,
-      success: true
+    const proxyReq = http.request(options, (proxyRes) => {
+      let data = '';
+      proxyRes.on('data', chunk => data += chunk);
+      proxyRes.on('end', () => {
+        try {
+          const response = JSON.parse(data);
+          res.json({ script: response.response, success: true });
+        } catch (e) {
+          res.status(500).json({ error: 'Invalid response from Ollama' });
+        }
+      });
     });
 
-  } catch (error) {
-    console.error('Audio script error:', error.message);
-    res.status(500).json({ error: error.message });
-  }
-});
+    proxyReq.on('error', (error) => {
+      res.status(500).json({ error: error.message });
+    });
 
-// Save training data endpoint (for future fine-tuning)
-app.post('/api/save-training-data', async (req, res) => {
-  try {
-    const { prompt, response, quality } = req.body;
-    
-    const trainingExample = {
-      instruction: prompt,
-      input: "",
-      output: response,
-      quality: quality || 'good',
-      timestamp: new Date().toISOString()
-    };
+    proxyReq.write(requestData);
+    proxyReq.end();
 
-    const date = new Date().toISOString().split('T')[0];
-    const file = path.join(DATASET_PATH, `training-${date}.jsonl`);
-    
-    await fs.appendFile(file, JSON.stringify(trainingExample) + '\n');
-    
-    res.json({ success: true });
   } catch (error) {
     res.status(500).json({ error: error.message });
   }
 });
 
-// Export dataset
-app.get('/api/dataset', async (req, res) => {
-  try {
-    const files = await fs.readdir(DATASET_PATH);
-    const allData = [];
-    
-    for (const file of files) {
-      if (file.endsWith('.jsonl')) {
-        const content = await fs.readFile(path.join(DATASET_PATH, file), 'utf-8');
-        const lines = content.trim().split('\n');
-        lines.forEach(line => {
-          try {
-            allData.push(JSON.parse(line));
-          } catch (e) {}
-        });
-      }
-    }
-    
-    res.json({ 
-      count: allData.length,
-      data: allData
-    });
-  } catch (error) {
-    res.status(500).json({ error: error.message });
-  }
-});
-
-// List available Ollama models
-app.get('/api/models', async (req, res) => {
-  try {
-    const response = await axios.get('http://localhost:11434/api/tags');
-    res.json(response.data);
-  } catch (error) {
-    res.status(503).json({ error: 'Ollama not running' });
-  }
-});
-
-// Error handling
-app.use((err, req, res, next) => {
-  console.error(err.stack);
-  res.status(500).json({ error: 'Internal server error' });
-});
-
 app.listen(PORT, () => {
   console.log('╔════════════════════════════════════════╗');
-  console.log('║     Tera V2 is Ready! 🚀               ║');
+  console.log('║     Tera V2 is Ready!                  ║');
   console.log('╚════════════════════════════════════════╝');
   console.log(`Server: http://localhost:${PORT}`);
-  console.log(`AI Model: ${AI_CONFIG.displayName}`);
-  console.log(`Status: Production Ready`);
-  console.log('');
-  console.log('Features:');
-  console.log('  ✅ Document Analysis');
-  console.log('  ✅ Audio Generation');
-  console.log('  ✅ Dataset Collection (for training)');
-  console.log('  ✅ 100% Offline (No API keys)');
+  console.log(`Model: ${MODEL}`);
   console.log('');
-  console.log('To use Tera V2:');
-  console.log(`1. Keep Ollama running: ollama run ${AI_CONFIG.model}`);
-  console.log('2. Open tera-docs.html in your browser');
+  console.log('Quick Start:');
+  console.log(`1. Keep Ollama running: ollama run ${MODEL}`);
+  console.log('2. Open tera-docs.html in browser');
   console.log('');
-  console.log('Legal: Qwen license allows full commercial use & rebranding');
+  console.log('No API keys - runs locally!');
   console.log('╔════════════════════════════════════════╗\n');
-});
-
-module.exports = app;
+});