Update app.py

app.py

@@ -1,875 +1,875 @@
-from fastapi import FastAPI, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import FileResponse
-from pydantic import BaseModel
-from typing import List, Optional, Dict
-import google.generativeai as genai
-import os
-from datetime import datetime
-import uuid
-import json
-from pathlib import Path
-from reportlab.lib.pagesizes import letter, A4
-from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
-from reportlab.lib.units import inch
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak, Table, TableStyle
-from reportlab.lib import colors
-from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
-from reportlab.pdfgen import canvas
-
-# Configure Gemini API
-os.environ["GOOGLE_API_KEY"] = "AIzaSyDid3I53qFohhZTnLjnOjz4QqlvK4RZm7o"
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-MODEL_ID = "gemini-2.0-flash-exp"
-
-# Create storage directories
-STORAGE_DIR = Path("consultation_storage")
-STORAGE_DIR.mkdir(exist_ok=True)
-
-PDF_DIR = Path("consultation_pdfs")
-PDF_DIR.mkdir(exist_ok=True)
-
-# System prompt (same as before)
-DOCTOR_SYSTEM_PROMPT = """
-You are Dr. HealBot, a calm, knowledgeable, and empathetic virtual doctor.
-
-GOAL:
-Hold a natural, focused conversation with the patient to understand their health issue and offer helpful preliminary medical guidance.
-
-CONVERSATION LOGIC:
-- Ask only relevant and concise medical questions necessary for diagnosing the illness.
-- Each question should help clarify symptoms or narrow possible causes.
-- Stop asking once enough information is collected for a basic assessment.
-- Then, provide a structured, friendly, and visually clear medical response using headings, emojis, and bullet points.
-
-FINAL RESPONSE FORMAT:
-When giving your full assessment, use this markdown-styled format:
-
-🩺 Based on what you've told me...
-Brief summary of what the patient described.
-
-💡 Possible Causes (Preliminary)
-- List 1–2 possible conditions using phrases like "It could be" or "This sounds like".
-- Include a disclaimer that this is not a confirmed diagnosis.
-
-💊 Suggested Over-the-Counter Medicines
-- Generic medicine names only (e.g., "Paracetamol 500mg every 6 hours if fever or pain")
-- Mention to check packaging or consult a pharmacist for dosage confirmation.
-
-🥗 Lifestyle & Home Care Tips
-- 2–3 practical suggestions (rest, hydration, warm compress, balanced diet, etc.)
-
-⚠ When to See a Real Doctor
-- 2–3 warning signs or conditions when urgent medical care is needed.
-
-📅 Follow-Up Advice
-- Brief recommendation for self-care or follow-up timing (e.g., "If not improving in 3 days, visit a clinic.")
-
-TONE & STYLE:
-- Speak like a real, caring doctor — short, clear, and empathetic (1–2 sentences per reply).
-- Use plain language, no jargon.
-- Only one question per turn unless clarification is essential.
-- Keep tone warm, calm, and professional.
-- Early messages: short questions only.
-- Final message: structured output with emojis and headings.
-
-IMPORTANT:
-- Always emphasize that this is preliminary guidance and not a substitute for professional care.
-- Never make definitive diagnoses; use phrases like "it sounds like" or "it could be".
-- If symptoms seem serious, always recommend urgent medical attention.
-
-CONVERSATION FLOW:
-1. Ask about the main symptom.
-2. Ask about its duration, severity, and any triggers.
-3. Ask about accompanying symptoms.
-4. Ask about medical history, allergies, or medications.
-5. Then, provide your structured assessment as described above.
-"""
-
-# =====================================================
-# PDF GENERATION FUNCTIONS
-# =====================================================
-
-def generate_pdf_summary(session_id: str, summary_text: str, patient_data: Dict, history: List[Dict]) -> str:
-    """Generate a professional PDF summary of the consultation"""
-    
-    pdf_filename = f"{session_id}_summary.pdf"
-    pdf_path = PDF_DIR / pdf_filename
-    
-    # Create PDF document
-    doc = SimpleDocTemplate(str(pdf_path), pagesize=letter,
-                           rightMargin=72, leftMargin=72,
-                           topMargin=72, bottomMargin=18)
-    
-    # Container for the 'Flowable' objects
-    elements = []
-    
-    # Define styles
-    styles = getSampleStyleSheet()
-    
-    # Custom styles
-    title_style = ParagraphStyle(
-        'CustomTitle',
-        parent=styles['Heading1'],
-        fontSize=24,
-        textColor=colors.HexColor('#667eea'),
-        spaceAfter=30,
-        alignment=TA_CENTER,
-        fontName='Helvetica-Bold'
-    )
-    
-    heading_style = ParagraphStyle(
-        'CustomHeading',
-        parent=styles['Heading2'],
-        fontSize=16,
-        textColor=colors.HexColor('#667eea'),
-        spaceAfter=12,
-        spaceBefore=12,
-        fontName='Helvetica-Bold'
-    )
-    
-    normal_style = ParagraphStyle(
-        'CustomNormal',
-        parent=styles['Normal'],
-        fontSize=11,
-        spaceAfter=12,
-        alignment=TA_JUSTIFY,
-        leading=14
-    )
-    
-    # Add Title
-    elements.append(Paragraph("🩺 AI DOCTOR CONSULTATION SUMMARY", title_style))
-    elements.append(Spacer(1, 0.3*inch))
-    
-    # Add horizontal line
-    elements.append(Spacer(1, 0.1*inch))
-    
-    # Patient Information Table
-    patient_info_data = [
-        ['Patient Name:', patient_data.get('name', 'N/A')],
-        ['Age:', patient_data.get('age', 'N/A')],
-        ['Session ID:', session_id[:20] + '...'],
-        ['Consultation Date:', datetime.now().strftime('%B %d, %Y at %I:%M %p')],
-        ['Total Messages:', str(len(history))]
-    ]
-    
-    patient_table = Table(patient_info_data, colWidths=[2*inch, 4*inch])
-    patient_table.setStyle(TableStyle([
-        ('BACKGROUND', (0, 0), (0, -1), colors.HexColor('#f0f0f0')),
-        ('TEXTCOLOR', (0, 0), (-1, -1), colors.black),
-        ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
-        ('FONTNAME', (0, 0), (0, -1), 'Helvetica-Bold'),
-        ('FONTNAME', (1, 0), (1, -1), 'Helvetica'),
-        ('FONTSIZE', (0, 0), (-1, -1), 10),
-        ('BOTTOMPADDING', (0, 0), (-1, -1), 8),
-        ('TOPPADDING', (0, 0), (-1, -1), 8),
-        ('GRID', (0, 0), (-1, -1), 1, colors.grey)
-    ]))
-    
-    elements.append(patient_table)
-    elements.append(Spacer(1, 0.3*inch))
-    
-    # Add Consultation Summary
-    elements.append(Paragraph("CONSULTATION SUMMARY", heading_style))
-    
-    # Process summary text - split by lines and convert to paragraphs
-    summary_lines = summary_text.split('\n')
-    for line in summary_lines:
-        if line.strip():
-            # Replace emojis with text equivalents for PDF compatibility
-            line = line.replace('🩺', '[Medical] ')
-            line = line.replace('💡', '[Insight] ')
-            line = line.replace('💊', '[Medicine] ')
-            line = line.replace('🥗', '[Lifestyle] ')
-            line = line.replace('⚠️', '[Warning] ')
-            line = line.replace('⚠', '[Warning] ')
-            line = line.replace('📅', '[Follow-up] ')
-            line = line.replace('━', '-')
-            
-            # Check if it's a heading (starts with **)
-            if line.strip().startswith('**') and line.strip().endswith('**'):
-                elements.append(Paragraph(line.strip('*'), heading_style))
-            else:
-                elements.append(Paragraph(line, normal_style))
-    
-    elements.append(Spacer(1, 0.3*inch))
-    
-    # Add Conversation History
-    elements.append(PageBreak())
-    elements.append(Paragraph("CONVERSATION HISTORY", heading_style))
-    elements.append(Spacer(1, 0.2*inch))
-    
-    for i, msg in enumerate(history, 1):
-        role = "DOCTOR" if msg['role'] == 'assistant' else "PATIENT"
-        timestamp = msg.get('timestamp', 'N/A')
-        
-        role_style = ParagraphStyle(
-            f'Role{i}',
-            parent=styles['Normal'],
-            fontSize=10,
-            textColor=colors.HexColor('#667eea') if role == "DOCTOR" else colors.HexColor('#28a745'),
-            fontName='Helvetica-Bold',
-            spaceAfter=4
-        )
-        
-        elements.append(Paragraph(f"{role} ({timestamp}):", role_style))
-        
-        content = msg['content'].replace('🩺', '').replace('💡', '').replace('💊', '')
-        content = content.replace('🥗', '').replace('⚠️', '').replace('⚠', '').replace('📅', '')
-        elements.append(Paragraph(content, normal_style))
-        elements.append(Spacer(1, 0.15*inch))
-    
-    # Add disclaimer at the end
-    elements.append(Spacer(1, 0.3*inch))
-    
-    disclaimer_style = ParagraphStyle(
-        'Disclaimer',
-        parent=styles['Normal'],
-        fontSize=9,
-        textColor=colors.red,
-        alignment=TA_CENTER,
-        fontName='Helvetica-Bold',
-        borderColor=colors.red,
-        borderWidth=1,
-        borderPadding=10,
-        spaceAfter=12
-    )
-    
-    elements.append(Paragraph(
-        "⚠ IMPORTANT DISCLAIMER ⚠<br/>" +
-        "This is a preliminary AI-generated consultation for informational purposes only.<br/>" +
-        "It is NOT a substitute for professional medical advice, diagnosis, or treatment.<br/>" +
-        "Always seek the advice of a qualified healthcare provider with any questions regarding a medical condition.",
-        disclaimer_style
-    ))
-    
-    # Build PDF
-    doc.build(elements)
-    
-    return pdf_filename
-
-# =====================================================
-# STORAGE FUNCTIONS (same as before)
-# =====================================================
-
-def save_session_to_json(session_id: str, memory: 'ConversationMemory'):
-    """Save session data to JSON file"""
-    file_path = STORAGE_DIR / f"{session_id}.json"
-    
-    session_data = {
-        "session_id": session_id,
-        "created_at": memory.created_at.isoformat(),
-        "last_updated": datetime.now().isoformat(),
-        "patient_data": memory.patient_data,
-        "questions_asked": memory.questions_asked,
-        "history": memory.history,
-        "message_count": len(memory.history),
-        "pdf_filename": getattr(memory, 'pdf_filename', None)
-    }
-    
-    with open(file_path, 'w', encoding='utf-8') as f:
-        json.dump(session_data, f, indent=2, ensure_ascii=False)
-
-def load_session_from_json(session_id: str) -> Optional[Dict]:
-    """Load session data from JSON file"""
-    file_path = STORAGE_DIR / f"{session_id}.json"
-    
-    if not file_path.exists():
-        return None
-    
-    with open(file_path, 'r', encoding='utf-8') as f:
-        return json.load(f)
-
-def list_all_sessions() -> List[Dict]:
-    """List all stored sessions"""
-    sessions_list = []
-    
-    for file_path in STORAGE_DIR.glob("*.json"):
-        try:
-            with open(file_path, 'r', encoding='utf-8') as f:
-                data = json.load(f)
-                sessions_list.append({
-                    "session_id": data["session_id"],
-                    "created_at": data["created_at"],
-                    "last_updated": data.get("last_updated", data["created_at"]),
-                    "patient_name": data["patient_data"].get("name", "Unknown"),
-                    "message_count": data["message_count"],
-                    "has_pdf": data.get("pdf_filename") is not None
-                })
-        except Exception as e:
-            print(f"Error reading {file_path}: {e}")
-    
-    return sorted(sessions_list, key=lambda x: x["last_updated"], reverse=True)
-
-# =====================================================
-# MEMORY MANAGEMENT (same as before)
-# =====================================================
-
-class ConversationMemory:
-    """Manages short-term memory for each session"""
-    def __init__(self, max_messages: int = 20, session_id: str = None):
-        self.max_messages = max_messages
-        self.history = []
-        self.patient_data = {}
-        self.created_at = datetime.now()
-        self.questions_asked = 0
-        self.session_id = session_id
-        self.pdf_filename = None
-        
-    def add_message(self, role: str, content: str):
-        """Add message to history with memory management"""
-        self.history.append({
-            "role": role,
-            "content": content,
-            "timestamp": datetime.now().isoformat()
-        })
-        
-        if role == "assistant" and "?" in content:
-            self.questions_asked += 1
-        
-        if len(self.history) > self.max_messages:
-            self.history = [self.history[0]] + self.history[-(self.max_messages-1):]
-        
-        if self.session_id:
-            save_session_to_json(self.session_id, self)
-    
-    def extract_patient_info(self, message: str):
-        """Extract and store patient information from conversation"""
-        message_lower = message.lower()
-        
-        if any(word in message_lower for word in ["name is", "i'm", "i am", "im"]):
-            words = message.split()
-            for i, word in enumerate(words):
-                if word.lower() in ["is", "i'm", "am", "im"] and i + 1 < len(words):
-                    self.patient_data["name"] = words[i + 1].strip(".,!?")
-        
-        if "year" in message_lower or "age" in message_lower:
-            import re
-            age_match = re.search(r'\b(\d{1,3})\b', message)
-            if age_match:
-                self.patient_data["age"] = age_match.group(1)
-        
-        if "fever" in message_lower or "pain" in message_lower or "sick" in message_lower:
-            self.patient_data["has_symptoms"] = True
-    
-    def should_give_recommendations(self) -> bool:
-        """Check if we should provide recommendations now"""
-        return (
-            self.questions_asked >= 7 or 
-            self.patient_data.get("has_symptoms", False)
-        )
-    
-    def get_context_summary(self) -> str:
-        """Generate a brief context summary for the AI"""
-        summary = "\n[Session Context: "
-        if "name" in self.patient_data:
-            summary += f"Name: {self.patient_data['name']}, "
-        if "age" in self.patient_data:
-            summary += f"Age: {self.patient_data['age']}, "
-        summary += f"Questions asked: {self.questions_asked}/7, "
-        
-        if self.questions_asked >= 5:
-            summary += "⚠️ IMPORTANT: You've asked enough questions. After the next 1-2 answers, IMMEDIATELY provide comprehensive medical recommendations.]"
-        elif self.questions_asked >= 7:
-            summary += "⚠️ CRITICAL: You MUST provide comprehensive medical recommendations NOW. Do not ask more questions!]"
-        else:
-            summary += f"Ask {7 - self.questions_asked} more essential questions then give recommendations.]"
-        
-        return summary
-    
-    def get_gemini_history(self) -> List[Dict]:
-        """Convert history to Gemini format"""
-        gemini_history = []
-        for msg in self.history:
-            gemini_history.append({
-                "role": "user" if msg["role"] == "user" else "model",
-                "parts": [msg["content"]]
-            })
-        return gemini_history
-    
-    @classmethod
-    def from_json(cls, session_data: Dict) -> 'ConversationMemory':
-        """Create ConversationMemory from JSON data"""
-        memory = cls(session_id=session_data["session_id"])
-        memory.history = session_data["history"]
-        memory.patient_data = session_data["patient_data"]
-        memory.questions_asked = session_data["questions_asked"]
-        memory.created_at = datetime.fromisoformat(session_data["created_at"])
-        memory.pdf_filename = session_data.get("pdf_filename")
-        return memory
-
-sessions: Dict[str, ConversationMemory] = {}
-
-def cleanup_old_sessions():
-    """Remove sessions older than 1 hour from memory"""
-    current_time = datetime.now()
-    expired_sessions = []
-    
-    for session_id, memory in sessions.items():
-        age = (current_time - memory.created_at).total_seconds()
-        if age > 3600:
-            expired_sessions.append(session_id)
-    
-    for session_id in expired_sessions:
-        del sessions[session_id]
-
-# =====================================================
-# FASTAPI APPLICATION
-# =====================================================
-
-app = FastAPI(
-    title="AI Doctor Consultation API with PDF Generation",
-    description="Professional medical consultation API with PDF summary generation",
-    version="3.0.0"
-)
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Pydantic models
-class ChatRequest(BaseModel):
-    session_id: Optional[str] = None
-    message: str
-
-class ChatResponse(BaseModel):
-    session_id: str
-    response: str
-    timestamp: str
-    patient_data: Dict
-
-class SessionRequest(BaseModel):
-    session_id: str
-
-class SummaryResponse(BaseModel):
-    summary: str
-    session_id: str
-    pdf_filename: str
-    pdf_url: str
-
-class HealthCheck(BaseModel):
-    status: str
-    timestamp: str
-    active_sessions: int
-    stored_sessions: int
-    stored_pdfs: int
-
-# =====================================================
-# API ENDPOINTS
-# =====================================================
-
-@app.get("/", response_model=HealthCheck)
-async def root():
-    """Health check endpoint"""
-    cleanup_old_sessions()
-    stored_count = len(list(STORAGE_DIR.glob("*.json")))
-    pdf_count = len(list(PDF_DIR.glob("*.pdf")))
-    return {
-        "status": "healthy",
-        "timestamp": datetime.now().isoformat(),
-        "active_sessions": len(sessions),
-        "stored_sessions": stored_count,
-        "stored_pdfs": pdf_count
-    }
-
-@app.post("/start-session")
-async def start_session():
-    """Start a new consultation session"""
-    session_id = str(uuid.uuid4())
-    sessions[session_id] = ConversationMemory(max_messages=20, session_id=session_id)
-    
-    initial_message = "Hello! I'm Dr. AI Assistant. I'm here to help you today.\n\n👤 May I have your name, please?"
-    
-    sessions[session_id].add_message("assistant", initial_message)
-    
-    return {
-        "session_id": session_id,
-        "message": initial_message,
-        "timestamp": datetime.now().isoformat()
-    }
-
-@app.post("/chat", response_model=ChatResponse)
-async def chat(request: ChatRequest):
-    """Send a message and get doctor's response"""
-    try:
-        if not request.session_id or request.session_id not in sessions:
-            session_id = str(uuid.uuid4())
-            sessions[session_id] = ConversationMemory(max_messages=20, session_id=session_id)
-        else:
-            session_id = request.session_id
-        
-        memory = sessions[session_id]
-        memory.extract_patient_info(request.message)
-        memory.add_message("user", request.message)
-        
-        context = memory.get_context_summary()
-        system_prompt = DOCTOR_SYSTEM_PROMPT + context
-        
-        model = genai.GenerativeModel(
-            model_name=MODEL_ID,
-            system_instruction=system_prompt
-        )
-        
-        chat = model.start_chat(history=memory.get_gemini_history()[:-1])
-        response = chat.send_message(request.message)
-        doctor_response = response.text
-        
-        memory.add_message("assistant", doctor_response)
-        
-        return {
-            "session_id": session_id,
-            "response": doctor_response,
-            "timestamp": datetime.now().isoformat(),
-            "patient_data": memory.patient_data
-        }
-    
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error: {str(e)}")
-
-@app.post("/summary", response_model=SummaryResponse)
-async def generate_summary(request: SessionRequest):
-    """Generate consultation summary and PDF"""
-    if request.session_id not in sessions:
-        session_data = load_session_from_json(request.session_id)
-        if not session_data:
-            raise HTTPException(status_code=404, detail="Session not found")
-        memory = ConversationMemory.from_json(session_data)
-        sessions[request.session_id] = memory
-    else:
-        memory = sessions[request.session_id]
-    
-    summary_request = """Please generate a COMPREHENSIVE and DETAILED medical consultation summary based on our entire conversation. Make it thorough and professional:
-
-📋 **COMPREHENSIVE MEDICAL CONSULTATION SUMMARY**
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-**PATIENT INFORMATION:**
-- Full Name: [Patient's name]
-- Age: [Patient's age if mentioned]
-- Gender: [If mentioned]
-- Consultation Date: [Current date and time]
-- Session Duration: [Approximate]
-- Current Medications: [List all mentioned]
-- Known Allergies: [If mentioned]
-
-**CHIEF COMPLAINTS & SYMPTOMS:**
-[Provide a detailed description of ALL symptoms mentioned, including:]
-- Primary symptom and severity
-- Duration of each symptom
-- Onset and progression
-- Associated symptoms
-- Aggravating and relieving factors
-- Impact on daily activities
-
-**DETAILED MEDICAL HISTORY:**
-[Include everything discussed:]
-- Current medications and dosages
-- Past medical conditions
-- Recent illnesses or infections
-- Family medical history (if mentioned)
-- Lifestyle factors (sleep, stress, diet)
-- Recent travel or exposures
-
-**CLINICAL ASSESSMENT:**
-[Provide detailed analysis:]
-- Most likely diagnosis with explanation
-- Differential diagnoses (2-3 possibilities)
-- Reasoning behind each possibility
-- Risk factors present
-- Severity assessment
-
-**COMPREHENSIVE TREATMENT PLAN:**
-
-1. **IMMEDIATE CARE RECOMMENDATIONS:**
-   - What to do in the next 24-48 hours
-   - Symptom management strategies
-   - Warning signs to watch for
-
-2. **MEDICATION RECOMMENDATIONS:**
-   - Primary medications (generic names, dosages, frequency, duration)
-   - Alternative options if first choice unavailable
-   - Potential side effects to monitor
-   - Drug interactions to avoid
-   - When to take each medication (with/without food)
-   - Important: Check with pharmacist for exact dosing
-
-3. **DETAILED DIETARY RECOMMENDATIONS:**
-   - Foods to eat (specific examples and portions)
-   - Foods to avoid completely
-   - Meal timing and frequency
-   - Hydration guidelines (specific amounts)
-   - Nutritional supplements if needed
-   - Sample meal plan for recovery
-
-4. **LIFESTYLE MODIFICATIONS:**
-   - Sleep recommendations (hours, timing, environment)
-   - Rest and activity balance
-   - Stress management techniques
-   - Environmental modifications
-   - Work/school attendance guidance
-   - Specific activities to avoid
-
-5. **HOME CARE REMEDIES:**
-   - Natural remedies that may help
-   - Temperature management techniques
-   - Pain relief methods
-   - Steam inhalation or other therapies
-   - Specific home treatments for symptoms
-
-6. **EXERCISE & PHYSICAL ACTIVITY:**
-   - Current activity restrictions
-   - Safe exercises during recovery
-   - When to resume normal activities
-   - Gradual activity progression plan
-   - Post-recovery exercise recommendations
-
-7. **PREVENTIVE MEASURES:**
-   - How to prevent recurrence
-   - Hygiene practices
-   - Vaccination recommendations
-   - Family/household precautions
-   - Long-term health maintenance
-
-8. **MONITORING PLAN:**
-   - Symptoms to track daily
-   - How to measure improvement
-   - When improvement should be expected
-   - What to document for doctor visit
-
-**CRITICAL WARNING SIGNS - SEEK IMMEDIATE MEDICAL ATTENTION IF:**
-[List 5-7 specific warning signs that require emergency care:]
-- [Specific symptom with threshold]
-- [Specific symptom with threshold]
-- [Continue with detailed warnings]
-
-**FOLLOW-UP CARE PLAN:**
-- Timeline for self-care (e.g., "Monitor for 48 hours")
-- When to schedule doctor appointment (specific timeframe)
-- What information to bring to doctor
-- Specialist referral recommendations if needed
-- Follow-up testing that may be needed
-
-**PROGNOSIS & EXPECTED RECOVERY:**
-- Expected recovery timeline
-- What to expect during recovery process
-- Signs of improvement to look for
-- Long-term outlook
-
-**ADDITIONAL RESOURCES:**
-- Reputable health information sources
-- Support resources if applicable
-- Emergency contact information reminder
-
-**PATIENT EDUCATION:**
-- Understanding your condition
-- How the body fights this illness
-- Why specific recommendations are important
-- Common misconceptions about this condition
-
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-⚠️ **CRITICAL DISCLAIMER** ⚠️
-This is a preliminary AI-generated consultation for informational and educational purposes ONLY. 
-This is NOT a substitute for professional medical advice, diagnosis, or treatment.
-This AI cannot examine you physically, run laboratory tests, or make definitive diagnoses.
-ALWAYS seek the advice of a qualified, licensed healthcare provider with any questions regarding a medical condition.
-Never disregard professional medical advice or delay seeking it because of this AI consultation.
-In case of emergency, call your local emergency services immediately.
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-Please make this summary as detailed, professional, and helpful as possible. Include specific, actionable advice."""
-    
-    try:
-        model = genai.GenerativeModel(
-            model_name=MODEL_ID,
-            system_instruction=DOCTOR_SYSTEM_PROMPT
-        )
-        
-        chat = model.start_chat(history=memory.get_gemini_history())
-        response = chat.send_message(summary_request)
-        summary_text = response.text
-        
-        # Generate PDF
-        pdf_filename = generate_pdf_summary(
-            request.session_id,
-            summary_text,
-            memory.patient_data,
-            memory.history
-        )
-        
-        # Save PDF filename to memory
-        memory.pdf_filename = pdf_filename
-        save_session_to_json(request.session_id, memory)
-        
-        return {
-            "summary": summary_text,
-            "session_id": request.session_id,
-            "pdf_filename": pdf_filename,
-            "pdf_url": f"/download-pdf/{request.session_id}"
-        }
-    
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error generating summary: {str(e)}")
-
-@app.get("/download-pdf/{session_id}")
-async def download_pdf(session_id: str):
-    """Download PDF summary for a session"""
-    # Check if session exists
-    if session_id in sessions:
-        memory = sessions[session_id]
-    else:
-        session_data = load_session_from_json(session_id)
-        if not session_data:
-            raise HTTPException(status_code=404, detail="Session not found")
-        memory = ConversationMemory.from_json(session_data)
-    
-    if not memory.pdf_filename:
-        raise HTTPException(status_code=404, detail="PDF not generated yet. Please generate summary first.")
-    
-    pdf_path = PDF_DIR / memory.pdf_filename
-    
-    if not pdf_path.exists():
-        raise HTTPException(status_code=404, detail="PDF file not found")
-    
-    patient_name = memory.patient_data.get('name', 'Patient')
-    download_filename = f"Consultation_Summary_{patient_name}_{datetime.now().strftime('%Y%m%d')}.pdf"
-    
-    return FileResponse(
-        path=str(pdf_path),
-        media_type='application/pdf',
-        filename=download_filename
-    )
-
-@app.get("/load-session/{session_id}")
-async def load_session(session_id: str):
-    """Load a previous consultation session by ID"""
-    if session_id in sessions:
-        memory = sessions[session_id]
-        return {
-            "session_id": session_id,
-            "loaded": True,
-            "from_cache": True,
-            "history": memory.history,
-            "patient_data": memory.patient_data,
-            "created_at": memory.created_at.isoformat(),
-            "questions_asked": memory.questions_asked,
-            "has_pdf": memory.pdf_filename is not None,
-            "pdf_url": f"/download-pdf/{session_id}" if memory.pdf_filename else None
-        }
-    
-    session_data = load_session_from_json(session_id)
-    
-    if not session_data:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
-    
-    memory = ConversationMemory.from_json(session_data)
-    sessions[session_id] = memory
-    
-    return {
-        "session_id": session_id,
-        "loaded": True,
-        "from_cache": False,
-        "history": memory.history,
-        "patient_data": memory.patient_data,
-        "created_at": memory.created_at.isoformat(),
-        "questions_asked": memory.questions_asked,
-        "has_pdf": memory.pdf_filename is not None,
-        "pdf_url": f"/download-pdf/{session_id}" if memory.pdf_filename else None,
-        "message": "Session loaded successfully. You can continue the conversation."
-    }
-
-@app.get("/all-sessions")
-async def get_all_sessions():
-    """Get list of all stored consultation sessions"""
-    return {
-        "total_sessions": len(list(STORAGE_DIR.glob("*.json"))),
-        "sessions": list_all_sessions()
-    }
-
-@app.post("/restart-session")
-async def restart_session(request: SessionRequest):
-    """Restart a consultation session"""
-    if request.session_id in sessions:
-        del sessions[request.session_id]
-    
-    sessions[request.session_id] = ConversationMemory(max_messages=20, session_id=request.session_id)
-    
-    initial_message = "Consultation restarted. Hello! I'm Dr. AI Assistant. May I have your name please?"
-    sessions[request.session_id].add_message("assistant", initial_message)
-    
-    return {
-        "session_id": request.session_id,
-        "message": initial_message,
-        "timestamp": datetime.now().isoformat()
-    }
-
-@app.delete("/session/{session_id}")
-async def delete_session(session_id: str):
-    """Delete a consultation session (from memory, JSON, and PDF)"""
-    if session_id in sessions:
-        memory = sessions[session_id]
-        pdf_filename = memory.pdf_filename
-        del sessions[session_id]
-    else:
-        session_data = load_session_from_json(session_id)
-        pdf_filename = session_data.get('pdf_filename') if session_data else None
-    
-    # Remove JSON file
-    file_path = STORAGE_DIR / f"{session_id}.json"
-    if file_path.exists():
-        file_path.unlink()
-    
-    # Remove PDF file if exists
-    if pdf_filename:
-        pdf_path = PDF_DIR / pdf_filename
-        if pdf_path.exists():
-            pdf_path.unlink()
-    
-    return {"message": "Session and associated files deleted successfully"}
-
-@app.get("/session/{session_id}/history")
-async def get_session_history(session_id: str):
-    """Get conversation history for a session"""
-    if session_id in sessions:
-        memory = sessions[session_id]
-    else:
-        session_data = load_session_from_json(session_id)
-        if not session_data:
-            raise HTTPException(status_code=404, detail="Session not found")
-        memory = ConversationMemory.from_json(session_data)
-    
-    return {
-        "session_id": session_id,
-        "history": memory.history,
-        "patient_data": memory.patient_data,
-        "created_at": memory.created_at.isoformat(),
-        "questions_asked": memory.questions_asked,
-        "has_pdf": memory.pdf_filename is not None
-    }
-
-@app.get("/active-sessions")
-async def get_active_sessions():
-    """Get list of all active sessions in memory"""
-    cleanup_old_sessions()
-    return {
-        "active_sessions": len(sessions),
-        "sessions": [
-            {
-                "session_id": sid,
-                "created_at": mem.created_at.isoformat(),
-                "message_count": len(mem.history),
-                "questions_asked": mem.questions_asked,
-                "patient_data": mem.patient_data,
-                "has_pdf": mem.pdf_filename is not None
-            }
-            for sid, mem in sessions.items()
-        ]
-    }
-
-if __name__ == "__main__":
-    import uvicorn
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse
+from pydantic import BaseModel
+from typing import List, Optional, Dict
+import google.generativeai as genai
+import os
+from datetime import datetime
+import uuid
+import json
+from pathlib import Path
+from reportlab.lib.pagesizes import letter, A4
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib.units import inch
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak, Table, TableStyle
+from reportlab.lib import colors
+from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
+from reportlab.pdfgen import canvas
+
+# Configure Gemini API
+os.environ["GOOGLE_API_KEY"] = "AIzaSyDTyKE4apGmzAi38CrWvjdVJ1vV6fdm-w8"
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+MODEL_ID = "gemini-2.0-flash-exp"
+
+# Create storage directories
+STORAGE_DIR = Path("consultation_storage")
+STORAGE_DIR.mkdir(exist_ok=True)
+
+PDF_DIR = Path("consultation_pdfs")
+PDF_DIR.mkdir(exist_ok=True)
+
+# System prompt (same as before)
+DOCTOR_SYSTEM_PROMPT = """
+You are Dr. HealBot, a calm, knowledgeable, and empathetic virtual doctor.
+
+GOAL:
+Hold a natural, focused conversation with the patient to understand their health issue and offer helpful preliminary medical guidance.
+
+CONVERSATION LOGIC:
+- Ask only relevant and concise medical questions necessary for diagnosing the illness.
+- Each question should help clarify symptoms or narrow possible causes.
+- Stop asking once enough information is collected for a basic assessment.
+- Then, provide a structured, friendly, and visually clear medical response using headings, emojis, and bullet points.
+
+FINAL RESPONSE FORMAT:
+When giving your full assessment, use this markdown-styled format:
+
+🩺 Based on what you've told me...
+Brief summary of what the patient described.
+
+💡 Possible Causes (Preliminary)
+- List 1–2 possible conditions using phrases like "It could be" or "This sounds like".
+- Include a disclaimer that this is not a confirmed diagnosis.
+
+💊 Suggested Over-the-Counter Medicines
+- Generic medicine names only (e.g., "Paracetamol 500mg every 6 hours if fever or pain")
+- Mention to check packaging or consult a pharmacist for dosage confirmation.
+
+🥗 Lifestyle & Home Care Tips
+- 2–3 practical suggestions (rest, hydration, warm compress, balanced diet, etc.)
+
+⚠ When to See a Real Doctor
+- 2–3 warning signs or conditions when urgent medical care is needed.
+
+📅 Follow-Up Advice
+- Brief recommendation for self-care or follow-up timing (e.g., "If not improving in 3 days, visit a clinic.")
+
+TONE & STYLE:
+- Speak like a real, caring doctor — short, clear, and empathetic (1–2 sentences per reply).
+- Use plain language, no jargon.
+- Only one question per turn unless clarification is essential.
+- Keep tone warm, calm, and professional.
+- Early messages: short questions only.
+- Final message: structured output with emojis and headings.
+
+IMPORTANT:
+- Always emphasize that this is preliminary guidance and not a substitute for professional care.
+- Never make definitive diagnoses; use phrases like "it sounds like" or "it could be".
+- If symptoms seem serious, always recommend urgent medical attention.
+
+CONVERSATION FLOW:
+1. Ask about the main symptom.
+2. Ask about its duration, severity, and any triggers.
+3. Ask about accompanying symptoms.
+4. Ask about medical history, allergies, or medications.
+5. Then, provide your structured assessment as described above.
+"""
+
+# =====================================================
+# PDF GENERATION FUNCTIONS
+# =====================================================
+
+def generate_pdf_summary(session_id: str, summary_text: str, patient_data: Dict, history: List[Dict]) -> str:
+    """Generate a professional PDF summary of the consultation"""
+    
+    pdf_filename = f"{session_id}_summary.pdf"
+    pdf_path = PDF_DIR / pdf_filename
+    
+    # Create PDF document
+    doc = SimpleDocTemplate(str(pdf_path), pagesize=letter,
+                           rightMargin=72, leftMargin=72,
+                           topMargin=72, bottomMargin=18)
+    
+    # Container for the 'Flowable' objects
+    elements = []
+    
+    # Define styles
+    styles = getSampleStyleSheet()
+    
+    # Custom styles
+    title_style = ParagraphStyle(
+        'CustomTitle',
+        parent=styles['Heading1'],
+        fontSize=24,
+        textColor=colors.HexColor('#667eea'),
+        spaceAfter=30,
+        alignment=TA_CENTER,
+        fontName='Helvetica-Bold'
+    )
+    
+    heading_style = ParagraphStyle(
+        'CustomHeading',
+        parent=styles['Heading2'],
+        fontSize=16,
+        textColor=colors.HexColor('#667eea'),
+        spaceAfter=12,
+        spaceBefore=12,
+        fontName='Helvetica-Bold'
+    )
+    
+    normal_style = ParagraphStyle(
+        'CustomNormal',
+        parent=styles['Normal'],
+        fontSize=11,
+        spaceAfter=12,
+        alignment=TA_JUSTIFY,
+        leading=14
+    )
+    
+    # Add Title
+    elements.append(Paragraph("🩺 AI DOCTOR CONSULTATION SUMMARY", title_style))
+    elements.append(Spacer(1, 0.3*inch))
+    
+    # Add horizontal line
+    elements.append(Spacer(1, 0.1*inch))
+    
+    # Patient Information Table
+    patient_info_data = [
+        ['Patient Name:', patient_data.get('name', 'N/A')],
+        ['Age:', patient_data.get('age', 'N/A')],
+        ['Session ID:', session_id[:20] + '...'],
+        ['Consultation Date:', datetime.now().strftime('%B %d, %Y at %I:%M %p')],
+        ['Total Messages:', str(len(history))]
+    ]
+    
+    patient_table = Table(patient_info_data, colWidths=[2*inch, 4*inch])
+    patient_table.setStyle(TableStyle([
+        ('BACKGROUND', (0, 0), (0, -1), colors.HexColor('#f0f0f0')),
+        ('TEXTCOLOR', (0, 0), (-1, -1), colors.black),
+        ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
+        ('FONTNAME', (0, 0), (0, -1), 'Helvetica-Bold'),
+        ('FONTNAME', (1, 0), (1, -1), 'Helvetica'),
+        ('FONTSIZE', (0, 0), (-1, -1), 10),
+        ('BOTTOMPADDING', (0, 0), (-1, -1), 8),
+        ('TOPPADDING', (0, 0), (-1, -1), 8),
+        ('GRID', (0, 0), (-1, -1), 1, colors.grey)
+    ]))
+    
+    elements.append(patient_table)
+    elements.append(Spacer(1, 0.3*inch))
+    
+    # Add Consultation Summary
+    elements.append(Paragraph("CONSULTATION SUMMARY", heading_style))
+    
+    # Process summary text - split by lines and convert to paragraphs
+    summary_lines = summary_text.split('\n')
+    for line in summary_lines:
+        if line.strip():
+            # Replace emojis with text equivalents for PDF compatibility
+            line = line.replace('🩺', '[Medical] ')
+            line = line.replace('💡', '[Insight] ')
+            line = line.replace('💊', '[Medicine] ')
+            line = line.replace('🥗', '[Lifestyle] ')
+            line = line.replace('⚠️', '[Warning] ')
+            line = line.replace('⚠', '[Warning] ')
+            line = line.replace('📅', '[Follow-up] ')
+            line = line.replace('━', '-')
+            
+            # Check if it's a heading (starts with **)
+            if line.strip().startswith('**') and line.strip().endswith('**'):
+                elements.append(Paragraph(line.strip('*'), heading_style))
+            else:
+                elements.append(Paragraph(line, normal_style))
+    
+    elements.append(Spacer(1, 0.3*inch))
+    
+    # Add Conversation History
+    elements.append(PageBreak())
+    elements.append(Paragraph("CONVERSATION HISTORY", heading_style))
+    elements.append(Spacer(1, 0.2*inch))
+    
+    for i, msg in enumerate(history, 1):
+        role = "DOCTOR" if msg['role'] == 'assistant' else "PATIENT"
+        timestamp = msg.get('timestamp', 'N/A')
+        
+        role_style = ParagraphStyle(
+            f'Role{i}',
+            parent=styles['Normal'],
+            fontSize=10,
+            textColor=colors.HexColor('#667eea') if role == "DOCTOR" else colors.HexColor('#28a745'),
+            fontName='Helvetica-Bold',
+            spaceAfter=4
+        )
+        
+        elements.append(Paragraph(f"{role} ({timestamp}):", role_style))
+        
+        content = msg['content'].replace('🩺', '').replace('💡', '').replace('💊', '')
+        content = content.replace('🥗', '').replace('⚠️', '').replace('⚠', '').replace('📅', '')
+        elements.append(Paragraph(content, normal_style))
+        elements.append(Spacer(1, 0.15*inch))
+    
+    # Add disclaimer at the end
+    elements.append(Spacer(1, 0.3*inch))
+    
+    disclaimer_style = ParagraphStyle(
+        'Disclaimer',
+        parent=styles['Normal'],
+        fontSize=9,
+        textColor=colors.red,
+        alignment=TA_CENTER,
+        fontName='Helvetica-Bold',
+        borderColor=colors.red,
+        borderWidth=1,
+        borderPadding=10,
+        spaceAfter=12
+    )
+    
+    elements.append(Paragraph(
+        "⚠ IMPORTANT DISCLAIMER ⚠<br/>" +
+        "This is a preliminary AI-generated consultation for informational purposes only.<br/>" +
+        "It is NOT a substitute for professional medical advice, diagnosis, or treatment.<br/>" +
+        "Always seek the advice of a qualified healthcare provider with any questions regarding a medical condition.",
+        disclaimer_style
+    ))
+    
+    # Build PDF
+    doc.build(elements)
+    
+    return pdf_filename
+
+# =====================================================
+# STORAGE FUNCTIONS (same as before)
+# =====================================================
+
+def save_session_to_json(session_id: str, memory: 'ConversationMemory'):
+    """Save session data to JSON file"""
+    file_path = STORAGE_DIR / f"{session_id}.json"
+    
+    session_data = {
+        "session_id": session_id,
+        "created_at": memory.created_at.isoformat(),
+        "last_updated": datetime.now().isoformat(),
+        "patient_data": memory.patient_data,
+        "questions_asked": memory.questions_asked,
+        "history": memory.history,
+        "message_count": len(memory.history),
+        "pdf_filename": getattr(memory, 'pdf_filename', None)
+    }
+    
+    with open(file_path, 'w', encoding='utf-8') as f:
+        json.dump(session_data, f, indent=2, ensure_ascii=False)
+
+def load_session_from_json(session_id: str) -> Optional[Dict]:
+    """Load session data from JSON file"""
+    file_path = STORAGE_DIR / f"{session_id}.json"
+    
+    if not file_path.exists():
+        return None
+    
+    with open(file_path, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+def list_all_sessions() -> List[Dict]:
+    """List all stored sessions"""
+    sessions_list = []
+    
+    for file_path in STORAGE_DIR.glob("*.json"):
+        try:
+            with open(file_path, 'r', encoding='utf-8') as f:
+                data = json.load(f)
+                sessions_list.append({
+                    "session_id": data["session_id"],
+                    "created_at": data["created_at"],
+                    "last_updated": data.get("last_updated", data["created_at"]),
+                    "patient_name": data["patient_data"].get("name", "Unknown"),
+                    "message_count": data["message_count"],
+                    "has_pdf": data.get("pdf_filename") is not None
+                })
+        except Exception as e:
+            print(f"Error reading {file_path}: {e}")
+    
+    return sorted(sessions_list, key=lambda x: x["last_updated"], reverse=True)
+
+# =====================================================
+# MEMORY MANAGEMENT (same as before)
+# =====================================================
+
+class ConversationMemory:
+    """Manages short-term memory for each session"""
+    def __init__(self, max_messages: int = 20, session_id: str = None):
+        self.max_messages = max_messages
+        self.history = []
+        self.patient_data = {}
+        self.created_at = datetime.now()
+        self.questions_asked = 0
+        self.session_id = session_id
+        self.pdf_filename = None
+        
+    def add_message(self, role: str, content: str):
+        """Add message to history with memory management"""
+        self.history.append({
+            "role": role,
+            "content": content,
+            "timestamp": datetime.now().isoformat()
+        })
+        
+        if role == "assistant" and "?" in content:
+            self.questions_asked += 1
+        
+        if len(self.history) > self.max_messages:
+            self.history = [self.history[0]] + self.history[-(self.max_messages-1):]
+        
+        if self.session_id:
+            save_session_to_json(self.session_id, self)
+    
+    def extract_patient_info(self, message: str):
+        """Extract and store patient information from conversation"""
+        message_lower = message.lower()
+        
+        if any(word in message_lower for word in ["name is", "i'm", "i am", "im"]):
+            words = message.split()
+            for i, word in enumerate(words):
+                if word.lower() in ["is", "i'm", "am", "im"] and i + 1 < len(words):
+                    self.patient_data["name"] = words[i + 1].strip(".,!?")
+        
+        if "year" in message_lower or "age" in message_lower:
+            import re
+            age_match = re.search(r'\b(\d{1,3})\b', message)
+            if age_match:
+                self.patient_data["age"] = age_match.group(1)
+        
+        if "fever" in message_lower or "pain" in message_lower or "sick" in message_lower:
+            self.patient_data["has_symptoms"] = True
+    
+    def should_give_recommendations(self) -> bool:
+        """Check if we should provide recommendations now"""
+        return (
+            self.questions_asked >= 7 or 
+            self.patient_data.get("has_symptoms", False)
+        )
+    
+    def get_context_summary(self) -> str:
+        """Generate a brief context summary for the AI"""
+        summary = "\n[Session Context: "
+        if "name" in self.patient_data:
+            summary += f"Name: {self.patient_data['name']}, "
+        if "age" in self.patient_data:
+            summary += f"Age: {self.patient_data['age']}, "
+        summary += f"Questions asked: {self.questions_asked}/7, "
+        
+        if self.questions_asked >= 5:
+            summary += "⚠️ IMPORTANT: You've asked enough questions. After the next 1-2 answers, IMMEDIATELY provide comprehensive medical recommendations.]"
+        elif self.questions_asked >= 7:
+            summary += "⚠️ CRITICAL: You MUST provide comprehensive medical recommendations NOW. Do not ask more questions!]"
+        else:
+            summary += f"Ask {7 - self.questions_asked} more essential questions then give recommendations.]"
+        
+        return summary
+    
+    def get_gemini_history(self) -> List[Dict]:
+        """Convert history to Gemini format"""
+        gemini_history = []
+        for msg in self.history:
+            gemini_history.append({
+                "role": "user" if msg["role"] == "user" else "model",
+                "parts": [msg["content"]]
+            })
+        return gemini_history
+    
+    @classmethod
+    def from_json(cls, session_data: Dict) -> 'ConversationMemory':
+        """Create ConversationMemory from JSON data"""
+        memory = cls(session_id=session_data["session_id"])
+        memory.history = session_data["history"]
+        memory.patient_data = session_data["patient_data"]
+        memory.questions_asked = session_data["questions_asked"]
+        memory.created_at = datetime.fromisoformat(session_data["created_at"])
+        memory.pdf_filename = session_data.get("pdf_filename")
+        return memory
+
+sessions: Dict[str, ConversationMemory] = {}
+
+def cleanup_old_sessions():
+    """Remove sessions older than 1 hour from memory"""
+    current_time = datetime.now()
+    expired_sessions = []
+    
+    for session_id, memory in sessions.items():
+        age = (current_time - memory.created_at).total_seconds()
+        if age > 3600:
+            expired_sessions.append(session_id)
+    
+    for session_id in expired_sessions:
+        del sessions[session_id]
+
+# =====================================================
+# FASTAPI APPLICATION
+# =====================================================
+
+app = FastAPI(
+    title="AI Doctor Consultation API with PDF Generation",
+    description="Professional medical consultation API with PDF summary generation",
+    version="3.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Pydantic models
+class ChatRequest(BaseModel):
+    session_id: Optional[str] = None
+    message: str
+
+class ChatResponse(BaseModel):
+    session_id: str
+    response: str
+    timestamp: str
+    patient_data: Dict
+
+class SessionRequest(BaseModel):
+    session_id: str
+
+class SummaryResponse(BaseModel):
+    summary: str
+    session_id: str
+    pdf_filename: str
+    pdf_url: str
+
+class HealthCheck(BaseModel):
+    status: str
+    timestamp: str
+    active_sessions: int
+    stored_sessions: int
+    stored_pdfs: int
+
+# =====================================================
+# API ENDPOINTS
+# =====================================================
+
+@app.get("/", response_model=HealthCheck)
+async def root():
+    """Health check endpoint"""
+    cleanup_old_sessions()
+    stored_count = len(list(STORAGE_DIR.glob("*.json")))
+    pdf_count = len(list(PDF_DIR.glob("*.pdf")))
+    return {
+        "status": "healthy",
+        "timestamp": datetime.now().isoformat(),
+        "active_sessions": len(sessions),
+        "stored_sessions": stored_count,
+        "stored_pdfs": pdf_count
+    }
+
+@app.post("/start-session")
+async def start_session():
+    """Start a new consultation session"""
+    session_id = str(uuid.uuid4())
+    sessions[session_id] = ConversationMemory(max_messages=20, session_id=session_id)
+    
+    initial_message = "Hello! I'm Dr. AI Assistant. I'm here to help you today.\n\n👤 May I have your name, please?"
+    
+    sessions[session_id].add_message("assistant", initial_message)
+    
+    return {
+        "session_id": session_id,
+        "message": initial_message,
+        "timestamp": datetime.now().isoformat()
+    }
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    """Send a message and get doctor's response"""
+    try:
+        if not request.session_id or request.session_id not in sessions:
+            session_id = str(uuid.uuid4())
+            sessions[session_id] = ConversationMemory(max_messages=20, session_id=session_id)
+        else:
+            session_id = request.session_id
+        
+        memory = sessions[session_id]
+        memory.extract_patient_info(request.message)
+        memory.add_message("user", request.message)
+        
+        context = memory.get_context_summary()
+        system_prompt = DOCTOR_SYSTEM_PROMPT + context
+        
+        model = genai.GenerativeModel(
+            model_name=MODEL_ID,
+            system_instruction=system_prompt
+        )
+        
+        chat = model.start_chat(history=memory.get_gemini_history()[:-1])
+        response = chat.send_message(request.message)
+        doctor_response = response.text
+        
+        memory.add_message("assistant", doctor_response)
+        
+        return {
+            "session_id": session_id,
+            "response": doctor_response,
+            "timestamp": datetime.now().isoformat(),
+            "patient_data": memory.patient_data
+        }
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error: {str(e)}")
+
+@app.post("/summary", response_model=SummaryResponse)
+async def generate_summary(request: SessionRequest):
+    """Generate consultation summary and PDF"""
+    if request.session_id not in sessions:
+        session_data = load_session_from_json(request.session_id)
+        if not session_data:
+            raise HTTPException(status_code=404, detail="Session not found")
+        memory = ConversationMemory.from_json(session_data)
+        sessions[request.session_id] = memory
+    else:
+        memory = sessions[request.session_id]
+    
+    summary_request = """Please generate a COMPREHENSIVE and DETAILED medical consultation summary based on our entire conversation. Make it thorough and professional:
+
+📋 **COMPREHENSIVE MEDICAL CONSULTATION SUMMARY**
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+
+**PATIENT INFORMATION:**
+- Full Name: [Patient's name]
+- Age: [Patient's age if mentioned]
+- Gender: [If mentioned]
+- Consultation Date: [Current date and time]
+- Session Duration: [Approximate]
+- Current Medications: [List all mentioned]
+- Known Allergies: [If mentioned]
+
+**CHIEF COMPLAINTS & SYMPTOMS:**
+[Provide a detailed description of ALL symptoms mentioned, including:]
+- Primary symptom and severity
+- Duration of each symptom
+- Onset and progression
+- Associated symptoms
+- Aggravating and relieving factors
+- Impact on daily activities
+
+**DETAILED MEDICAL HISTORY:**
+[Include everything discussed:]
+- Current medications and dosages
+- Past medical conditions
+- Recent illnesses or infections
+- Family medical history (if mentioned)
+- Lifestyle factors (sleep, stress, diet)
+- Recent travel or exposures
+
+**CLINICAL ASSESSMENT:**
+[Provide detailed analysis:]
+- Most likely diagnosis with explanation
+- Differential diagnoses (2-3 possibilities)
+- Reasoning behind each possibility
+- Risk factors present
+- Severity assessment
+
+**COMPREHENSIVE TREATMENT PLAN:**
+
+1. **IMMEDIATE CARE RECOMMENDATIONS:**
+   - What to do in the next 24-48 hours
+   - Symptom management strategies
+   - Warning signs to watch for
+
+2. **MEDICATION RECOMMENDATIONS:**
+   - Primary medications (generic names, dosages, frequency, duration)
+   - Alternative options if first choice unavailable
+   - Potential side effects to monitor
+   - Drug interactions to avoid
+   - When to take each medication (with/without food)
+   - Important: Check with pharmacist for exact dosing
+
+3. **DETAILED DIETARY RECOMMENDATIONS:**
+   - Foods to eat (specific examples and portions)
+   - Foods to avoid completely
+   - Meal timing and frequency
+   - Hydration guidelines (specific amounts)
+   - Nutritional supplements if needed
+   - Sample meal plan for recovery
+
+4. **LIFESTYLE MODIFICATIONS:**
+   - Sleep recommendations (hours, timing, environment)
+   - Rest and activity balance
+   - Stress management techniques
+   - Environmental modifications
+   - Work/school attendance guidance
+   - Specific activities to avoid
+
+5. **HOME CARE REMEDIES:**
+   - Natural remedies that may help
+   - Temperature management techniques
+   - Pain relief methods
+   - Steam inhalation or other therapies
+   - Specific home treatments for symptoms
+
+6. **EXERCISE & PHYSICAL ACTIVITY:**
+   - Current activity restrictions
+   - Safe exercises during recovery
+   - When to resume normal activities
+   - Gradual activity progression plan
+   - Post-recovery exercise recommendations
+
+7. **PREVENTIVE MEASURES:**
+   - How to prevent recurrence
+   - Hygiene practices
+   - Vaccination recommendations
+   - Family/household precautions
+   - Long-term health maintenance
+
+8. **MONITORING PLAN:**
+   - Symptoms to track daily
+   - How to measure improvement
+   - When improvement should be expected
+   - What to document for doctor visit
+
+**CRITICAL WARNING SIGNS - SEEK IMMEDIATE MEDICAL ATTENTION IF:**
+[List 5-7 specific warning signs that require emergency care:]
+- [Specific symptom with threshold]
+- [Specific symptom with threshold]
+- [Continue with detailed warnings]
+
+**FOLLOW-UP CARE PLAN:**
+- Timeline for self-care (e.g., "Monitor for 48 hours")
+- When to schedule doctor appointment (specific timeframe)
+- What information to bring to doctor
+- Specialist referral recommendations if needed
+- Follow-up testing that may be needed
+
+**PROGNOSIS & EXPECTED RECOVERY:**
+- Expected recovery timeline
+- What to expect during recovery process
+- Signs of improvement to look for
+- Long-term outlook
+
+**ADDITIONAL RESOURCES:**
+- Reputable health information sources
+- Support resources if applicable
+- Emergency contact information reminder
+
+**PATIENT EDUCATION:**
+- Understanding your condition
+- How the body fights this illness
+- Why specific recommendations are important
+- Common misconceptions about this condition
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+⚠️ **CRITICAL DISCLAIMER** ⚠️
+This is a preliminary AI-generated consultation for informational and educational purposes ONLY. 
+This is NOT a substitute for professional medical advice, diagnosis, or treatment.
+This AI cannot examine you physically, run laboratory tests, or make definitive diagnoses.
+ALWAYS seek the advice of a qualified, licensed healthcare provider with any questions regarding a medical condition.
+Never disregard professional medical advice or delay seeking it because of this AI consultation.
+In case of emergency, call your local emergency services immediately.
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+
+Please make this summary as detailed, professional, and helpful as possible. Include specific, actionable advice."""
+    
+    try:
+        model = genai.GenerativeModel(
+            model_name=MODEL_ID,
+            system_instruction=DOCTOR_SYSTEM_PROMPT
+        )
+        
+        chat = model.start_chat(history=memory.get_gemini_history())
+        response = chat.send_message(summary_request)
+        summary_text = response.text
+        
+        # Generate PDF
+        pdf_filename = generate_pdf_summary(
+            request.session_id,
+            summary_text,
+            memory.patient_data,
+            memory.history
+        )
+        
+        # Save PDF filename to memory
+        memory.pdf_filename = pdf_filename
+        save_session_to_json(request.session_id, memory)
+        
+        return {
+            "summary": summary_text,
+            "session_id": request.session_id,
+            "pdf_filename": pdf_filename,
+            "pdf_url": f"/download-pdf/{request.session_id}"
+        }
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error generating summary: {str(e)}")
+
+@app.get("/download-pdf/{session_id}")
+async def download_pdf(session_id: str):
+    """Download PDF summary for a session"""
+    # Check if session exists
+    if session_id in sessions:
+        memory = sessions[session_id]
+    else:
+        session_data = load_session_from_json(session_id)
+        if not session_data:
+            raise HTTPException(status_code=404, detail="Session not found")
+        memory = ConversationMemory.from_json(session_data)
+    
+    if not memory.pdf_filename:
+        raise HTTPException(status_code=404, detail="PDF not generated yet. Please generate summary first.")
+    
+    pdf_path = PDF_DIR / memory.pdf_filename
+    
+    if not pdf_path.exists():
+        raise HTTPException(status_code=404, detail="PDF file not found")
+    
+    patient_name = memory.patient_data.get('name', 'Patient')
+    download_filename = f"Consultation_Summary_{patient_name}_{datetime.now().strftime('%Y%m%d')}.pdf"
+    
+    return FileResponse(
+        path=str(pdf_path),
+        media_type='application/pdf',
+        filename=download_filename
+    )
+
+@app.get("/load-session/{session_id}")
+async def load_session(session_id: str):
+    """Load a previous consultation session by ID"""
+    if session_id in sessions:
+        memory = sessions[session_id]
+        return {
+            "session_id": session_id,
+            "loaded": True,
+            "from_cache": True,
+            "history": memory.history,
+            "patient_data": memory.patient_data,
+            "created_at": memory.created_at.isoformat(),
+            "questions_asked": memory.questions_asked,
+            "has_pdf": memory.pdf_filename is not None,
+            "pdf_url": f"/download-pdf/{session_id}" if memory.pdf_filename else None
+        }
+    
+    session_data = load_session_from_json(session_id)
+    
+    if not session_data:
+        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
+    
+    memory = ConversationMemory.from_json(session_data)
+    sessions[session_id] = memory
+    
+    return {
+        "session_id": session_id,
+        "loaded": True,
+        "from_cache": False,
+        "history": memory.history,
+        "patient_data": memory.patient_data,
+        "created_at": memory.created_at.isoformat(),
+        "questions_asked": memory.questions_asked,
+        "has_pdf": memory.pdf_filename is not None,
+        "pdf_url": f"/download-pdf/{session_id}" if memory.pdf_filename else None,
+        "message": "Session loaded successfully. You can continue the conversation."
+    }
+
+@app.get("/all-sessions")
+async def get_all_sessions():
+    """Get list of all stored consultation sessions"""
+    return {
+        "total_sessions": len(list(STORAGE_DIR.glob("*.json"))),
+        "sessions": list_all_sessions()
+    }
+
+@app.post("/restart-session")
+async def restart_session(request: SessionRequest):
+    """Restart a consultation session"""
+    if request.session_id in sessions:
+        del sessions[request.session_id]
+    
+    sessions[request.session_id] = ConversationMemory(max_messages=20, session_id=request.session_id)
+    
+    initial_message = "Consultation restarted. Hello! I'm Dr. AI Assistant. May I have your name please?"
+    sessions[request.session_id].add_message("assistant", initial_message)
+    
+    return {
+        "session_id": request.session_id,
+        "message": initial_message,
+        "timestamp": datetime.now().isoformat()
+    }
+
+@app.delete("/session/{session_id}")
+async def delete_session(session_id: str):
+    """Delete a consultation session (from memory, JSON, and PDF)"""
+    if session_id in sessions:
+        memory = sessions[session_id]
+        pdf_filename = memory.pdf_filename
+        del sessions[session_id]
+    else:
+        session_data = load_session_from_json(session_id)
+        pdf_filename = session_data.get('pdf_filename') if session_data else None
+    
+    # Remove JSON file
+    file_path = STORAGE_DIR / f"{session_id}.json"
+    if file_path.exists():
+        file_path.unlink()
+    
+    # Remove PDF file if exists
+    if pdf_filename:
+        pdf_path = PDF_DIR / pdf_filename
+        if pdf_path.exists():
+            pdf_path.unlink()
+    
+    return {"message": "Session and associated files deleted successfully"}
+
+@app.get("/session/{session_id}/history")
+async def get_session_history(session_id: str):
+    """Get conversation history for a session"""
+    if session_id in sessions:
+        memory = sessions[session_id]
+    else:
+        session_data = load_session_from_json(session_id)
+        if not session_data:
+            raise HTTPException(status_code=404, detail="Session not found")
+        memory = ConversationMemory.from_json(session_data)
+    
+    return {
+        "session_id": session_id,
+        "history": memory.history,
+        "patient_data": memory.patient_data,
+        "created_at": memory.created_at.isoformat(),
+        "questions_asked": memory.questions_asked,
+        "has_pdf": memory.pdf_filename is not None
+    }
+
+@app.get("/active-sessions")
+async def get_active_sessions():
+    """Get list of all active sessions in memory"""
+    cleanup_old_sessions()
+    return {
+        "active_sessions": len(sessions),
+        "sessions": [
+            {
+                "session_id": sid,
+                "created_at": mem.created_at.isoformat(),
+                "message_count": len(mem.history),
+                "questions_asked": mem.questions_asked,
+                "patient_data": mem.patient_data,
+                "has_pdf": mem.pdf_filename is not None
+            }
+            for sid, mem in sessions.items()
+        ]
+    }
+
+if __name__ == "__main__":
+    import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=8000)