👁️ Give Your AI Super Vision!

Your AI can chat, create images, understand speech, analyze files, and talk back. Now let’s give it eyes! 👀

Imagine users uploading a business chart and your AI saying: “This shows a 23% increase in Q3 sales, with the highest growth in the mobile segment.” Or analyzing a screenshot and providing detailed UI/UX feedback!

What we’re building: Your AI will become a visual expert that can analyze photos, documents, charts, screenshots - anything visual - with professional-level insights!

Current state: Your AI can process text, but images are a mystery Target state: Your AI sees and understands any visual content!

🔄 The Visual Intelligence Transformation

Before (Blind AI):

User: [Uploads business chart] "What does this show?"
AI: "I can't see images, please describe it" 😕

After (AI with Vision):

User: [Uploads business chart] "What does this show?"
AI: "This bar chart shows quarterly revenue growth, with Q3 showing a 34% increase over Q2. The mobile division is your strongest performer with $2.3M in sales." 🤩

The magic: Your AI becomes a visual expert that understands images like a human!

🚀 Why Vision Makes Your App Incredible

Real-world scenarios your AI will handle:

📈 Business charts - “Revenue increased 23% with mobile leading growth”
📝 Documents - Extract key data, dates, and important information
📱 Screenshots - “The login button should be bigger and more prominent”
🎨 Photos - “This shows a golden retriever in a park with 3 people”
📊 Dashboards - “Your conversion rate dropped 5% but user engagement is up”

Without vision AI:

❌ Manually examine every image
❌ Miss important visual patterns
❌ Time-consuming data extraction
❌ Limited to text-only analysis

With vision AI:

✅ Instant professional image analysis
✅ Extract data with perfect accuracy
✅ Spot patterns humans might miss
✅ Complete multimedia intelligence

🕰️ Your AI’s New Visual Superpowers

📄 Document Detective Mode

Perfect for: Invoices, contracts, forms, reports
AI becomes: Professional document analyst
Results: "Invoice #12345 dated March 15th for $2,847.50 from TechCorp"

📊 Chart Analyst Mode

Perfect for: Graphs, dashboards, data visualizations
AI becomes: Business intelligence expert
Results: "Sales peaked in Q3 at $1.2M, showing 45% growth over Q2"

🎯 Everything Mode

Perfect for: Photos, screenshots, anything visual
AI becomes: Universal visual expert
Results: "This UI mockup has good spacing but the CTA button needs more contrast"

The best part: One AI handles all types perfectly!

🛠️ Step 1: Add Super Vision to Your Backend

Great news: We’re using your proven Response API patterns!

What you already know:

// Your familiar text analysis
const response = await client.responses.create({
  model: "gpt-4o",
  input: [expertPrompt, userMessage]
});

What we’re adding:

// Same pattern + image input!
const response = await client.responses.create({
  model: "gpt-4o",  // Same model, now with vision!
  input: [
    expertPrompt,
    {
      role: "user",
      content: [
        { type: "text", text: "Analyze this image" },
        { type: "image_url", image_url: uploadedImage }
      ]
    }
  ]
});

Perfect! Same Response API, just with image superpowers added.

🧠 Understanding Vision Analysis Flow

Simple concept: Image goes in → Expert analysis comes out!

// What we need to track:
const visionState = {
  uploadedImage: "user-screenshot.png",          // What to analyze
  analysisMode: "general",                       // How to analyze it
  visionSettings: {                              // Analysis options
    includeOCR: true,      // Extract text
    extractData: true,     // Find numbers/dates
    detailLevel: "high"    // Depth of analysis
  },
  aiResults: "Professional analysis...",        // Expert insights!
}

Vision analysis types:

📝 Document mode - Focus on text extraction and data
📊 Chart mode - Analyze data visualizations and trends
🎯 General mode - Comprehensive understanding of anything
🔍 Detail levels - From quick summaries to deep analysis

Step 2: Quick Setup (30 seconds)

Add one package for image optimization:

# In your backend folder
npm install sharp

What sharp does: Makes images perfect for AI analysis - faster processing and better results!

Step 3: Add the Vision Analysis Route

Add this to your server - same reliable patterns:

import sharp from 'sharp';

// 👁️ VISION ANALYSIS ENDPOINT: Add this to your existing server
app.post("/api/vision/analyze", upload.single("image"), async (req, res) => {
  try {
    // 🛡️ VALIDATION: Check if image was uploaded
    const uploadedImage = req.file;
    const { analysisType = "general", includeOCR = true, extractData = true } = req.body;

    if (!uploadedImage) {
      return res.status(400).json({
        error: "Image file is required",
        success: false
      });
    }

    console.log(`👁️ Analyzing: ${uploadedImage.originalname} (${uploadedImage.size} bytes)`);

    // 🖼️ IMAGE OPTIMIZATION: Prepare image for vision analysis
    const optimizedImage = await optimizeImageForVision(uploadedImage.buffer);
    const base64Image = optimizedImage.toString('base64');
    const imageUrl = `data:${uploadedImage.mimetype};base64,${base64Image}`;

    // 🔍 ANALYSIS PROMPT: Generate appropriate prompt based on type
    const analysisPrompt = generateVisionPrompt(analysisType, includeOCR, extractData);

    // 🤖 AI VISION ANALYSIS: Process with GPT-4o
    const response = await openai.responses.create({
      model: "gpt-4o",
      input: [
        {
          role: "system",
          content: analysisPrompt.systemPrompt
        },
        {
          role: "user",
          content: [
            {
              type: "text",
              text: analysisPrompt.userPrompt
            },
            {
              type: "image_url",
              image_url: {
                url: imageUrl,
                detail: "high"
              }
            }
          ]
        }
      ]
    });

    // 📤 SUCCESS RESPONSE: Send analysis results
    res.json({
      success: true,
      file_info: {
        name: uploadedImage.originalname,
        size: uploadedImage.size,
        type: uploadedImage.mimetype
      },
      analysis: {
        type: analysisType,
        include_ocr: includeOCR,
        extract_data: extractData,
        result: response.output_text,
        model: "gpt-4o"
      },
      timestamp: new Date().toISOString()
    });

  } catch (error) {
    // 🚨 ERROR HANDLING: Handle analysis failures
    console.error("Vision analysis error:", error);

    res.status(500).json({
      error: "Failed to analyze image",
      details: error.message,
      success: false
    });
  }
});

// 🔧 HELPER FUNCTIONS: Vision analysis utilities

// Optimize image for better vision analysis
const optimizeImageForVision = async (imageBuffer) => {
  try {
    // Resize large images for better processing
    const optimized = await sharp(imageBuffer)
      .resize(2048, 2048, {
        fit: 'inside',
        withoutEnlargement: true
      })
      .jpeg({ quality: 85 })
      .toBuffer();

    return optimized;
  } catch (error) {
    console.error('Image optimization error:', error);
    return imageBuffer; // Return original if optimization fails
  }
};

// Generate analysis prompts based on type
const generateVisionPrompt = (analysisType, includeOCR, extractData) => {
  const baseSystem = "You are a professional visual analyst with expertise in document analysis, data extraction, and image understanding.";

  switch (analysisType) {
    case 'document':
      return {
        systemPrompt: `${baseSystem} You specialize in document analysis, OCR, and text extraction.`,
        userPrompt: `Analyze this document image with focus on:
        1. TEXT EXTRACTION: ${includeOCR ? 'Extract all readable text content using OCR' : 'Summarize visible text content'}
        2. DOCUMENT STRUCTURE: Identify document type, layout, and organization
        3. KEY DATA: Extract important numbers, dates, names, and values
        4. INSIGHTS: Provide analysis of the document's purpose and key information

        Provide clear, structured analysis that's easy to understand.`
      };

    case 'chart':
      return {
        systemPrompt: `${baseSystem} You specialize in chart analysis, data visualization interpretation, and trend analysis.`,
        userPrompt: `Analyze this chart/graph with focus on:
        1. CHART TYPE: Identify the type of visualization (bar, line, pie, etc.)
        2. DATA EXTRACTION: ${extractData ? 'Extract specific numerical values and data points' : 'Summarize key trends and patterns'}
        3. TRENDS: Identify patterns, trends, and significant changes
        4. INSIGHTS: Provide business intelligence and actionable insights

        Focus on accuracy and clear interpretation of the visual data.`
      };

    default: // general
      return {
        systemPrompt: `${baseSystem} You provide comprehensive visual analysis for any type of image.`,
        userPrompt: `Analyze this image comprehensively:
        1. CONTENT DESCRIPTION: What do you see in this image?
        2. KEY ELEMENTS: Important objects, text, or data visible
        3. CONTEXT ANALYSIS: Purpose, setting, or business context
        4. ACTIONABLE INSIGHTS: Useful observations or recommendations

        ${includeOCR ? 'Include any readable text content.' : ''}
        ${extractData ? 'Extract any numerical or structured data visible.' : ''}

        Provide practical, useful analysis that helps users understand the image better.`
      };
  }
};

Function breakdown:

Validation - Ensure we have an image to analyze
Image optimization - Prepare image for better AI analysis
Prompt generation - Create appropriate analysis prompts
Vision analysis - Process with GPT-4o vision capabilities
Response formatting - Return structured results with metadata

Step 2D: Updating File Upload Configuration

Update your existing multer configuration to handle images:

// Update your existing multer setup to handle images
const upload = multer({
  storage: multer.memoryStorage(),
  limits: {
    fileSize: 25 * 1024 * 1024 // 25MB limit
  },
  fileFilter: (req, file, cb) => {
    // Accept all previous file types PLUS images
    const allowedTypes = [
      'application/pdf',
      'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
      'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
      'text/plain',
      'text/csv',
      'application/json',
      'text/javascript',
      'text/x-python',
      'audio/wav',
      'audio/mp3',
      'audio/mpeg',
      'audio/mp4',
      'audio/webm',
      'image/jpeg',        // Add image support
      'image/png',         // Add image support
      'image/webp',        // Add image support
      'image/gif'          // Add image support
    ];

    const extension = path.extname(file.originalname).toLowerCase();
    const allowedExtensions = ['.pdf', '.docx', '.xlsx', '.csv', '.txt', '.md', '.json', '.js', '.py', '.wav', '.mp3', '.jpeg', '.jpg', '.png', '.webp', '.gif'];

    if (allowedTypes.includes(file.mimetype) || allowedExtensions.includes(extension)) {
      cb(null, true);
    } else {
      cb(new Error('Unsupported file type'), false);
    }
  }
});

Your backend now supports:

Text chat (existing functionality)
Streaming chat (existing functionality)
Image generation (existing functionality)
Audio transcription (existing functionality)
File analysis (existing functionality)
Text-to-speech (existing functionality)
Vision analysis (new functionality)

🔧 Step 3: Building the React Vision Component

Now let’s create a React component for vision analysis using the same patterns from your existing components.

Step 3A: Creating the Vision Analysis Component

Create a new file src/VisionAnalysis.jsx:

import { useState, useRef } from "react";
import { Upload, Eye, FileText, BarChart3, Download, Camera } from "lucide-react";

function VisionAnalysis() {
  // 🧠 STATE: Vision analysis data management
  const [selectedImage, setSelectedImage] = useState(null);        // Uploaded image
  const [analysisType, setAnalysisType] = useState("general");     // Analysis mode
  const [isAnalyzing, setIsAnalyzing] = useState(false);           // Processing status
  const [analysisResult, setAnalysisResult] = useState(null);      // Analysis results
  const [error, setError] = useState(null);                        // Error messages
  const [previewUrl, setPreviewUrl] = useState(null);             // Image preview
  const [options, setOptions] = useState({                         // Analysis options
    includeOCR: true,
    extractData: true
  });
  const fileInputRef = useRef(null);

  // 🔧 FUNCTIONS: Vision analysis logic engine

  // Handle image selection
  const handleImageSelect = (event) => {
    const file = event.target.files[0];
    if (file) {
      // Validate file size (25MB limit)
      if (file.size > 25 * 1024 * 1024) {
        setError('Image too large. Maximum size is 25MB.');
        return;
      }

      // Validate file type
      const allowedTypes = ['image/jpeg', 'image/png', 'image/webp', 'image/gif'];
      if (!allowedTypes.includes(file.type)) {
        setError('Unsupported image type. Please upload JPEG, PNG, WebP, or GIF files.');
        return;
      }

      setSelectedImage(file);
      setAnalysisResult(null);
      setError(null);

      // Create preview URL
      const url = URL.createObjectURL(file);
      setPreviewUrl(url);
    }
  };

  // Clear selected image
  const clearImage = () => {
    setSelectedImage(null);
    setAnalysisResult(null);
    setError(null);
    if (previewUrl) {
      URL.revokeObjectURL(previewUrl);
      setPreviewUrl(null);
    }
    if (fileInputRef.current) {
      fileInputRef.current.value = '';
    }
  };

  // Main vision analysis function
  const analyzeImage = async () => {
    // 🛡️ GUARDS: Prevent invalid analysis
    if (!selectedImage || isAnalyzing) return;

    // 🔄 SETUP: Prepare for analysis
    setIsAnalyzing(true);
    setError(null);
    setAnalysisResult(null);

    try {
      // 📤 FORM DATA: Prepare multipart form data
      const formData = new FormData();
      formData.append('image', selectedImage);
      formData.append('analysisType', analysisType);
      formData.append('includeOCR', options.includeOCR);
      formData.append('extractData', options.extractData);

      // 📡 API CALL: Send to your backend
      const response = await fetch("http://localhost:8000/api/vision/analyze", {
        method: "POST",
        body: formData
      });

      const data = await response.json();

      if (!response.ok) {
        throw new Error(data.error || 'Failed to analyze image');
      }

      // ✅ SUCCESS: Store analysis results
      setAnalysisResult(data);

    } catch (error) {
      // 🚨 ERROR HANDLING: Show user-friendly message
      console.error('Vision analysis failed:', error);
      setError(error.message || 'Something went wrong while analyzing the image');
    } finally {
      // 🧹 CLEANUP: Reset processing state
      setIsAnalyzing(false);
    }
  };

  // Download analysis results
  const downloadAnalysis = () => {
    if (!analysisResult) return;

    const element = document.createElement('a');
    const file = new Blob([JSON.stringify(analysisResult, null, 2)], { type: 'application/json' });
    element.href = URL.createObjectURL(file);
    element.download = `vision-analysis-${selectedImage.name}-${Date.now()}.json`;
    document.body.appendChild(element);
    element.click();
    document.body.removeChild(element);
  };

  // Analysis type options
  const analysisTypes = [
    { value: "general", label: "General Analysis", desc: "Comprehensive visual understanding", icon: Eye },
    { value: "document", label: "Document Analysis", desc: "OCR and text extraction focus", icon: FileText },
    { value: "chart", label: "Chart Analysis", desc: "Data visualization interpretation", icon: BarChart3 }
  ];

  // Format file size
  const formatFileSize = (bytes) => {
    if (bytes === 0) return '0 Bytes';
    const k = 1024;
    const sizes = ['Bytes', 'KB', 'MB'];
    const i = Math.floor(Math.log(bytes) / Math.log(k));
    return parseFloat((bytes / Math.pow(k, i)).toFixed(2)) + ' ' + sizes[i];
  };

  // 🎨 UI: Interface components
  return (
    <div className="min-h-screen bg-gradient-to-br from-indigo-50 to-purple-50 flex items-center justify-center p-4">
      <div className="bg-white rounded-2xl shadow-2xl w-full max-w-6xl flex flex-col overflow-hidden">

        {/* Header */}
        <div className="bg-gradient-to-r from-indigo-600 to-purple-600 text-white p-6">
          <div className="flex items-center space-x-3">
            <div className="w-10 h-10 bg-white bg-opacity-20 rounded-full flex items-center justify-center">
              <Eye className="w-5 h-5" />
            </div>
            <div>
              <h1 className="text-xl font-bold">👁️ AI Vision Analysis</h1>
              <p className="text-indigo-100 text-sm">Analyze any image with AI intelligence!</p>
            </div>
          </div>
        </div>

        {/* Analysis Type Selection */}
        <div className="p-6 border-b border-gray-200">
          <h3 className="font-semibold text-gray-900 mb-4 flex items-center">
            <Camera className="w-5 h-5 mr-2 text-indigo-600" />
            Analysis Type
          </h3>

          <div className="grid grid-cols-1 md:grid-cols-3 gap-4">
            {analysisTypes.map((type) => {
              const IconComponent = type.icon;
              return (
                <button
                  key={type.value}
                  onClick={() => setAnalysisType(type.value)}
                  className={`p-4 rounded-lg border-2 text-left transition-all duration-200 ${
                    analysisType === type.value
                      ? 'border-indigo-500 bg-indigo-50 shadow-md'
                      : 'border-gray-200 hover:border-indigo-300 hover:bg-indigo-50'
                  }`}
                >
                  <div className="flex items-center mb-2">
                    <IconComponent className="w-5 h-5 mr-2 text-indigo-600" />
                    <h4 className="font-medium text-gray-900">{type.label}</h4>
                  </div>
                  <p className="text-sm text-gray-600">{type.desc}</p>
                </button>
              );
            })}
          </div>
        </div>

        {/* Analysis Options */}
        <div className="p-6 border-b border-gray-200">
          <h3 className="font-semibold text-gray-900 mb-4">Analysis Options</h3>

          <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
            <label className="flex items-center space-x-3 p-3 rounded-lg border border-gray-200 hover:bg-gray-50 cursor-pointer">
              <input
                type="checkbox"
                checked={options.includeOCR}
                onChange={(e) => setOptions(prev => ({ ...prev, includeOCR: e.target.checked }))}
                className="w-4 h-4 text-indigo-600 rounded focus:ring-indigo-500"
              />
              <div>
                <span className="font-medium text-gray-900">Include OCR</span>
                <p className="text-sm text-gray-600">Extract text content from images</p>
              </div>
            </label>

            <label className="flex items-center space-x-3 p-3 rounded-lg border border-gray-200 hover:bg-gray-50 cursor-pointer">
              <input
                type="checkbox"
                checked={options.extractData}
                onChange={(e) => setOptions(prev => ({ ...prev, extractData: e.target.checked }))}
                className="w-4 h-4 text-indigo-600 rounded focus:ring-indigo-500"
              />
              <div>
                <span className="font-medium text-gray-900">Extract Data</span>
                <p className="text-sm text-gray-600">Find numerical data and structured information</p>
              </div>
            </label>
          </div>
        </div>

        {/* Image Upload Section */}
        <div className="p-6 border-b border-gray-200">
          <h3 className="font-semibold text-gray-900 mb-4 flex items-center">
            <Upload className="w-5 h-5 mr-2 text-indigo-600" />
            Upload Image for Analysis
          </h3>

          {!selectedImage ? (
            <div
              onClick={() => fileInputRef.current?.click()}
              className="border-2 border-dashed border-gray-300 rounded-xl p-8 text-center cursor-pointer hover:border-indigo-400 hover:bg-indigo-50 transition-colors duration-200"
            >
              <Upload className="w-12 h-12 text-gray-400 mx-auto mb-4" />
              <h4 className="text-lg font-semibold text-gray-700 mb-2">Upload Image</h4>
              <p className="text-gray-600 mb-4">
                Support for JPEG, PNG, WebP, and GIF files up to 25MB
              </p>
              <button className="px-6 py-3 bg-gradient-to-r from-indigo-600 to-purple-600 text-white rounded-xl hover:from-indigo-700 hover:to-purple-700 transition-all duration-200 inline-flex items-center space-x-2 shadow-lg">
                <Upload className="w-4 h-4" />
                <span>Choose Image</span>
              </button>
            </div>
          ) : (
            <div className="bg-gray-50 rounded-lg p-4 border border-gray-200">
              <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
                {/* Image Preview */}
                <div>
                  <h4 className="font-medium text-gray-900 mb-2">Preview:</h4>
                  <img
                    src={previewUrl}
                    alt={selectedImage.name}
                    className="w-full h-48 object-cover rounded-lg border border-gray-200"
                  />
                </div>

                {/* Image Info */}
                <div>
                  <div className="flex items-center justify-between mb-4">
                    <div>
                      <h4 className="font-medium text-gray-900">{selectedImage.name}</h4>
                      <p className="text-sm text-gray-600">{formatFileSize(selectedImage.size)}</p>
                    </div>
                    <button
                      onClick={clearImage}
                      className="p-2 text-gray-400 hover:text-red-600 transition-colors duration-200"
                    >
                      ×
                    </button>
                  </div>

                  <button
                    onClick={analyzeImage}
                    disabled={isAnalyzing}
                    className="w-full bg-gradient-to-r from-indigo-600 to-purple-600 hover:from-indigo-700 hover:to-purple-700 disabled:from-gray-300 disabled:to-gray-300 text-white px-6 py-3 rounded-lg transition-all duration-200 flex items-center justify-center space-x-2 shadow-lg disabled:shadow-none"
                  >
                    {isAnalyzing ? (
                      <>
                        <div className="w-4 h-4 border-2 border-white border-t-transparent rounded-full animate-spin"></div>
                        <span>Analyzing...</span>
                      </>
                    ) : (
                      <>
                        <Eye className="w-4 h-4" />
                        <span>Analyze Image</span>
                      </>
                    )}
                  </button>
                </div>
              </div>
            </div>
          )}

          <input
            ref={fileInputRef}
            type="file"
            accept="image/jpeg,image/png,image/webp,image/gif"
            onChange={handleImageSelect}
            className="hidden"
          />
        </div>

        {/* Results Section */}
        <div className="flex-1 p-6">
          {/* Error Display */}
          {error && (
            <div className="bg-red-50 border border-red-200 rounded-lg p-4 mb-4">
              <p className="text-red-700">
                <strong>Error:</strong> {error}
              </p>
            </div>
          )}

          {/* Analysis Results */}
          {analysisResult ? (
            <div className="bg-gray-50 rounded-lg p-4">
              <div className="flex items-center justify-between mb-4">
                <h4 className="font-semibold text-gray-900">Vision Analysis Results</h4>
                <button
                  onClick={downloadAnalysis}
                  className="bg-gradient-to-r from-blue-500 to-blue-600 hover:from-blue-600 hover:to-blue-700 text-white px-4 py-2 rounded-lg transition-all duration-200 flex items-center space-x-2"
                >
                  <Download className="w-4 h-4" />
                  <span>Download</span>
                </button>
              </div>

              <div className="space-y-4">
                {/* File Information */}
                <div className="bg-white rounded-lg p-4">
                  <h5 className="font-medium text-gray-700 mb-2">Image Information:</h5>
                  <div className="grid grid-cols-2 md:grid-cols-4 gap-4 text-sm">
                    <div>
                      <span className="text-gray-600">Name:</span>
                      <p className="font-medium">{analysisResult.file_info.name}</p>
                    </div>
                    <div>
                      <span className="text-gray-600">Size:</span>
                      <p className="font-medium">{formatFileSize(analysisResult.file_info.size)}</p>
                    </div>
                    <div>
                      <span className="text-gray-600">Type:</span>
                      <p className="font-medium">{analysisResult.file_info.type}</p>
                    </div>
                    <div>
                      <span className="text-gray-600">Analysis:</span>
                      <p className="font-medium capitalize">{analysisResult.analysis.type}</p>
                    </div>
                  </div>
                </div>

                {/* Analysis Content */}
                <div className="bg-white rounded-lg p-4">
                  <h5 className="font-medium text-gray-700 mb-2">AI Vision Analysis:</h5>
                  <div className="text-gray-900 leading-relaxed whitespace-pre-wrap max-h-96 overflow-y-auto">
                    {analysisResult.analysis.result}
                  </div>
                </div>
              </div>
            </div>
          ) : !isAnalyzing && !error && (
            // Welcome State
            <div className="text-center py-12">
              <div className="w-16 h-16 bg-indigo-100 rounded-2xl flex items-center justify-center mx-auto mb-4">
                <Eye className="w-8 h-8 text-indigo-600" />
              </div>
              <h3 className="text-lg font-semibold text-gray-700 mb-2">
                Ready to Analyze!
              </h3>
              <p className="text-gray-600 max-w-md mx-auto">
                Upload any image to get AI-powered visual analysis, text extraction, and intelligent insights.
              </p>
            </div>
          )}
        </div>
      </div>
    </div>
  );
}

export default VisionAnalysis;

Update your src/App.jsx to include the new vision analysis component:

import { useState } from "react";
import StreamingChat from "./StreamingChat";
import ImageGenerator from "./ImageGenerator";
import AudioTranscription from "./AudioTranscription";
import FileAnalysis from "./FileAnalysis";
import TextToSpeech from "./TextToSpeech";
import VisionAnalysis from "./VisionAnalysis";
import { MessageSquare, Image, Mic, Folder, Volume2, Eye } from "lucide-react";

function App() {
  // 🧠 STATE: Navigation management
  const [currentView, setCurrentView] = useState("chat"); // 'chat', 'images', 'audio', 'files', 'speech', or 'vision'

  // 🎨 UI: Main app with navigation
  return (
    <div className="min-h-screen bg-gray-100">
      {/* Navigation Header */}
      <nav className="bg-white shadow-sm border-b border-gray-200">
        <div className="max-w-6xl mx-auto px-4">
          <div className="flex items-center justify-between h-16">
            {/* Logo */}
            <div className="flex items-center space-x-3">
              <div className="w-8 h-8 bg-gradient-to-r from-blue-500 to-purple-600 rounded-lg flex items-center justify-center">
                <span className="text-white font-bold text-sm">AI</span>
              </div>
              <h1 className="text-xl font-bold text-gray-900">OpenAI Mastery</h1>
            </div>

            {/* Navigation Buttons */}
            <div className="flex space-x-2">
              <button
                onClick={() => setCurrentView("chat")}
                className={`px-4 py-2 rounded-lg flex items-center space-x-2 transition-all duration-200 ${
                  currentView === "chat"
                    ? "bg-blue-100 text-blue-700 shadow-sm"
                    : "text-gray-600 hover:text-gray-900 hover:bg-gray-100"
                }`}
              >
                <MessageSquare className="w-4 h-4" />
                <span>Chat</span>
              </button>

              <button
                onClick={() => setCurrentView("images")}
                className={`px-4 py-2 rounded-lg flex items-center space-x-2 transition-all duration-200 ${
                  currentView === "images"
                    ? "bg-purple-100 text-purple-700 shadow-sm"
                    : "text-gray-600 hover:text-gray-900 hover:bg-gray-100"
                }`}
              >
                <Image className="w-4 h-4" />
                <span>Images</span>
              </button>

              <button
                onClick={() => setCurrentView("audio")}
                className={`px-4 py-2 rounded-lg flex items-center space-x-2 transition-all duration-200 ${
                  currentView === "audio"
                    ? "bg-blue-100 text-blue-700 shadow-sm"
                    : "text-gray-600 hover:text-gray-900 hover:bg-gray-100"
                }`}
              >
                <Mic className="w-4 h-4" />
                <span>Audio</span>
              </button>

              <button
                onClick={() => setCurrentView("files")}
                className={`px-4 py-2 rounded-lg flex items-center space-x-2 transition-all duration-200 ${
                  currentView === "files"
                    ? "bg-green-100 text-green-700 shadow-sm"
                    : "text-gray-600 hover:text-gray-900 hover:bg-gray-100"
                }`}
              >
                <Folder className="w-4 h-4" />
                <span>Files</span>
              </button>

              <button
                onClick={() => setCurrentView("speech")}
                className={`px-4 py-2 rounded-lg flex items-center space-x-2 transition-all duration-200 ${
                  currentView === "speech"
                    ? "bg-orange-100 text-orange-700 shadow-sm"
                    : "text-gray-600 hover:text-gray-900 hover:bg-gray-100"
                }`}
              >
                <Volume2 className="w-4 h-4" />
                <span>Speech</span>
              </button>

              <button
                onClick={() => setCurrentView("vision")}
                className={`px-4 py-2 rounded-lg flex items-center space-x-2 transition-all duration-200 ${
                  currentView === "vision"
                    ? "bg-indigo-100 text-indigo-700 shadow-sm"
                    : "text-gray-600 hover:text-gray-900 hover:bg-gray-100"
                }`}
              >
                <Eye className="w-4 h-4" />
                <span>Vision</span>
              </button>
            </div>
          </div>
        </div>
      </nav>

      {/* Main Content */}
      <main className="h-[calc(100vh-4rem)]">
        {currentView === "chat" && <StreamingChat />}
        {currentView === "images" && <ImageGenerator />}
        {currentView === "audio" && <AudioTranscription />}
        {currentView === "files" && <FileAnalysis />}
        {currentView === "speech" && <TextToSpeech />}
        {currentView === "vision" && <VisionAnalysis />}
      </main>
    </div>
  );
}

export default App;

🧪 Testing Your Vision Analysis

Let’s test your vision analysis feature step by step to make sure everything works correctly.

Step 1: Backend Route Test

First, verify your backend route works by testing it directly:

Test with a simple image:

# Test the endpoint with an image file
curl -X POST http://localhost:8000/api/vision/analyze \
  -F "image=@test-image.jpg" \
  -F "analysisType=general" \
  -F "includeOCR=true" \
  -F "extractData=true"

Expected response:

{
  "success": true,
  "file_info": {
    "name": "test-image.jpg",
    "size": 245678,
    "type": "image/jpeg"
  },
  "analysis": {
    "type": "general",
    "include_ocr": true,
    "extract_data": true,
    "result": "This image shows...",
    "model": "gpt-4o"
  },
  "timestamp": "2024-01-15T10:30:00.000Z"
}

Step 2: Full Application Test

Start both servers:

Backend (in your backend folder):

npm run dev

Frontend (in your frontend folder):

npm run dev

Test the complete flow:

Navigate to Vision → Click the “Vision” tab in navigation
Select analysis type → Choose “General”, “Document”, or “Chart” analysis
Configure options → Enable OCR or data extraction as needed
Upload an image → Try a screenshot, document, or chart
Analyze → Click “Analyze Image” and see loading state
View results → See AI analysis with image information
Download → Test downloading analysis as JSON file
Switch images → Try different image types and analysis modes

Step 3: Error Handling Test

Test error scenarios:

❌ Large image: Upload image larger than 25MB
❌ Wrong type: Upload unsupported file (like .txt or .mp4)
❌ Empty upload: Try to analyze without selecting an image
❌ Corrupt image: Upload damaged image file

Expected behavior:

Clear error messages displayed
No application crashes
User can try again with different image
Image upload resets properly after errors

✅ What You Built

Congratulations! You’ve extended your existing application with complete AI vision analysis:

✅ Extended your backend with vision processing and GPT-4o integration
✅ Added React vision component following the same patterns as your other features
✅ Implemented intelligent image analysis for documents, charts, and general content
✅ Created flexible analysis modes with OCR and data extraction options
✅ Added download functionality for analysis results
✅ Maintained consistent design with your existing application

Your application now has:

Text chat with streaming responses
Image generation with DALL-E 3 and GPT-Image-1
Audio transcription with Whisper voice recognition
File analysis with intelligent document processing
Text-to-speech with natural voice synthesis
Vision analysis with GPT-4o visual intelligence
Unified navigation between all features
Professional UI with consistent TailwindCSS styling

Complete OpenAI mastery achieved! You now have a comprehensive application that leverages all major OpenAI capabilities in a unified, professional interface. 👁️

<function_calls> [{“content”: “Create Module 2 folder structure for advanced OpenAI features”, “status”: “completed”, “priority”: “high”, “id”: “13”}, {“content”: “Create Image Generation guide with DALL-E and GPT-image-1”, “status”: “completed”, “priority”: “high”, “id”: “14”}, {“content”: “Create Audio Transcription guide with Whisper integration”, “status”: “completed”, “priority”: “high”, “id”: “15”}, {“content”: “Create Vision Analysis guide with GPT-4o”, “status”: “completed”, “priority”: “high”, “id”: “16”}, {“content”: “Create File Interaction guide with Response API”, “status”: “in_progress”, “priority”: “high”, “id”: “17”}, {“content”: “Create Text-to-Speech guide with voice synthesis”, “status”: “pending”, “priority”: “high”, “id”: “18”}]