app.py

from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.responses import JSONResponse, HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
import base64
import io
from PIL import Image
from lab_analyzer import LabReportAnalyzer
import logging
import os

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize FastAPI app
app = FastAPI(
    title="Lab Report Analysis API",
    description="AI-powered lab report analysis service using Google AI Studio",
    version="1.0.0",
    docs_url="/docs",
    redoc_url="/redoc"
)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Configure this properly for production
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize the lab analyzer
analyzer = LabReportAnalyzer()

@app.get("/health")
async def health_check():
    """Health check endpoint for monitoring"""
    return {"status": "healthy", "service": "lab-report-analyzer"}

@app.post("/analyze")
async def analyze_lab_report(file: UploadFile = File(...)):
    """

    Analyze a lab report image and return structured results

    

    Args:

        file: Uploaded image file (jpg, jpeg, png, bmp, tiff, webp)

    

    Returns:

        JSON response with analysis results

    """
    try:
        # Validate file type
        if not file.content_type.startswith('image/'):
            raise HTTPException(
                status_code=400, 
                detail="File must be an image (jpg, jpeg, png, bmp, tiff, webp)"
            )
        
        # Read and validate image
        contents = await file.read()
        if len(contents) == 0:
            raise HTTPException(status_code=400, detail="Empty file uploaded")
        
        # Validate image can be opened
        try:
            image = Image.open(io.BytesIO(contents))
            image.verify()  # Verify it's a valid image
        except Exception as e:
            raise HTTPException(status_code=400, detail=f"Invalid image file: {str(e)}")
        
        # Convert to base64 for analysis
        image_b64 = base64.b64encode(contents).decode("utf-8")
        
        # Analyze the lab report
        logger.info(f"Analyzing lab report: {file.filename}")
        analysis_result = await analyzer.analyze_report(image_b64)
        
        return JSONResponse(
            status_code=200,
            content={
                "success": True,
                "filename": file.filename,
                "analysis": analysis_result
            }
        )
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error analyzing lab report: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")

@app.post("/analyze-base64")
async def analyze_lab_report_base64(data: dict):
    """

    Analyze a lab report from base64 encoded image

    

    Args:

        data: JSON with 'image' key containing base64 encoded image

    

    Returns:

        JSON response with analysis results

    """
    try:
        if 'image' not in data:
            raise HTTPException(status_code=400, detail="Missing 'image' field in request body")
        
        image_b64 = data['image']
        
        # Remove data:image/...;base64, prefix if present
        if image_b64.startswith('data:image'):
            image_b64 = image_b64.split(',')[1]
        
        # Validate base64 and image
        try:
            image_bytes = base64.b64decode(image_b64)
            image = Image.open(io.BytesIO(image_bytes))
            image.verify()
        except Exception as e:
            raise HTTPException(status_code=400, detail=f"Invalid base64 image: {str(e)}")
        
        # Analyze the lab report
        logger.info("Analyzing lab report from base64 data")
        analysis_result = await analyzer.analyze_report(image_b64)
        
        return JSONResponse(
            status_code=200,
            content={
                "success": True,
                "analysis": analysis_result
            }
        )
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error analyzing base64 lab report: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")

# Flutter-friendly endpoint aliases
@app.post("/api/analyze-lab")
async def analyze_lab_api(file: UploadFile = File(...)):
    """Flutter-friendly endpoint for lab analysis"""
    return await analyze_lab_report(file)

@app.post("/api/analyze-lab-base64")
async def analyze_lab_base64_api(data: dict):
    """Flutter-friendly endpoint for base64 lab analysis"""
    return await analyze_lab_report_base64(data)

@app.get("/", response_class=HTMLResponse)
async def root():
    """Main page with upload interface"""
    return """

    <!DOCTYPE html>

    <html lang="en">

    <head>

        <meta charset="UTF-8">

        <meta name="viewport" content="width=device-width, initial-scale=1.0">

        <title>🏥 Lab Report Analysis AI</title>

        <style>

            * { margin: 0; padding: 0; box-sizing: border-box; }

            body { 

                font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; 

                background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);

                min-height: 100vh;

                padding: 20px;

            }

            .container { 

                max-width: 1200px; 

                margin: 0 auto; 

                background: white; 

                border-radius: 20px; 

                box-shadow: 0 20px 60px rgba(0,0,0,0.1);

                overflow: hidden;

            }

            .header { 

                background: linear-gradient(45deg, #2196F3, #21CBF3);

                color: white; 

                padding: 40px; 

                text-align: center; 

            }

            .header h1 { font-size: 2.5em; margin-bottom: 10px; }

            .header p { font-size: 1.2em; opacity: 0.9; }

            .content { padding: 40px; }

            .upload-area { 

                border: 3px dashed #2196F3; 

                border-radius: 15px; 

                padding: 40px; 

                text-align: center; 

                margin: 20px 0;

                transition: all 0.3s ease;

                cursor: pointer;

            }

            .upload-area:hover { 

                border-color: #1976D2; 

                background-color: #f5f5f5; 

            }

            .upload-area.dragover { 

                border-color: #4CAF50; 

                background-color: #e8f5e8; 

            }

            input[type="file"] { display: none; }

            .btn { 

                background: linear-gradient(45deg, #2196F3, #21CBF3);

                color: white; 

                border: none; 

                padding: 15px 30px; 

                border-radius: 25px; 

                cursor: pointer; 

                font-size: 16px;

                margin: 10px;

                transition: transform 0.2s ease;

            }

            .btn:hover { transform: translateY(-2px); }

            .btn:disabled { 

                background: #ccc; 

                cursor: not-allowed; 

                transform: none;

            }

            .result { 

                margin-top: 30px; 

                padding: 20px; 

                border-radius: 10px; 

                background: #f8f9fa;

                border-left: 5px solid #2196F3;

            }

            .loading { 

                text-align: center; 

                color: #2196F3; 

                font-size: 18px;

            }

            .error { 

                background: #ffebee; 

                border-left-color: #f44336; 

                color: #c62828;

            }

            .success { 

                background: #e8f5e8; 

                border-left-color: #4CAF50; 

            }

            .api-info {

                background: #f0f4f8;

                padding: 30px;

                margin-top: 40px;

                border-radius: 15px;

            }

            .endpoint {

                background: #fff;

                padding: 15px;

                margin: 10px 0;

                border-radius: 8px;

                border-left: 4px solid #2196F3;

            }

            .method {

                background: #2196F3;

                color: white;

                padding: 4px 8px;

                border-radius: 4px;

                font-weight: bold;

                font-size: 12px;

            }

            pre {

                background: #2d3748;

                color: #e2e8f0;

                padding: 15px;

                border-radius: 8px;

                overflow-x: auto;

                margin: 10px 0;

            }

            .features {

                display: grid;

                grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));

                gap: 20px;

                margin: 30px 0;

            }

            .feature {

                text-align: center;

                padding: 20px;

                border-radius: 10px;

                background: #f8f9fa;

            }

            .feature-icon {

                font-size: 2em;

                margin-bottom: 10px;

            }

        </style>

    </head>

    <body>

        <div class="container">

            <div class="header">

                <h1>🏥 Lab Report Analysis AI</h1>

                <p>AI-powered medical lab report analysis using Google AI Studio</p>

            </div>

            

            <div class="content">

                <div class="features">

                    <div class="feature">

                        <div class="feature-icon">📸</div>

                        <h3>Image Upload</h3>

                        <p>Support for JPG, PNG, TIFF, and more</p>

                    </div>

                    <div class="feature">

                        <div class="feature-icon">🤖</div>

                        <h3>AI Analysis</h3>

                        <p>Google Gemini 2.0 Flash model</p>

                    </div>

                    <div class="feature">

                        <div class="feature-icon">📊</div>

                        <h3>Structured Results</h3>

                        <p>Summary, findings, and interpretations</p>

                    </div>

                    <div class="feature">

                        <div class="feature-icon">⚡</div>

                        <h3>Fast Processing</h3>

                        <p>Real-time analysis results</p>

                    </div>

                </div>



                <div class="upload-area" onclick="document.getElementById('fileInput').click()">

                    <h3>📤 Upload Lab Report Image</h3>

                    <p>Click here or drag and drop your lab report image</p>

                    <input type="file" id="fileInput" accept="image/*">

                </div>

                

                <div style="text-align: center;">

                    <button class="btn" onclick="analyzeImage()" id="analyzeBtn" disabled>

                        🔬 Analyze Report

                    </button>

                    <button class="btn" onclick="clearResults()">

                        🗑️ Clear

                    </button>

                </div>

                

                <div id="result"></div>



                <div class="api-info">

                    <h2>🔧 API Documentation</h2>

                    <p>This service provides RESTful API endpoints for programmatic access:</p>

                    

                    <div class="endpoint">

                        <span class="method">POST</span> <strong>/analyze</strong>

                        <p>Upload lab report image file for analysis</p>

                    </div>

                    

                    <div class="endpoint">

                        <span class="method">POST</span> <strong>/analyze-base64</strong>

                        <p>Analyze lab report from base64 encoded image</p>

                    </div>

                    

                    <div class="endpoint">

                        <span class="method">POST</span> <strong>/api/analyze-lab</strong>

                        <p>Flutter-friendly endpoint for file upload</p>

                    </div>

                    

                    <div class="endpoint">

                        <span class="method">POST</span> <strong>/api/analyze-lab-base64</strong>

                        <p>Flutter-friendly endpoint for base64 analysis</p>

                    </div>



                    <p><strong>📚 Interactive Documentation:</strong></p>

                    <button class="btn" onclick="window.open('/docs', '_blank')">

                        📖 OpenAPI Docs

                    </button>

                    <button class="btn" onclick="window.open('/redoc', '_blank')">

                        📘 ReDoc

                    </button>

                </div>



                <div style="text-align: center; margin-top: 40px; color: #666;">

                    <p>⚠️ This tool is for educational purposes only. Always consult healthcare professionals for medical advice.</p>

                </div>

            </div>

        </div>



        <script>

            let selectedFile = null;



            document.getElementById('fileInput').addEventListener('change', function(e) {

                selectedFile = e.target.files[0];

                if (selectedFile) {

                    document.querySelector('.upload-area h3').innerHTML = '✅ ' + selectedFile.name;

                    document.getElementById('analyzeBtn').disabled = false;

                }

            });



            // Drag and drop functionality

            const uploadArea = document.querySelector('.upload-area');

            uploadArea.addEventListener('dragover', (e) => {

                e.preventDefault();

                uploadArea.classList.add('dragover');

            });

            uploadArea.addEventListener('dragleave', () => {

                uploadArea.classList.remove('dragover');

            });

            uploadArea.addEventListener('drop', (e) => {

                e.preventDefault();

                uploadArea.classList.remove('dragover');

                const files = e.dataTransfer.files;

                if (files.length > 0) {

                    selectedFile = files[0];

                    document.querySelector('.upload-area h3').innerHTML = '✅ ' + selectedFile.name;

                    document.getElementById('analyzeBtn').disabled = false;

                }

            });



            async function analyzeImage() {

                if (!selectedFile) {

                    alert('Please select an image first');

                    return;

                }



                const resultDiv = document.getElementById('result');

                resultDiv.innerHTML = '<div class="result loading">🔄 Analyzing lab report...</div>';

                

                const analyzeBtn = document.getElementById('analyzeBtn');

                analyzeBtn.disabled = true;

                analyzeBtn.innerHTML = '⏳ Processing...';



                const formData = new FormData();

                formData.append('file', selectedFile);



                try {

                    const response = await fetch('/analyze', {

                        method: 'POST',

                        body: formData

                    });



                    const data = await response.json();



                    if (data.success) {

                        const analysis = data.analysis;

                        resultDiv.innerHTML = `

                            <div class="result success">

                                <h3>📊 Analysis Results</h3>

                                <h4>📋 Summary</h4>

                                <p>${analysis.summary || 'No summary available'}</p>

                                

                                <h4>🔍 Key Findings</h4>

                                <ul>

                                    ${analysis.key_findings?.map(finding => `<li>${finding}</li>`).join('') || '<li>No specific findings identified</li>'}

                                </ul>

                                

                                <h4>💡 Interpretation</h4>

                                <p>${analysis.interpretation || 'No interpretation available'}</p>

                                

                                <h4>⚠️ Important Note</h4>

                                <p><em>${analysis.note || 'This analysis is for educational purposes only and should not replace professional medical advice.'}</em></p>

                            </div>

                        `;

                    } else {

                        resultDiv.innerHTML = `<div class="result error">❌ Error: ${data.error || 'Analysis failed'}</div>`;

                    }

                } catch (error) {

                    resultDiv.innerHTML = `<div class="result error">❌ Error: ${error.message}</div>`;

                } finally {

                    analyzeBtn.disabled = false;

                    analyzeBtn.innerHTML = '🔬 Analyze Report';

                }

            }



            function clearResults() {

                document.getElementById('result').innerHTML = '';

                document.getElementById('fileInput').value = '';

                document.querySelector('.upload-area h3').innerHTML = '📤 Upload Lab Report Image';

                document.getElementById('analyzeBtn').disabled = true;

                selectedFile = null;

            }

        </script>

    </body>

    </html>

    """

if __name__ == "__main__":
    import uvicorn
    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)