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backend/main.py

@@ -1,445 +1,44 @@
 """
-Medical Report Analysis Platform - Main Backend Application
-Comprehensive AI-powered medical document analysis with multi-model processing
-With HIPAA/GDPR Security & Compliance Features
+Medical Report Analysis Platform - Minimal Bootstrap
 """
 
-from fastapi import FastAPI, File, UploadFile, HTTPException, BackgroundTasks, Request, Depends
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse, FileResponse
-from fastapi.staticfiles import StaticFiles
-from pydantic import BaseModel
-from pathlib import Path
-from typing import List, Dict, Optional, Any
-import os
-import tempfile
-import logging
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
 from datetime import datetime
-import uuid
-
-# Import processing modules
-from pdf_processor import PDFProcessor
-from document_classifier import DocumentClassifier
-from model_router import ModelRouter
-from analysis_synthesizer import AnalysisSynthesizer
-from security import get_security_manager, ComplianceValidator, DataEncryption
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-)
-logger = logging.getLogger(__name__)
 
 # Initialize FastAPI app
 app = FastAPI(
     title="Medical Report Analysis Platform",
-    description="HIPAA/GDPR Compliant AI-powered medical document analysis",
+    description="AI-powered medical document analysis",
     version="2.0.0"
 )
 
-# CORS configuration
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Configure appropriately for production
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Mount static files (frontend)
-static_dir = Path(__file__).parent / "static"
-if static_dir.exists():
-    app.mount("/assets", StaticFiles(directory=static_dir / "assets"), name="assets")
-    logger.info("Static files mounted successfully")
-
-# Initialize processing components
-pdf_processor = PDFProcessor()
-document_classifier = DocumentClassifier()
-model_router = ModelRouter()
-analysis_synthesizer = AnalysisSynthesizer()
-
-# Initialize security components
-security_manager = get_security_manager()
-compliance_validator = ComplianceValidator()
-data_encryption = DataEncryption()
-
-logger.info("Security and compliance features initialized")
-
-# Request/Response Models
-class AnalysisStatus(BaseModel):
-    job_id: str
-    status: str
-    progress: float
-    message: str
-
-class AnalysisResult(BaseModel):
-    job_id: str
-    document_type: str
-    confidence: float
-    analysis: Dict[str, Any]
-    specialized_results: List[Dict[str, Any]]
-    summary: str
-    timestamp: str
-
-class HealthCheck(BaseModel):
-    status: str
-    version: str
-    timestamp: str
-
-# In-memory job tracking (use Redis/database in production)
-job_tracker: Dict[str, Dict[str, Any]] = {}
-
-
-@app.get("/api", response_model=HealthCheck)
-async def api_root():
-    """API health check endpoint"""
-    return HealthCheck(
-        status="healthy",
-        version="1.0.0",
-        timestamp=datetime.utcnow().isoformat()
-    )
-
-
 @app.get("/")
 async def root():
-    """Serve frontend"""
-    static_dir = Path(__file__).parent / "static"
-    index_file = static_dir / "index.html"
-    
-    if index_file.exists():
-        return FileResponse(index_file)
-    else:
-        return {"message": "Medical Report Analysis Platform API", "version": "1.0.0"}
-
+    """Root endpoint"""
+    return {
+        "message": "Medical Report Analysis Platform API",
+        "version": "2.0.0",
+        "status": "online"
+    }
 
 @app.get("/health")
 async def health_check():
-    """Detailed health check with component status"""
+    """Health check endpoint"""
     return {
         "status": "healthy",
-        "components": {
-            "pdf_processor": "ready",
-            "classifier": "ready",
-            "model_router": "ready",
-            "synthesizer": "ready",
-            "security": "ready",
-            "compliance": "active"
-        },
         "timestamp": datetime.utcnow().isoformat()
     }
 
-
-@app.get("/compliance-status")
-async def get_compliance_status():
-    """Get HIPAA/GDPR compliance status"""
-    return compliance_validator.check_compliance()
-
-
-@app.post("/auth/login")
-async def login(email: str, password: str):
-    """
-    User authentication endpoint
-    In production, validate credentials against secure database
-    """
-    # Demo authentication - in production, validate against database
-    logger.warning("Demo authentication - implement secure auth in production")
-    
-    # For demo, accept any credentials
-    user_id = str(uuid.uuid4())
-    token = security_manager.create_access_token(user_id, email)
-    
-    return {
-        "access_token": token,
-        "token_type": "bearer",
-        "user_id": user_id,
-        "email": email
-    }
-
-
-@app.post("/analyze", response_model=AnalysisStatus)
-async def analyze_document(
-    request: Request,
-    file: UploadFile = File(...),
-    background_tasks: BackgroundTasks = BackgroundTasks(),
-    current_user: Dict[str, Any] = Depends(security_manager.get_current_user)
-):
-    """
-    Upload and analyze a medical document with audit logging
-    
-    This endpoint initiates the two-layer processing:
-    - Layer 1: PDF extraction and classification
-    - Layer 2: Specialized model analysis
-    
-    Security: Logs all PHI access for HIPAA compliance
-    """
-    
-    # Generate unique job ID
-    job_id = str(uuid.uuid4())
-    
-    # Audit log: Document upload
-    client_ip = request.client.host if request.client else "unknown"
-    security_manager.audit_logger.log_phi_access(
-        user_id=current_user.get("user_id", "unknown"),
-        document_id=job_id,
-        action="UPLOAD",
-        ip_address=client_ip
-    )
-    
-    # Validate file type
-    if not file.filename.lower().endswith('.pdf'):
-        raise HTTPException(
-            status_code=400,
-            detail="Only PDF files are supported"
-        )
-    
-    # Initialize job tracking
-    job_tracker[job_id] = {
-        "status": "processing",
-        "progress": 0.0,
-        "filename": file.filename,
-        "user_id": current_user.get("user_id"),
-        "created_at": datetime.utcnow().isoformat()
-    }
-    
-    try:
-        # Save uploaded file temporarily
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
-            content = await file.read()
-            tmp_file.write(content)
-            tmp_file_path = tmp_file.name
-        
-        # Schedule background processing
-        background_tasks.add_task(
-            process_document_pipeline,
-            job_id,
-            tmp_file_path,
-            file.filename,
-            current_user.get("user_id")
-        )
-        
-        logger.info(f"Analysis job {job_id} created for file: {file.filename}")
-        
-        return AnalysisStatus(
-            job_id=job_id,
-            status="processing",
-            progress=0.0,
-            message="Document uploaded successfully. Analysis in progress."
-        )
-        
-    except Exception as e:
-        logger.error(f"Error creating analysis job: {str(e)}")
-        job_tracker[job_id]["status"] = "failed"
-        job_tracker[job_id]["error"] = str(e)
-        
-        # Audit log: Failed upload
-        security_manager.audit_logger.log_access(
-            user_id=current_user.get("user_id", "unknown"),
-            action="UPLOAD_FAILED",
-            resource=f"document:{job_id}",
-            ip_address=client_ip,
-            status="FAILED",
-            details={"error": str(e)}
-        )
-        
-        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
-
-
-@app.get("/status/{job_id}", response_model=AnalysisStatus)
-async def get_analysis_status(job_id: str):
-    """Get the current status of an analysis job"""
-    
-    if job_id not in job_tracker:
-        raise HTTPException(status_code=404, detail="Job not found")
-    
-    job_data = job_tracker[job_id]
-    
-    return AnalysisStatus(
-        job_id=job_id,
-        status=job_data["status"],
-        progress=job_data.get("progress", 0.0),
-        message=job_data.get("message", "Processing...")
-    )
-
-
-@app.get("/results/{job_id}", response_model=AnalysisResult)
-async def get_analysis_results(job_id: str):
-    """Retrieve the analysis results for a completed job"""
-    
-    if job_id not in job_tracker:
-        raise HTTPException(status_code=404, detail="Job not found")
-    
-    job_data = job_tracker[job_id]
-    
-    if job_data["status"] != "completed":
-        raise HTTPException(
-            status_code=400,
-            detail=f"Analysis not completed. Current status: {job_data['status']}"
-        )
-    
-    return AnalysisResult(**job_data["result"])
-
-
-@app.get("/supported-models")
-async def get_supported_models():
-    """Get list of supported medical AI models by domain"""
+@app.get("/api")
+async def api_root():
+    """API status"""
     return {
-        "domains": {
-            "clinical_notes": {
-                "models": ["MedGemma 27B", "Bio_ClinicalBERT"],
-                "tasks": ["summarization", "entity_extraction", "coding"]
-            },
-            "radiology": {
-                "models": ["MedGemma 4B Multimodal", "MONAI"],
-                "tasks": ["vqa", "report_generation", "segmentation"]
-            },
-            "pathology": {
-                "models": ["Path Foundation", "UNI2-h"],
-                "tasks": ["slide_classification", "embedding_generation"]
-            },
-            "cardiology": {
-                "models": ["HuBERT-ECG"],
-                "tasks": ["ecg_analysis", "event_prediction"]
-            },
-            "laboratory": {
-                "models": ["DrLlama", "Lab-AI"],
-                "tasks": ["normalization", "explanation"]
-            },
-            "drug_interactions": {
-                "models": ["CatBoost DDI", "DrugGen"],
-                "tasks": ["interaction_classification"]
-            },
-            "diagnosis": {
-                "models": ["MedGemma 27B"],
-                "tasks": ["differential_diagnosis", "triage"]
-            },
-            "coding": {
-                "models": ["Rayyan Med Coding", "ICD-10 Predictors"],
-                "tasks": ["icd10_extraction", "cpt_coding"]
-            },
-            "mental_health": {
-                "models": ["MentalBERT"],
-                "tasks": ["screening", "sentiment_analysis"]
-            }
-        }
+        "status": "healthy",
+        "version": "2.0.0",
+        "timestamp": datetime.utcnow().isoformat()
     }
 
-
-async def process_document_pipeline(job_id: str, file_path: str, filename: str, user_id: str = "unknown"):
-    """
-    Background task for processing medical documents through the full pipeline
-    
-    Pipeline stages:
-    1. PDF Extraction (text, images, tables)
-    2. Document Classification
-    3. Intelligent Routing
-    4. Specialized Model Analysis
-    5. Result Synthesis
-    
-    Security: All stages logged for HIPAA compliance
-    """
-    
-    try:
-        # Stage 1: PDF Processing
-        job_tracker[job_id]["progress"] = 0.1
-        job_tracker[job_id]["message"] = "Extracting content from PDF..."
-        logger.info(f"Job {job_id}: Starting PDF extraction")
-        
-        pdf_content = await pdf_processor.extract_content(file_path)
-        
-        # Stage 2: Document Classification
-        job_tracker[job_id]["progress"] = 0.3
-        job_tracker[job_id]["message"] = "Classifying document type..."
-        logger.info(f"Job {job_id}: Classifying document")
-        
-        classification = await document_classifier.classify(pdf_content)
-        
-        # Audit log: Classification complete
-        security_manager.audit_logger.log_phi_access(
-            user_id=user_id,
-            document_id=job_id,
-            action="CLASSIFY",
-            ip_address="internal"
-        )
-        
-        # Stage 3: Model Routing
-        job_tracker[job_id]["progress"] = 0.4
-        job_tracker[job_id]["message"] = "Routing to specialized models..."
-        logger.info(f"Job {job_id}: Routing to models - {classification['document_type']}")
-        
-        model_tasks = model_router.route(classification, pdf_content)
-        
-        # Stage 4: Specialized Analysis
-        job_tracker[job_id]["progress"] = 0.5
-        job_tracker[job_id]["message"] = "Running specialized analysis..."
-        logger.info(f"Job {job_id}: Running {len(model_tasks)} specialized models")
-        
-        specialized_results = []
-        for i, task in enumerate(model_tasks):
-            result = await model_router.execute_task(task)
-            specialized_results.append(result)
-            progress = 0.5 + (0.3 * (i + 1) / len(model_tasks))
-            job_tracker[job_id]["progress"] = progress
-        
-        # Stage 5: Result Synthesis
-        job_tracker[job_id]["progress"] = 0.9
-        job_tracker[job_id]["message"] = "Synthesizing results..."
-        logger.info(f"Job {job_id}: Synthesizing results")
-        
-        final_analysis = await analysis_synthesizer.synthesize(
-            classification,
-            specialized_results,
-            pdf_content
-        )
-        
-        # Complete
-        job_tracker[job_id]["progress"] = 1.0
-        job_tracker[job_id]["status"] = "completed"
-        job_tracker[job_id]["message"] = "Analysis complete"
-        job_tracker[job_id]["result"] = {
-            "job_id": job_id,
-            "document_type": classification["document_type"],
-            "confidence": classification["confidence"],
-            "analysis": final_analysis,
-            "specialized_results": specialized_results,
-            "summary": final_analysis.get("summary", ""),
-            "timestamp": datetime.utcnow().isoformat()
-        }
-        
-        logger.info(f"Job {job_id}: Analysis completed successfully")
-        
-        # Audit log: Analysis complete
-        security_manager.audit_logger.log_phi_access(
-            user_id=user_id,
-            document_id=job_id,
-            action="ANALYSIS_COMPLETE",
-            ip_address="internal"
-        )
-        
-        # Secure cleanup of temporary file
-        data_encryption.secure_delete(file_path)
-        
-    except Exception as e:
-        logger.error(f"Job {job_id}: Analysis failed - {str(e)}")
-        job_tracker[job_id]["status"] = "failed"
-        job_tracker[job_id]["message"] = f"Analysis failed: {str(e)}"
-        job_tracker[job_id]["error"] = str(e)
-        
-        # Audit log: Analysis failed
-        security_manager.audit_logger.log_access(
-            user_id=user_id,
-            action="ANALYSIS_FAILED",
-            resource=f"document:{job_id}",
-            ip_address="internal",
-            status="FAILED",
-            details={"error": str(e)}
-        )
-        
-        # Cleanup on error
-        if os.path.exists(file_path):
-            data_encryption.secure_delete(file_path)
-
-
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=7860)

backend/main_full.py

@@ -0,0 +1,445 @@
+"""
+Medical Report Analysis Platform - Main Backend Application
+Comprehensive AI-powered medical document analysis with multi-model processing
+With HIPAA/GDPR Security & Compliance Features
+"""
+
+from fastapi import FastAPI, File, UploadFile, HTTPException, BackgroundTasks, Request, Depends
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, FileResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+from pathlib import Path
+from typing import List, Dict, Optional, Any
+import os
+import tempfile
+import logging
+from datetime import datetime
+import uuid
+
+# Import processing modules
+from pdf_processor import PDFProcessor
+from document_classifier import DocumentClassifier
+from model_router import ModelRouter
+from analysis_synthesizer import AnalysisSynthesizer
+from security import get_security_manager, ComplianceValidator, DataEncryption
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="Medical Report Analysis Platform",
+    description="HIPAA/GDPR Compliant AI-powered medical document analysis",
+    version="2.0.0"
+)
+
+# CORS configuration
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Configure appropriately for production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Mount static files (frontend)
+static_dir = Path(__file__).parent / "static"
+if static_dir.exists():
+    app.mount("/assets", StaticFiles(directory=static_dir / "assets"), name="assets")
+    logger.info("Static files mounted successfully")
+
+# Initialize processing components
+pdf_processor = PDFProcessor()
+document_classifier = DocumentClassifier()
+model_router = ModelRouter()
+analysis_synthesizer = AnalysisSynthesizer()
+
+# Initialize security components
+security_manager = get_security_manager()
+compliance_validator = ComplianceValidator()
+data_encryption = DataEncryption()
+
+logger.info("Security and compliance features initialized")
+
+# Request/Response Models
+class AnalysisStatus(BaseModel):
+    job_id: str
+    status: str
+    progress: float
+    message: str
+
+class AnalysisResult(BaseModel):
+    job_id: str
+    document_type: str
+    confidence: float
+    analysis: Dict[str, Any]
+    specialized_results: List[Dict[str, Any]]
+    summary: str
+    timestamp: str
+
+class HealthCheck(BaseModel):
+    status: str
+    version: str
+    timestamp: str
+
+# In-memory job tracking (use Redis/database in production)
+job_tracker: Dict[str, Dict[str, Any]] = {}
+
+
+@app.get("/api", response_model=HealthCheck)
+async def api_root():
+    """API health check endpoint"""
+    return HealthCheck(
+        status="healthy",
+        version="1.0.0",
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
+@app.get("/")
+async def root():
+    """Serve frontend"""
+    static_dir = Path(__file__).parent / "static"
+    index_file = static_dir / "index.html"
+    
+    if index_file.exists():
+        return FileResponse(index_file)
+    else:
+        return {"message": "Medical Report Analysis Platform API", "version": "1.0.0"}
+
+
+@app.get("/health")
+async def health_check():
+    """Detailed health check with component status"""
+    return {
+        "status": "healthy",
+        "components": {
+            "pdf_processor": "ready",
+            "classifier": "ready",
+            "model_router": "ready",
+            "synthesizer": "ready",
+            "security": "ready",
+            "compliance": "active"
+        },
+        "timestamp": datetime.utcnow().isoformat()
+    }
+
+
+@app.get("/compliance-status")
+async def get_compliance_status():
+    """Get HIPAA/GDPR compliance status"""
+    return compliance_validator.check_compliance()
+
+
+@app.post("/auth/login")
+async def login(email: str, password: str):
+    """
+    User authentication endpoint
+    In production, validate credentials against secure database
+    """
+    # Demo authentication - in production, validate against database
+    logger.warning("Demo authentication - implement secure auth in production")
+    
+    # For demo, accept any credentials
+    user_id = str(uuid.uuid4())
+    token = security_manager.create_access_token(user_id, email)
+    
+    return {
+        "access_token": token,
+        "token_type": "bearer",
+        "user_id": user_id,
+        "email": email
+    }
+
+
+@app.post("/analyze", response_model=AnalysisStatus)
+async def analyze_document(
+    request: Request,
+    file: UploadFile = File(...),
+    background_tasks: BackgroundTasks = BackgroundTasks(),
+    current_user: Dict[str, Any] = Depends(security_manager.get_current_user)
+):
+    """
+    Upload and analyze a medical document with audit logging
+    
+    This endpoint initiates the two-layer processing:
+    - Layer 1: PDF extraction and classification
+    - Layer 2: Specialized model analysis
+    
+    Security: Logs all PHI access for HIPAA compliance
+    """
+    
+    # Generate unique job ID
+    job_id = str(uuid.uuid4())
+    
+    # Audit log: Document upload
+    client_ip = request.client.host if request.client else "unknown"
+    security_manager.audit_logger.log_phi_access(
+        user_id=current_user.get("user_id", "unknown"),
+        document_id=job_id,
+        action="UPLOAD",
+        ip_address=client_ip
+    )
+    
+    # Validate file type
+    if not file.filename.lower().endswith('.pdf'):
+        raise HTTPException(
+            status_code=400,
+            detail="Only PDF files are supported"
+        )
+    
+    # Initialize job tracking
+    job_tracker[job_id] = {
+        "status": "processing",
+        "progress": 0.0,
+        "filename": file.filename,
+        "user_id": current_user.get("user_id"),
+        "created_at": datetime.utcnow().isoformat()
+    }
+    
+    try:
+        # Save uploaded file temporarily
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
+            content = await file.read()
+            tmp_file.write(content)
+            tmp_file_path = tmp_file.name
+        
+        # Schedule background processing
+        background_tasks.add_task(
+            process_document_pipeline,
+            job_id,
+            tmp_file_path,
+            file.filename,
+            current_user.get("user_id")
+        )
+        
+        logger.info(f"Analysis job {job_id} created for file: {file.filename}")
+        
+        return AnalysisStatus(
+            job_id=job_id,
+            status="processing",
+            progress=0.0,
+            message="Document uploaded successfully. Analysis in progress."
+        )
+        
+    except Exception as e:
+        logger.error(f"Error creating analysis job: {str(e)}")
+        job_tracker[job_id]["status"] = "failed"
+        job_tracker[job_id]["error"] = str(e)
+        
+        # Audit log: Failed upload
+        security_manager.audit_logger.log_access(
+            user_id=current_user.get("user_id", "unknown"),
+            action="UPLOAD_FAILED",
+            resource=f"document:{job_id}",
+            ip_address=client_ip,
+            status="FAILED",
+            details={"error": str(e)}
+        )
+        
+        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
+
+
+@app.get("/status/{job_id}", response_model=AnalysisStatus)
+async def get_analysis_status(job_id: str):
+    """Get the current status of an analysis job"""
+    
+    if job_id not in job_tracker:
+        raise HTTPException(status_code=404, detail="Job not found")
+    
+    job_data = job_tracker[job_id]
+    
+    return AnalysisStatus(
+        job_id=job_id,
+        status=job_data["status"],
+        progress=job_data.get("progress", 0.0),
+        message=job_data.get("message", "Processing...")
+    )
+
+
+@app.get("/results/{job_id}", response_model=AnalysisResult)
+async def get_analysis_results(job_id: str):
+    """Retrieve the analysis results for a completed job"""
+    
+    if job_id not in job_tracker:
+        raise HTTPException(status_code=404, detail="Job not found")
+    
+    job_data = job_tracker[job_id]
+    
+    if job_data["status"] != "completed":
+        raise HTTPException(
+            status_code=400,
+            detail=f"Analysis not completed. Current status: {job_data['status']}"
+        )
+    
+    return AnalysisResult(**job_data["result"])
+
+
+@app.get("/supported-models")
+async def get_supported_models():
+    """Get list of supported medical AI models by domain"""
+    return {
+        "domains": {
+            "clinical_notes": {
+                "models": ["MedGemma 27B", "Bio_ClinicalBERT"],
+                "tasks": ["summarization", "entity_extraction", "coding"]
+            },
+            "radiology": {
+                "models": ["MedGemma 4B Multimodal", "MONAI"],
+                "tasks": ["vqa", "report_generation", "segmentation"]
+            },
+            "pathology": {
+                "models": ["Path Foundation", "UNI2-h"],
+                "tasks": ["slide_classification", "embedding_generation"]
+            },
+            "cardiology": {
+                "models": ["HuBERT-ECG"],
+                "tasks": ["ecg_analysis", "event_prediction"]
+            },
+            "laboratory": {
+                "models": ["DrLlama", "Lab-AI"],
+                "tasks": ["normalization", "explanation"]
+            },
+            "drug_interactions": {
+                "models": ["CatBoost DDI", "DrugGen"],
+                "tasks": ["interaction_classification"]
+            },
+            "diagnosis": {
+                "models": ["MedGemma 27B"],
+                "tasks": ["differential_diagnosis", "triage"]
+            },
+            "coding": {
+                "models": ["Rayyan Med Coding", "ICD-10 Predictors"],
+                "tasks": ["icd10_extraction", "cpt_coding"]
+            },
+            "mental_health": {
+                "models": ["MentalBERT"],
+                "tasks": ["screening", "sentiment_analysis"]
+            }
+        }
+    }
+
+
+async def process_document_pipeline(job_id: str, file_path: str, filename: str, user_id: str = "unknown"):
+    """
+    Background task for processing medical documents through the full pipeline
+    
+    Pipeline stages:
+    1. PDF Extraction (text, images, tables)
+    2. Document Classification
+    3. Intelligent Routing
+    4. Specialized Model Analysis
+    5. Result Synthesis
+    
+    Security: All stages logged for HIPAA compliance
+    """
+    
+    try:
+        # Stage 1: PDF Processing
+        job_tracker[job_id]["progress"] = 0.1
+        job_tracker[job_id]["message"] = "Extracting content from PDF..."
+        logger.info(f"Job {job_id}: Starting PDF extraction")
+        
+        pdf_content = await pdf_processor.extract_content(file_path)
+        
+        # Stage 2: Document Classification
+        job_tracker[job_id]["progress"] = 0.3
+        job_tracker[job_id]["message"] = "Classifying document type..."
+        logger.info(f"Job {job_id}: Classifying document")
+        
+        classification = await document_classifier.classify(pdf_content)
+        
+        # Audit log: Classification complete
+        security_manager.audit_logger.log_phi_access(
+            user_id=user_id,
+            document_id=job_id,
+            action="CLASSIFY",
+            ip_address="internal"
+        )
+        
+        # Stage 3: Model Routing
+        job_tracker[job_id]["progress"] = 0.4
+        job_tracker[job_id]["message"] = "Routing to specialized models..."
+        logger.info(f"Job {job_id}: Routing to models - {classification['document_type']}")
+        
+        model_tasks = model_router.route(classification, pdf_content)
+        
+        # Stage 4: Specialized Analysis
+        job_tracker[job_id]["progress"] = 0.5
+        job_tracker[job_id]["message"] = "Running specialized analysis..."
+        logger.info(f"Job {job_id}: Running {len(model_tasks)} specialized models")
+        
+        specialized_results = []
+        for i, task in enumerate(model_tasks):
+            result = await model_router.execute_task(task)
+            specialized_results.append(result)
+            progress = 0.5 + (0.3 * (i + 1) / len(model_tasks))
+            job_tracker[job_id]["progress"] = progress
+        
+        # Stage 5: Result Synthesis
+        job_tracker[job_id]["progress"] = 0.9
+        job_tracker[job_id]["message"] = "Synthesizing results..."
+        logger.info(f"Job {job_id}: Synthesizing results")
+        
+        final_analysis = await analysis_synthesizer.synthesize(
+            classification,
+            specialized_results,
+            pdf_content
+        )
+        
+        # Complete
+        job_tracker[job_id]["progress"] = 1.0
+        job_tracker[job_id]["status"] = "completed"
+        job_tracker[job_id]["message"] = "Analysis complete"
+        job_tracker[job_id]["result"] = {
+            "job_id": job_id,
+            "document_type": classification["document_type"],
+            "confidence": classification["confidence"],
+            "analysis": final_analysis,
+            "specialized_results": specialized_results,
+            "summary": final_analysis.get("summary", ""),
+            "timestamp": datetime.utcnow().isoformat()
+        }
+        
+        logger.info(f"Job {job_id}: Analysis completed successfully")
+        
+        # Audit log: Analysis complete
+        security_manager.audit_logger.log_phi_access(
+            user_id=user_id,
+            document_id=job_id,
+            action="ANALYSIS_COMPLETE",
+            ip_address="internal"
+        )
+        
+        # Secure cleanup of temporary file
+        data_encryption.secure_delete(file_path)
+        
+    except Exception as e:
+        logger.error(f"Job {job_id}: Analysis failed - {str(e)}")
+        job_tracker[job_id]["status"] = "failed"
+        job_tracker[job_id]["message"] = f"Analysis failed: {str(e)}"
+        job_tracker[job_id]["error"] = str(e)
+        
+        # Audit log: Analysis failed
+        security_manager.audit_logger.log_access(
+            user_id=user_id,
+            action="ANALYSIS_FAILED",
+            resource=f"document:{job_id}",
+            ip_address="internal",
+            status="FAILED",
+            details={"error": str(e)}
+        )
+        
+        # Cleanup on error
+        if os.path.exists(file_path):
+            data_encryption.secure_delete(file_path)
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

backend/requirements.txt

@@ -1,15 +1,4 @@
 fastapi==0.109.0
-uvicorn[standard]==0.27.0
+uvicorn==0.27.0
 python-multipart==0.0.6
 pydantic==2.5.3
-PyPDF2==3.0.1
-pdf2image==1.17.0
-Pillow==10.2.0
-pytesseract==0.3.10
-PyMuPDF==1.23.8
-requests==2.31.0
-aiofiles==23.2.1
-PyJWT==2.8.0
-python-docx==1.1.0
-numpy==1.26.4
-pandas==2.2.0