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Reference/aboutdata.txt

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+Columns in the dataset:
+['Name', 'Age', 'City', 'Profession', 'Salary', 'Expenses', 'Savings', 'Lifecycle Stage', 'Risk Appetite', 'Investment Horizon', 'Equity (%)', 'Debt (%)', 'Gold (%)', 'FD/Cash (%)']
+
+Preview of the data:
+           Name  Age       City         Profession  Salary  Expenses  Savings  \
+0  Rohan Sharma   28     Mumbai  Software Engineer   85000     65000    20000   
+1   Priya Patel   45      Delhi             Doctor  220000    150000    70000   
+2   Arjun Singh   60  Bangalore     Retired Banker   45000     40000     5000   
+3   Anika Reddy   22  Hyderabad            Student   15000     14000     1000   
+4  Vikram Mehta   35       Pune  Marketing Manager  140000    100000    40000   
+
+  Lifecycle Stage Risk Appetite Investment Horizon  Equity (%)  Debt (%)  \
+0    Early Career        Medium          Long-term          45        25   
+1      Mid-Career        Medium        Medium-term          40        30   
+2         Retired           Low         Short-term          10        40   
+3         Student           Low         Short-term           0        30   
+4    Early Career          High          Long-term          70        15   
+
+   Gold (%)  FD/Cash (%)  
+0        10           20  
+1        10           20  
+2        15           35  
+3         5           65  
+4         5           10  
+
+Data types:
+Name                  object
+Age                    int64
+City                  object
+Profession            object
+Salary                 int64
+Expenses               int64
+Savings                int64
+Lifecycle Stage       object
+Risk Appetite         object
+Investment Horizon    object
+Equity (%)             int64
+Debt (%)               int64
+Gold (%)               int64
+FD/Cash (%)            int64
+dtype: object
+
+Unique values in 'City':
+['Mumbai' 'Delhi' 'Bangalore' 'Hyderabad' 'Pune' 'Chennai' 'Jaipur'
+ 'Kochi' 'Kolkata' 'Ahmedabad' 'Gurgaon' 'Lucknow' 'Nagpur' 'Chandigarh'
+ 'Surat' 'Indore' 'Bhopal']
+
+Unique values in 'Profession':
+['Software Engineer' 'Doctor' 'Retired Banker' 'Student'
+ 'Marketing Manager' 'Teacher' 'Freelancer' 'Architect' 'Business Owner'
+ 'Nurse' 'Product Manager' 'CA' 'Data Analyst' 'Retired Professor'
+ 'Journalist' 'Graphic Designer' 'Sales Executive' 'HR Manager' 'Intern'
+ 'Dentist' 'Lawyer' 'Content Creator' 'Pensioner' 'UX Designer'
+ 'Government Employee' 'Fashion Designer' 'Startup Founder'
+ 'Homemaker (Investor)' 'Investment Banker' 'IT Consultant'
+ 'College Student' 'Pharmacist' 'Textile Business Owner' 'Event Planner'
+ 'Film Producer' 'Nutritionist' 'Retired Army Officer'
+ 'Social Media Manager' 'Airline Pilot' 'Biotech Researcher'
+ 'Real Estate Agent' 'AI Engineer' 'Retired Teacher' 'Financial Analyst'
+ 'Software Developer' 'NGO Director' 'Fitness Trainer' 'Digital Marketer'
+ 'Content Strategist' 'Retired Engineer' 'UI Developer'
+ 'Operations Manager' 'Corporate Lawyer' 'School Principal'
+ 'AI Researcher' 'Junior Doctor' 'Finance Manager' 'Fashion Blogger'
+ 'HR Consultant' 'Video Editor' 'Small Business Owner' 'Dietitian'
+ 'Supply Chain Manager' 'Interior Designer' 'Sales Manager' 'PR Executive'
+ 'Logistics Head' 'Content Writer' 'Retired Govt. Employee'
+ 'Marketing Lead' 'IT Manager' 'Startup Intern' 'Export Manager'
+ 'Fitness Instructor' 'Real Estate Broker' 'Event Manager'
+ 'Software Trainee' 'Data Scientist' 'HR Director' 'Hotel Manager'
+ 'Social Worker' 'Cybersecurity Expert' 'E-commerce Manager'
+ 'Bank Manager' 'Retired IT Manager' 'UX Researcher' 'Product Designer'
+ 'Cybersecurity Analyst' 'Podcast Producer' 'E-commerce Seller'
+ 'Cloud Architect' 'Corporate Trainer' 'AI Trainer' 'Supply Chain Head'
+ 'Product Owner' 'UI/UX Designer' 'Finance Director'
+ 'Sustainability Consultant' 'Retired Bank Manager'
+ 'Social Media Influencer' 'Blockchain Developer' 'NGO Head'
+ 'Data Engineer' 'Event Curator' 'CTO' 'Content Marketer'
+ 'Retired Army Major' 'AR/VR Developer' 'Logistics Manager' 'VP Sales'
+ 'EdTech Founder' 'SEO Specialist' 'Yoga Instructor' 'IT Director'
+ 'App Developer' 'Consultant Cardiologist' 'Freelance Writer'
+ 'Cloud Engineer' 'Retired CA' 'Digital Artist' 'Retired Pilot'
+ 'Graphic Animator' 'AI Ethicist' 'School Trustee' 'Robotics Engineer'
+ 'Fashion Stylist' 'Retired Nurse' 'AI Ethics Consultant' 'Drone Engineer'
+ 'Retired Bank Clerk' 'Digital Nomad' 'CFO' 'Sustainability Analyst'
+ 'Retired Journalist' 'Social Entrepreneur' 'DevOps Engineer'
+ 'School Counselor' 'Retired Army Colonel' 'AR Developer' 'Sales Director'
+ 'EdTech Consultant' 'SEO Expert' 'Cardiologist' '3D Artist' 'HR Head'
+ 'Animator' 'Fashion Influencer']
+
+Unique values in 'Lifecycle Stage':
+['Early Career' 'Mid-Career' 'Retired' 'Student' 'Late Career']
+
+Unique values in 'Risk Appetite':
+['Medium' 'Low' 'High']
+
+Unique values in 'Investment Horizon':
+['Long-term' 'Medium-term' 'Short-term']
+
+Numerical columns summary:
+              Salary       Expenses       Savings  Equity (%)    Debt (%)  \
+count     198.000000     198.000000    198.000000  198.000000  198.000000   
+mean   115075.757576   84686.868687  30388.888889   39.469697   28.308081   
+std     69388.842695   48499.494260  21425.713424   20.713089   11.534149   
+min      8000.000000    7500.000000    500.000000    0.000000   10.000000   
+25%     60000.000000   48000.000000  10000.000000   25.000000   20.000000   
+50%     95000.000000   70000.000000  25000.000000   45.000000   25.000000   
+75%    163750.000000  118750.000000  45000.000000   50.000000   30.000000   
+max    310000.000000  230000.000000  90000.000000   70.000000   55.000000   
+
+         Gold (%)  FD/Cash (%)  
+count  198.000000   198.000000  
+mean     8.434343    23.787879  
+std      3.427751    16.426902  
+min      0.000000    10.000000  
+25%      5.000000    15.000000  
+50%     10.000000    20.000000  
+75%     10.000000    20.000000  
+max     15.000000    80.000000 

Reference/basemodel.py

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+import joblib
+
+# Load the basic model pipeline
+pipeline = joblib.load('basic_portfolio_model.pkl')
+
+input_data = {
+    'Salary': 150000,
+    'Expenses': 100000,
+    'Savings': 50000,
+    'Lifecycle Stage': 'Mid-Career',
+    'Risk Appetite': 'Medium',
+    'Investment Horizon': 'Long-term'
+}
+
+# Convert categorical features (FIXED SYNTAX)
+input_data['Lifecycle Stage'] = pipeline['mappings']['lifecycle'][input_data['Lifecycle Stage']]  # Added closing ]
+input_data['Risk Appetite'] = pipeline['mappings']['risk'][input_data['Risk Appetite']]  # Added closing ]
+input_data['Investment Horizon'] = pipeline['mappings']['horizon'][input_data['Investment Horizon']]  # Added closing ]
+
+# Create feature array in correct order
+X = [
+    input_data['Salary'],
+    input_data['Expenses'],
+    input_data['Savings'],
+    input_data['Lifecycle Stage'],
+    input_data['Risk Appetite'],
+    input_data['Investment Horizon']
+]
+
+# Scale and predict
+X_scaled = pipeline['scaler'].transform([X])
+pred = pipeline['model'].predict(X_scaled)[0]
+
+# Normalize and format
+total = pred.sum()
+final_allocation = {
+    'Equity': round((pred[0]/total)*100, 1),
+    'Debt': round((pred[1]/total)*100, 1),
+    'Gold': round((pred[2]/total)*100, 1),
+    'FD/Cash': round((pred[3]/total)*100, 1)
+}
+
+print("Recommended Portfolio:")
+for asset, perc in final_allocation.items():
+    print(f"{asset}: {perc}%")
+print(f"Total: {sum(final_allocation.values())}%")

Reference/basic_portfolio_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ae20f4cf2e1cb3f75623f692aa11f947c83529dbe6335a5634365052bd8d15ca
+size 1817898

Reference/enhanced_portfolio_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2560b79a9705ca3674cd96820fd31fd6799dde89b12d4815afc6887111b52746
+size 1691633

Reference/enhancedmodel.py

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+import joblib
+import pandas as pd
+
+# Load enhanced model pipeline
+pipeline = joblib.load('enhanced_portfolio_model.pkl')
+
+# Sample input (MUST include Profession/City)
+test_case = {
+    'Profession': 'Software Engineer',
+    'City': 'Mumbai',
+    'Salary': 150000,
+    'Expenses': 100000,
+    'Savings': 50000,
+    'Lifecycle Stage': 'Mid-Career',
+    'Risk Appetite': 'Medium',
+    'Investment Horizon': 'Long-term'
+}
+
+# Convert categorical features
+test_case['Profession'] = pipeline['profession_encoder'].transform([test_case['Profession']])[0]
+test_case['City'] = pipeline['city_encoder'].transform([test_case['City']])[0]
+test_case['Lifecycle Stage'] = pipeline['mappings']['lifecycle'][test_case['Lifecycle Stage']]
+test_case['Risk Appetite'] = pipeline['mappings']['risk'][test_case['Risk Appetite']]
+test_case['Investment Horizon'] = pipeline['mappings']['horizon'][test_case['Investment Horizon']]
+
+# Create feature array IN EXACT ORDER:
+# ['Profession', 'City', 'Salary', 'Expenses', 'Savings',
+#  'Lifecycle Stage', 'Risk Appetite', 'Investment Horizon']
+X = [
+    test_case['Profession'],
+    test_case['City'],
+    test_case['Salary'],
+    test_case['Expenses'],
+    test_case['Savings'],
+    test_case['Lifecycle Stage'],
+    test_case['Risk Appetite'],
+    test_case['Investment Horizon']
+]
+
+# Scale and predict
+X_scaled = pipeline['scaler'].transform([X])
+pred = pipeline['model'].predict(X_scaled)[0]
+
+# Normalize to 100%
+total = pred.sum()
+final_allocation = {
+    'Equity': round((pred[0]/total)*100, 1),
+    'Debt': round((pred[1]/total)*100, 1),
+    'Gold': round((pred[2]/total)*100, 1),
+    'FD/Cash': round((pred[3]/total)*100, 1)
+}
+
+print("Enhanced Model Recommended Portfolio:")
+for asset, perc in final_allocation.items():
+    print(f"{asset}: {perc}%")
+print(f"Total: {sum(final_allocation.values())}%")

Reference/rulebasedmodel.py

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+import datetime
+import math
+
+# --- Helper Functions (mostly unchanged) ---
+def get_float(prompt):
+    """Helper to get a non-negative float from the user."""
+    while True:
+        try:
+            val_str = input(prompt).strip()
+            if not val_str:
+                raise ValueError("Input cannot be empty.")
+            val = float(val_str)
+            if val < 0:
+                raise ValueError("Please enter a non-negative number.")
+            return val
+        except ValueError as e:
+            print(f"Invalid input: {e}")
+
+def get_int(prompt):
+    """Helper to get a non-negative integer from the user."""
+    while True:
+        try:
+            val_str = input(prompt).strip()
+            if not val_str:
+                raise ValueError("Input cannot be empty.")
+            val = int(val_str)
+            if val < 0:
+                raise ValueError("Please enter a non-negative whole number.")
+            return val
+        except ValueError as e:
+            print(f"Invalid input: {e}")
+
+def yes_no(prompt):
+    """Helper to get a yes/no response."""
+    while True:
+        resp = input(prompt + " (y/n): ").strip().lower()
+        if resp in ("y", "yes"):
+            return True
+        if resp in ("n", "no"):
+            return False
+        print("Please answer 'y' or 'n'.")
+
+# --- Simplified Profile and Financials Gathering ---
+
+def get_user_profile():
+    """Gathers lifecycle stage, risk appetite, and investment horizon."""
+    print("\n--- Let's Understand Your Investor Profile ---")
+
+    # 1. Lifecycle Stage
+    print("\nWhich of these best describes your current life stage?")
+    lifecycle_options = {
+        "1": "Student (Focus: Learning, initial savings)",
+        "2": "Early Career (20s-early 30s, Focus: Growth, accumulation)",
+        "3": "Mid-Career (Mid 30s-late 40s, Focus: Balancing growth & responsibilities)",
+        "4": "Late Career/Pre-Retirement (50s+, Focus: Capital preservation, income generation)",
+        "5": "Retired (Focus: Sustainable income, capital preservation)"
+    }
+    for key, value in lifecycle_options.items():
+        print(f"{key}. {value}")
+    while True:
+        choice = input("Enter number (1-5): ").strip()
+        if choice in lifecycle_options:
+            lifecycle_stage = lifecycle_options[choice].split('(')[0].strip() # Get "Student", "Early Career" etc.
+            break
+        else:
+            print("Invalid choice. Please enter a number between 1 and 5.")
+
+    # 2. Risk Appetite
+    print("\nHow would you describe your risk tolerance for investments?")
+    risk_options = {
+        "1": "Low (Prefer safety and capital preservation, okay with lower returns)",
+        "2": "Medium (Willing to take some risk for moderate growth, comfortable with some fluctuations)",
+        "3": "High (Willing to take significant risk for potentially high returns, can handle large fluctuations)"
+    }
+    for key, value in risk_options.items():
+        print(f"{key}. {value}")
+    while True:
+        choice = input("Enter number (1-3): ").strip()
+        if choice == "1": risk_appetite = "low"; break
+        elif choice == "2": risk_appetite = "medium"; break
+        elif choice == "3": risk_appetite = "high"; break
+        else:
+            print("Invalid choice. Please enter 1, 2, or 3.")
+
+    # 3. Investment Horizon
+    print("\nWhat is your general investment time horizon for the majority of your savings?")
+    horizon_options = {
+        "1": "Short-term (Less than 3 years)",
+        "2": "Medium-term (3-7 years)",
+        "3": "Long-term (More than 7 years)"
+    }
+    for key, value in horizon_options.items():
+        print(f"{key}. {value}")
+    while True:
+        choice = input("Enter number (1-3): ").strip()
+        if choice == "1": investment_horizon = "short-term"; break
+        elif choice == "2": investment_horizon = "medium-term"; break
+        elif choice == "3": investment_horizon = "long-term"; break
+        else:
+            print("Invalid choice. Please enter 1, 2, or 3.")
+
+    return lifecycle_stage, risk_appetite, investment_horizon
+
+def get_financial_details():
+    """Gets annual package, monthly in-hand salary, and total monthly expenses."""
+    print("\n--- Let's Get Your Financial Details ---")
+    annual_package = get_float("What is your approximate annual salary package (CTC)? \u20B9 ")
+
+    # Simplified monthly in-hand: Ask directly or provide a rough estimate and ask for correction
+    print(f"\nBased on an annual package of \u20B9 {annual_package:.2f}, your monthly in-hand salary might be around \u20B9 {annual_package / 14:.2f} to \u20B9 {annual_package / 13:.2f} (this is a rough estimate).")
+    monthly_in_hand_salary = get_float("What is your actual average monthly in-hand salary? \u20B9 ")
+
+    total_monthly_expenses = get_float("What are your total estimated monthly expenses (all inclusive)? \u20B9 ")
+
+    monthly_savings = monthly_in_hand_salary - total_monthly_expenses
+
+    print(f"\nBased on your input:")
+    print(f"  Monthly In-hand Salary: \u20B9 {monthly_in_hand_salary:.2f}")
+    print(f"  Total Monthly Expenses: \u20B9 {total_monthly_expenses:.2f}")
+    if monthly_savings > 0:
+        print(f"  Calculated Monthly Savings: \u20B9 {monthly_savings:.2f}")
+    else:
+        print(f"  Calculated Monthly Deficit: \u20B9 {abs(monthly_savings):.2f}")
+        print("  It seems your expenses exceed your income. Please review your budget.")
+
+    return monthly_in_hand_salary, total_monthly_expenses, monthly_savings
+
+# --- Simplified Investment Allocation Logic ---
+def allocate_savings_simplified(savings, risk_profile, horizon, lifecycle):
+    """
+    Determines investment allocation percentages based on simplified profile.
+    Returns a dictionary of { "Asset Class": percentage, ... }
+    """
+    allocations = {}
+    justification_points = [
+        f"This allocation considers your {risk_profile} risk profile, {horizon} horizon, and {lifecycle} stage."
+    ]
+
+    # Prioritize Emergency Fund if not explicitly covered or if savings are first-time
+    # For simplicity, this version assumes user will manage EF separately or this is for surplus post-EF.
+    # A more robust version would inquire about EF status.
+
+    if risk_profile == "low":
+        allocations = {
+            "Fixed Deposits / Recurring Deposits (Safety & Stability)": 0.35,
+            "Debt Mutual Funds (Liquid/Short Duration - Better than savings account returns)": 0.30,
+            "Gold (SGBs/ETFs - Inflation Hedge, Diversification)": 0.10,
+            "Equity MFs (Large Cap/Index Funds - Long-term inflation beating, low volatility equity)": 0.15,
+            "Cash / Savings Account (Immediate Liquidity)": 0.10
+        }
+        justification_points.append("- Focus on capital preservation and stable returns.")
+        justification_points.append("- Suitable for short-term goals or very conservative investors.")
+        if horizon != "short-term":
+            justification_points.append("- Even with a longer horizon, a 'low' risk choice emphasizes safety above all.")
+
+
+    elif risk_profile == "medium":
+        allocations = {
+            "Equity MFs (Diversified - Large & Mid Cap/Flexi Cap SIPs for growth)": 0.45,
+            "Debt Mutual Funds (For stability and portfolio balance)": 0.25,
+            "Fixed Deposits / PPF (Secure, long-term component)": 0.10,
+            "Gold (SGBs/ETFs - Diversification)": 0.10,
+            "Equity MFs (International - For global diversification, if comfortable)": 0.05, # Optional
+            "Cash / Savings Account (Buffer)": 0.05
+        }
+        justification_points.append("- Aims for a balance between growth (equity) and stability (debt, gold).")
+        justification_points.append("- Suitable for medium to long-term goals.")
+        if lifecycle in ["Early Career", "Student"] and horizon == "long-term":
+            justification_points.append("- Your stage and horizon allow for good equity exposure for wealth creation.")
+            # Could slightly increase equity for very young, long-term, medium risk.
+            # allocations["Equity MFs (Diversified - Large & Mid Cap/Flexi Cap SIPs for growth)"] = 0.50
+            # allocations["Debt Mutual Funds (For stability and portfolio balance)"] = 0.20
+
+    elif risk_profile == "high":
+        allocations = {
+            "Equity MFs (Aggressive Growth - Mid/Small Cap, Thematic SIPs, with research)": 0.60,
+            "Equity MFs (International - Global growth opportunities)": 0.15,
+            "Direct Stocks (If experienced & well-researched, otherwise add to Equity MFs)": 0.10, # User needs to self-assess this.
+            "Debt Mutual Funds (Strategic, for some diversification)": 0.05,
+            "Gold (SGBs/ETFs - Tactical Diversification)": 0.05,
+            "Alternative Inv. (REITs/InvITs - very small, if understood & suitable, else to Equity)": 0.05
+        }
+        justification_points.append("- Focuses on maximizing long-term growth potential, accepting higher volatility.")
+        justification_points.append("- Best suited for long-term goals and investors comfortable with significant market swings.")
+        if lifecycle not in ["Early Career", "Mid-Career"] and horizon == "long-term":
+             justification_points.append(f"- CAUTION: High risk at {lifecycle} stage needs careful consideration of your overall financial stability and nearness to needing funds.")
+        if horizon != "long-term":
+            justification_points.append(f"- CAUTION: High risk for a {horizon} horizon is generally not advisable. Ensure goals truly allow for this risk.")
+
+
+    # Normalize percentages to ensure they sum to 100%
+    current_total_percentage = sum(allocations.values())
+    if abs(current_total_percentage - 1.0) > 0.001: # If not already 100%
+        factor = 1.0 / current_total_percentage
+        normalized_allocations = {k: v * factor for k, v in allocations.items()}
+        # Small check to ensure the largest component doesn't become negative if factor is weird (shouldn't happen with positive inputs)
+        # And ensure no tiny values are left that make no sense, e.g. less than 0.5% could be merged.
+        # For simplicity, we'll assume initial definitions are close enough.
+        final_allocations = {}
+        temp_sum = 0
+        for asset, perc in normalized_allocations.items():
+            # Round to sensible points e.g. 1 decimal for percentage display
+            # but use more precision for calculation
+            final_allocations[asset] = perc
+            temp_sum += perc
+
+        # Final check and adjustment of the largest item if sum isn't perfect due to rounding during normalization.
+        if abs(temp_sum - 1.0) > 0.0001:
+            diff = 1.0 - temp_sum
+            if final_allocations: # Check if dict is not empty
+                 # Find largest item to adjust
+                largest_item_key = max(final_allocations, key=final_allocations.get)
+                final_allocations[largest_item_key] += diff
+        allocations = final_allocations
+
+
+    return allocations, justification_points
+
+def display_investment_plan(monthly_savings, allocations, justification):
+    """Displays the final investment plan and justification."""
+    print("\n\n--- Your Personalized Investment Allocation Plan ---")
+    print(f"Based on your monthly savings of \u20B9 {monthly_savings:.2f}:\n")
+
+    print("Suggested Allocation:")
+    print("---------------------------------------------------------------------------")
+    print(f"{'Asset Class/Instrument':<50} | {'Percentage':>12} | {'Amount (₹)':>12}")
+    print("---------------------------------------------------------------------------")
+    if not allocations: # Should not happen if logic is correct
+        print("No specific allocation generated. Please review inputs or consult an advisor.")
+        return
+
+    total_allocated_amount_check = 0
+    for asset, percentage in allocations.items():
+        amount = monthly_savings * percentage
+        total_allocated_amount_check += amount
+        print(f"{asset:<50} | {percentage*100:>11.1f}% | {amount:>11.2f}")
+    print("---------------------------------------------------------------------------")
+    print(f"{'TOTAL':<50} | {'100.0%':>12} | {total_allocated_amount_check:>11.2f}")
+
+
+    print("\nWhy these allocations?")
+    for point in justification:
+        print(f"- {point}")
+    print("\n- Learn more about general investing principles at: https://www.investopedia.com/financial-advisor/asset-allocation/\n"
+          "  and for Indian context: https://www.amfiindia.com (Investor Education section)")
+
+
+def provide_general_financial_tips(lifecycle_stage, monthly_savings, original_salary):
+    """Provides general financial tips."""
+    print("\n--- General Financial Tips ---")
+    tips = []
+    if monthly_savings <= 0 and original_salary > 0 :
+        tips.append("- Your expenses currently meet or exceed your income. Focus on creating a budget to identify areas to cut back or explore ways to increase your income.")
+    elif original_salary > 0:
+        savings_rate = (monthly_savings / original_salary) * 100
+        tips.append(f"- Your current savings rate is approximately {savings_rate:.1f}%. Aim for at least 20-30% if possible, especially in accumulation stages.")
+        if savings_rate < 15:
+             tips.append("- Consider reviewing discretionary spending to boost your savings rate.")
+
+    tips.append("- Build & Maintain an Emergency Fund: Aim for 3-6 months of essential living expenses in a safe, liquid account (e.g., savings account, liquid mutual fund). This is your top priority before aggressive investing.")
+    tips.append("- Get Adequately Insured: Ensure you have sufficient health insurance for yourself and your family. If you have dependents, a term life insurance policy is crucial.")
+    tips.append("- Invest Regularly & Be Disciplined: Consistency through SIPs (Systematic Investment Plans) is key, especially for long-term goals. Don't try to time the market.")
+    tips.append("- Review Periodically: Revisit your financial plan and investments at least annually, or when major life events occur (marriage, new job, child, etc.).")
+    tips.append("- Understand Your Investments: Before investing in any product, understand its risks, costs (like expense ratios for MFs), and how it fits your goals.")
+
+    if lifecycle_stage == "Student" or lifecycle_stage == "Early Career":
+        tips.append("- Focus on upskilling and career growth to increase your earning potential. Your human capital is your biggest asset at this stage.")
+    if lifecycle_stage == "Late Career/Pre-Retirement" or lifecycle_stage == "Retired":
+        tips.append("- Plan for healthcare expenses in retirement. Consider if your current investments align with generating regular income if needed.")
+
+    for tip in tips:
+        print(f"* {tip}")
+
+# --- Main Program Flow (Simplified) ---
+def main_simplified():
+    print("╔══════════════════════════════════════════════════════════════╗")
+    print("║          Simplified Monthly Savings Allocator                ║")
+    print(f"║                 Today's Date: {datetime.date.today().strftime('%Y-%m-%d')}                  ║")
+    print("╚══════════════════════════════════════════════════════════════╝")
+    print("\nWelcome! This tool will help you allocate your monthly savings.")
+    print("This is for educational purposes and NOT financial advice.\n")
+
+    # 1. Get User Profile
+    lifecycle, risk, horizon = get_user_profile()
+    print(f"\nYour Profile Summary: Lifecycle: {lifecycle}, Risk: {risk.capitalize()}, Horizon: {horizon.capitalize()}")
+
+    # 2. Get Financial Details
+    salary, expenses, savings = get_financial_details()
+
+    if savings <= 0:
+        print("\nSince you don't have a monthly surplus, investment allocation is not applicable now.")
+        print("Focus on budgeting and increasing savings first.")
+    else:
+        # 3. Allocate Savings
+        allocations_dict, justification_list = allocate_savings_simplified(savings, risk, horizon, lifecycle)
+
+        # 4. Display Plan
+        display_investment_plan(savings, allocations_dict, justification_list)
+
+    # 5. Provide General Tips
+    provide_general_financial_tips(lifecycle, savings, salary)
+
+    # 6. Disclaimer
+    print("\n\n--- Disclaimer ---")
+    print("The information and suggestions provided by this script are for general guidance and educational purposes ONLY.")
+    print("This does NOT constitute financial, investment, tax, or legal advice.")
+    print("Investment decisions involve risks. Past performance is not indicative of future results.")
+    print("Asset allocation models are generalized and may not be suitable for your individual circumstances.")
+    print("It is strongly recommended to consult with a SEBI-registered Investment Adviser or a qualified financial professional for personalized advice.")
+
+    print("\nBudget calculation and investment suggestions complete. Plan wisely!\n")
+
+if __name__ == "__main__":
+    main_simplified()

app/__init__.py

@@ -0,0 +1 @@
+# This file makes the 'app' directory a Python package.

app/admin.py

@@ -0,0 +1,73 @@
+from fastapi import APIRouter, Request, Depends, HTTPException, Query
+from fastapi.responses import HTMLResponse
+from sqlalchemy.orm import Session
+from typing import List
+
+from app import models, schemas, crud
+from app.database import get_db
+from app.auth import get_current_admin_user, get_current_active_user # Import both
+from app.main import templates # Import templates from main.py
+
+router = APIRouter()
+
+# Route to serve the HTML shell for the dashboard
+@router.get("/dashboard", response_class=HTMLResponse)
+async def admin_dashboard_shell(request: Request):
+    # This route loads the page structure.
+    # Client-side JS will verify admin status and fetch data.
+    return templates.TemplateResponse("admin/dashboard.html", {
+        "request": request,
+        "title": "Admin Dashboard"
+        # No user or users_list passed here initially
+    })
+
+# New API endpoint to fetch dashboard data (protected)
+@router.get("/api/dashboard-data")
+async def get_admin_dashboard_data(
+    db: Session = Depends(get_db),
+    current_user: models.User = Depends(get_current_admin_user), # Ensures only admin can access
+    search_query: str = Query(None, alias="search")
+):
+    if search_query:
+        user = crud.get_user_by_email(db, email=search_query)
+        users_list = [schemas.User.from_orm(user)] if user else [] # Convert to schema
+    else:
+        users = crud.get_users(db, limit=100)
+        users_list = [schemas.User.from_orm(u) for u in users] # Convert list to schema
+
+    # Return data needed by the dashboard template's JS
+    return {"users_list": users_list, "search_query": search_query, "admin_email": current_user.email}
+
+
+# Route to view specific user details (protected)
+# This serves an HTML page, so it will also need the client-side auth check pattern
+@router.get("/users/{user_id}", response_class=HTMLResponse)
+async def admin_view_user_details_shell(request: Request, user_id: int):
+    # Serve the shell page. JS will fetch details.
+     return templates.TemplateResponse("admin/user_details.html", {
+        "request": request,
+        "user_id": user_id, # Pass user_id for JS to use
+        "title": f"User Details" # Generic title initially
+    })
+
+# New API endpoint to fetch specific user details (protected)
+@router.get("/api/users/{user_id}")
+async def get_admin_user_details_data(
+    user_id: int,
+    db: Session = Depends(get_db),
+    current_admin: models.User = Depends(get_current_admin_user) # Ensure admin access
+):
+    target_user = crud.get_user(db, user_id=user_id)
+    if not target_user:
+        raise HTTPException(status_code=404, detail="User not found")
+
+    user_inputs_orm = crud.get_user_data_inputs_by_user_id(db, user_id=user_id, limit=100)
+    
+    # Convert ORM objects to Pydantic schemas for JSON response
+    target_user_schema = schemas.User.from_orm(target_user)
+    user_inputs_schema = [schemas.UserDataInputResponse.from_orm(item) for item in user_inputs_orm]
+
+    return {
+        "target_user": target_user_schema,
+        "user_inputs": user_inputs_schema
+    }

app/auth.py

@@ -0,0 +1,99 @@
+from fastapi import APIRouter, Depends, HTTPException, status, Form
+from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
+from sqlalchemy.orm import Session
+from datetime import timedelta
+
+from . import crud, models, schemas
+from .core import security
+from .database import get_db
+
+router = APIRouter()
+
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/auth/token") # Points to the token login endpoint
+
+ACCESS_TOKEN_EXPIRE_MINUTES = security.ACCESS_TOKEN_EXPIRE_MINUTES
+
+async def get_current_user(token: str = Depends(oauth2_scheme), db: Session = Depends(get_db)) -> models.User:
+    credentials_exception = HTTPException(
+        status_code=status.HTTP_401_UNAUTHORIZED,
+        detail="Could not validate credentials",
+        headers={"WWW-Authenticate": "Bearer"},
+    )
+    payload = security.decode_access_token(token)
+    if payload is None:
+        raise credentials_exception
+    email: str = payload.get("sub")
+    if email is None:
+        raise credentials_exception
+    
+    user = crud.get_user_by_email(db, email=email)
+    if user is None:
+        raise credentials_exception
+    return user
+
+async def get_current_active_user(current_user: models.User = Depends(get_current_user)) -> models.User:
+    # Add any active/inactive checks here if needed
+    # if not current_user.is_active:
+    #     raise HTTPException(status_code=400, detail="Inactive user")
+    return current_user
+
+async def get_current_admin_user(current_user: models.User = Depends(get_current_active_user)) -> models.User:
+    if not current_user.is_admin:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="The user doesn't have enough privileges"
+        )
+    return current_user
+
+
+@router.post("/signup", response_model=schemas.User)
+async def signup_user(user: schemas.UserCreate, db: Session = Depends(get_db)):
+    db_user = crud.get_user_by_email(db, email=user.email)
+    if db_user:
+        raise HTTPException(status_code=400, detail="Email already registered")
+    if user.password != user.confirm_password:
+        raise HTTPException(status_code=400, detail="Passwords do not match")
+    return crud.create_user(db=db, user=user)
+
+@router.post("/token", response_model=schemas.Token)
+async def login_for_access_token(form_data: OAuth2PasswordRequestForm = Depends(), db: Session = Depends(get_db)):
+    user = crud.get_user_by_email(db, email=form_data.username) # OAuth2 form uses 'username' for email
+    if not user or not security.verify_password(form_data.password, user.hashed_password):
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Incorrect email or password",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+    access_token_expires = timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)
+    access_token = security.create_access_token(
+        data={"sub": user.email}, expires_delta=access_token_expires
+    )
+    return {"access_token": access_token, "token_type": "bearer"}
+
+# This is a utility endpoint, usually not directly called by frontend for login
+# It's what OAuth2PasswordRequestForm uses internally.
+# The actual login form should post to /auth/token.
+@router.post("/login", response_model=schemas.Token)
+async def login_user_form(email: str = Form(...), password: str = Form(...), db: Session = Depends(get_db)):
+    """
+    Login endpoint that takes email and password from form data.
+    This is an alternative if not using OAuth2PasswordRequestForm directly in frontend JS.
+    Frontend forms would post to this.
+    """
+    user = crud.get_user_by_email(db, email=email)
+    if not user or not security.verify_password(password, user.hashed_password):
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Incorrect email or password",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+    access_token_expires = timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)
+    access_token = security.create_access_token(
+        data={"sub": user.email}, expires_delta=access_token_expires
+    )
+    return {"access_token": access_token, "token_type": "bearer"}
+
+
+@router.get("/users/me", response_model=schemas.User)
+async def read_users_me(current_user: models.User = Depends(get_current_active_user)):
+    return current_user

app/core/security.py

@@ -0,0 +1,40 @@
+from datetime import datetime, timedelta
+from typing import Optional
+from jose import JWTError, jwt
+from passlib.context import CryptContext
+from pydantic import BaseModel
+
+# Password Hashing
+pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
+
+# JWT Configuration (Consider moving to a .env file or config management)
+SECRET_KEY = "your-super-secret-key"  # CHANGE THIS IN PRODUCTION!
+ALGORITHM = "HS256"
+ACCESS_TOKEN_EXPIRE_MINUTES = 30 # Token validity period
+
+def verify_password(plain_password: str, hashed_password: str) -> bool:
+    return pwd_context.verify(plain_password, hashed_password)
+
+def get_password_hash(password: str) -> str:
+    return pwd_context.hash(password)
+
+def create_access_token(data: dict, expires_delta: Optional[timedelta] = None) -> str:
+    to_encode = data.copy()
+    if expires_delta:
+        expire = datetime.utcnow() + expires_delta
+    else:
+        expire = datetime.utcnow() + timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)
+    to_encode.update({"exp": expire})
+    encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
+    return encoded_jwt
+
+def decode_access_token(token: str) -> Optional[dict]:
+    try:
+        payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
+        return payload
+    except JWTError:
+        return None
+
+class TokenPayload(BaseModel):
+    sub: Optional[str] = None # 'sub' is standard for subject (e.g., user email or ID)
+    exp: Optional[datetime] = None

app/crud.py

@@ -0,0 +1,48 @@
+from sqlalchemy.orm import Session
+from . import models, schemas
+from .core.security import get_password_hash
+from typing import List, Optional
+
+# --- User CRUD ---
+def get_user(db: Session, user_id: int) -> Optional[models.User]:
+    return db.query(models.User).filter(models.User.id == user_id).first()
+
+def get_user_by_email(db: Session, email: str) -> Optional[models.User]:
+    return db.query(models.User).filter(models.User.email == email).first()
+
+def get_users(db: Session, skip: int = 0, limit: int = 100) -> List[models.User]:
+    return db.query(models.User).offset(skip).limit(limit).all()
+
+def create_user(db: Session, user: schemas.UserCreate) -> models.User:
+    hashed_password = get_password_hash(user.password)
+    db_user = models.User(email=user.email, hashed_password=hashed_password)
+    db.add(db_user)
+    db.commit()
+    db.refresh(db_user)
+    return db_user
+
+def create_admin_user(db: Session, user: schemas.UserCreate) -> models.User:
+    hashed_password = get_password_hash(user.password)
+    db_user = models.User(email=user.email, hashed_password=hashed_password, is_admin=True)
+    db.add(db_user)
+    db.commit()
+    db.refresh(db_user)
+    return db_user
+
+# --- UserDataInput CRUD ---
+def create_user_data_input(db: Session, item: schemas.UserDataInputCreate, user_id: int) -> models.UserDataInput:
+    db_item = models.UserDataInput(**item.dict(), user_id=user_id)
+    db.add(db_item)
+    db.commit()
+    db.refresh(db_item)
+    return db_item
+
+def get_user_data_inputs_by_user_id(db: Session, user_id: int, skip: int = 0, limit: int = 100) -> List[models.UserDataInput]:
+    return db.query(models.UserDataInput).filter(models.UserDataInput.user_id == user_id).offset(skip).limit(limit).all()
+
+def get_all_user_data_inputs(db: Session, skip: int = 0, limit: int = 1000) -> List[models.UserDataInput]:
+    """
+    Fetches all user data inputs, primarily for admin use.
+    Consider pagination for very large datasets.
+    """
+    return db.query(models.UserDataInput).order_by(models.UserDataInput.timestamp.desc()).offset(skip).limit(limit).all()

app/database.py

@@ -0,0 +1,26 @@
+from sqlalchemy import create_engine
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+import os
+
+DATABASE_URL = "sqlite:///./app/ml_models/financial_advisor.db"
+
+# Create the directory for the database if it doesn't exist
+os.makedirs(os.path.dirname(DATABASE_URL.split("///")[-1]), exist_ok=True)
+
+engine = create_engine(
+    DATABASE_URL, connect_args={"check_same_thread": False}
+)
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+Base = declarative_base()
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+def create_db_and_tables():
+    Base.metadata.create_all(bind=engine)

app/main.py

@@ -0,0 +1,146 @@
+from fastapi import FastAPI, Request, Depends, HTTPException, status
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from fastapi.responses import HTMLResponse, RedirectResponse
+from pathlib import Path
+from sqlalchemy.orm import Session
+
+from app import models, schemas, crud, auth # app. is used if main.py is outside app/
+from app.database import engine, get_db, create_db_and_tables
+from app.services import chatbot_service, data_service # app.services
+from app.auth import get_current_active_user, get_current_admin_user, oauth2_scheme # app.auth
+
+# Determine the base directory of the 'app' package
+BASE_DIR = Path(__file__).resolve().parent
+
+app = FastAPI(title="Financial Advisor Chatbot")
+
+# Mount static files (CSS, JS, images)
+# The path "static" here is relative to where main.py is.
+# If main.py is in app/, then app/static/
+app.mount("/static", StaticFiles(directory=BASE_DIR / "static"), name="static")
+
+# Setup Jinja2 templates
+# If main.py is in app/, then app/templates/
+templates = Jinja2Templates(directory=BASE_DIR / "templates")
+
+# --- Event Handlers ---
+@app.on_event("startup")
+async def startup_event():
+    create_db_and_tables()
+    # Initialize CSV file with headers if it doesn't exist
+    data_service.initialize_csv()
+    # Load ML models
+    chatbot_service.load_models()
+    # Create a default admin user if one doesn't exist (optional)
+    db = next(get_db()) # Get a DB session
+    try:
+        admin_email = "admin@example.com"
+        admin_password = "adminpassword" # Change this!
+        
+        admin_user_obj = crud.get_user_by_email(db, email=admin_email)
+        
+        if not admin_user_obj:
+            print(f"Admin user '{admin_email}' not found, creating...")
+            admin_user_schema = schemas.UserCreate(email=admin_email, password=admin_password, confirm_password=admin_password)
+            crud.create_admin_user(db, admin_user_schema)
+            print(f"Default admin user '{admin_email}' created with password '{admin_password}'. PLEASE CHANGE THE PASSWORD.")
+        elif not admin_user_obj.is_admin:
+            # If user exists but is not admin, update them (or log warning)
+            print(f"User '{admin_email}' exists but is not admin. Updating status...")
+            admin_user_obj.is_admin = True
+            # If you also want to ensure the password is set, you might reset it here:
+            # admin_user_obj.hashed_password = security.get_password_hash(admin_password)
+            db.add(admin_user_obj) # Add the existing object to the session to track changes
+            db.commit()
+            print(f"User '{admin_email}' updated to be an admin.")
+        else:
+             print(f"Admin user '{admin_email}' already exists.")
+
+    except Exception as e:
+        print(f"Error during admin user setup: {e}")
+    finally:
+        db.close()
+
+
+# --- Include Routers ---
+# Assuming auth.py, user.py, admin.py are in the same directory as main.py (i.e. in 'app')
+# If main.py is in the root, these would be from app.auth, app.user, app.admin
+from . import auth as auth_router # Renaming to avoid conflict with 'auth' module
+# We will create user_router and admin_router later
+from . import user as user_router
+from . import admin as admin_router
+
+app.include_router(auth_router.router, prefix="/auth", tags=["Authentication"])
+app.include_router(user_router.router, prefix="/user", tags=["User Pages"]) # User specific pages like /user/home
+app.include_router(admin_router.router, prefix="/admin", tags=["Admin Pages"]) # Admin specific pages like /admin/dashboard
+
+# --- Root and Basic Page Routes ---
+@app.get("/", response_class=HTMLResponse)
+async def read_root(request: Request):
+    # Redirect to login page by default, or to home if logged in (more complex logic for latter)
+    return templates.TemplateResponse("auth/login.html", {"request": request, "title": "Login"})
+
+@app.get("/signup-page", response_class=HTMLResponse) # Renamed to avoid conflict if auth router has /signup
+async def signup_page_render(request: Request):
+    return templates.TemplateResponse("auth/signup.html", {"request": request, "title": "Sign Up"})
+
+@app.get("/login-page", response_class=HTMLResponse) # Renamed to avoid conflict if auth router has /login
+async def login_page_render(request: Request):
+    return templates.TemplateResponse("auth/login.html", {"request": request, "title": "Login"})
+
+# The actual /home and /admin/dashboard routes are now in user.py and admin.py respectively.
+
+# --- Chatbot API endpoint ---
+@app.post("/api/chatbot", response_model=schemas.ChatbotInteractionResponse)
+async def api_chatbot_interact(
+    request_data: schemas.ChatbotInteractionRequest,
+    db: Session = Depends(get_db),
+    current_user: models.User = Depends(get_current_active_user)
+):
+    # Process the interaction using the chatbot service
+    response = chatbot_service.process_chatbot_interaction(request_data)
+
+    # If no error in recommendation, save input and potentially output to DB and CSV
+    if "error" not in response.recommendation:
+        # Save to DB
+        user_input_db = schemas.UserDataInputCreate(
+            model_type=request_data.model_type,
+            input_data=request_data.inputs
+            # output_data=response.recommendation # Optionally store output
+        )
+        crud.create_user_data_input(db=db, item=user_input_db, user_id=current_user.id)
+
+        # Save to CSV
+        data_service.append_data_to_csv(
+            user_id=current_user.id,
+            user_email=current_user.email,
+            model_type=request_data.model_type,
+            input_data=request_data.inputs,
+            output_data=response.recommendation # Pass the allocation part
+        )
+    
+    return response
+
+# --- Logout ---
+@app.get("/logout")
+async def logout(request: Request):
+    # For token-based auth, logout is primarily a client-side operation (deleting the token).
+    # Server-side, you might blacklist the token if using a more complex setup.
+    # Here, we'll just redirect to the root path which serves the login page.
+    response = RedirectResponse(url="/", status_code=status.HTTP_302_FOUND) # Changed url to "/"
+    # Instruct browser to clear any relevant cookies if they were set by server (not typical for Bearer tokens)
+    # response.delete_cookie("access_token_cookie") # If you were setting it as a cookie
+    return response
+
+
+# To run the app (example, usually done with uvicorn command line):
+# if __name__ == "__main__":
+#     import uvicorn
+#     # Note: Uvicorn should typically be run from the project root, not from within app/
+#     # e.g., uvicorn app.main:app --reload
+#     # For direct execution from app/main.py (less common for FastAPI projects):
+#     # uvicorn.run(app, host="0.0.0.0", port=8000)
+#     # Better to run with: uvicorn financial_advisor.app.main:app --reload from the project root
+#     # Or if main.py is in the root: uvicorn main:app --reload
+#     pass

app/ml_models/basic_portfolio_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae20f4cf2e1cb3f75623f692aa11f947c83529dbe6335a5634365052bd8d15ca
+size 1817898

app/ml_models/enhanced_portfolio_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a780fe27d28ffee39b771925d3746551b30417e09d513f1899a38091e607e5d0
+size 1738333

app/ml_models/enhanced_portfolio_model111.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2560b79a9705ca3674cd96820fd31fd6799dde89b12d4815afc6887111b52746
+size 1691633

app/models.py

@@ -0,0 +1,28 @@
+from sqlalchemy import Column, Integer, String, Boolean, DateTime, JSON, ForeignKey
+from sqlalchemy.orm import relationship
+from sqlalchemy.sql import func
+from .database import Base
+
+class User(Base):
+    __tablename__ = "users"
+
+    id = Column(Integer, primary_key=True, index=True)
+    email = Column(String, unique=True, index=True, nullable=False)
+    hashed_password = Column(String, nullable=False)
+    is_admin = Column(Boolean, default=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    inputs = relationship("UserDataInput", back_populates="owner")
+
+class UserDataInput(Base):
+    __tablename__ = "user_data_inputs"
+
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(Integer, ForeignKey("users.id"))
+    model_type = Column(String, index=True) # "base", "enhanced", "rule_based"
+    input_data = Column(JSON) # Stores the dictionary of inputs
+    # Output data can also be stored if needed, e.g., portfolio allocation
+    # output_data = Column(JSON) 
+    timestamp = Column(DateTime(timezone=True), server_default=func.now())
+
+    owner = relationship("User", back_populates="inputs")

app/schemas.py

@@ -0,0 +1,139 @@
+from pydantic import BaseModel, EmailStr, field_validator, model_validator, ValidationInfo
+from typing import Optional, Dict, Any, List
+from datetime import datetime
+
+# --- User Schemas ---
+class UserBase(BaseModel):
+    email: EmailStr
+
+class UserCreate(UserBase):
+    password: str
+    confirm_password: Optional[str] = None # For signup form
+
+    @model_validator(mode='after')
+    def passwords_match(self) -> 'UserCreate':
+        if self.password is not None and self.confirm_password is not None and self.password != self.confirm_password:
+            raise ValueError('Passwords do not match')
+        # Ensure confirm_password is not passed to the User model if it's just for validation
+        # However, our User model doesn't have confirm_password, so it's fine.
+        return self
+
+class User(UserBase):
+    id: int
+    is_admin: bool
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+# --- UserDataInput Schemas ---
+class UserDataInputBase(BaseModel):
+    model_type: str
+    input_data: Dict[str, Any]
+
+class UserDataInputCreate(UserDataInputBase):
+    pass
+
+class UserDataInputResponse(UserDataInputBase):
+    id: int
+    user_id: int
+    timestamp: datetime
+    # output_data: Optional[Dict[str, Any]] = None # If storing output
+
+    class Config:
+        from_attributes = True
+
+class UserWithInputs(User):
+    inputs: List[UserDataInputResponse] = []
+
+    class Config: # Added for consistency, though User already has it.
+        from_attributes = True
+
+
+# --- Token Schemas ---
+class Token(BaseModel):
+    access_token: str
+    token_type: str
+
+class TokenData(BaseModel):
+    email: Optional[str] = None
+
+
+# --- Chatbot Input Schemas (Specific to each model for validation) ---
+# These can be more specific if needed, for now using a generic Dict
+# For example, for Base Model:
+class BaseModeInputSchema(BaseModel):
+    Salary: float
+    Expenses: float
+    Savings: float
+    Lifecycle_Stage: str 
+    Risk_Appetite: str
+    Investment_Horizon: str
+
+    @field_validator('Salary', 'Expenses', 'Savings')
+    @classmethod
+    def check_non_negative_numeric(cls, v: Any, info: ValidationInfo) -> Any:
+        if not isinstance(v, (int, float)):
+            raise ValueError(f"{info.field_name} must be a number")
+        if v < 0:
+            raise ValueError(f"{info.field_name} cannot be negative")
+        return v
+    
+    class Config:
+        from_attributes = True
+
+
+class EnhancedModelInputSchema(BaseModel):
+    Profession: str
+    City: str
+    Salary: float
+    Expenses: float
+    Savings: float
+    Lifecycle_Stage: str
+    Risk_Appetite: str
+    Investment_Horizon: str
+
+    @field_validator('Salary', 'Expenses', 'Savings')
+    @classmethod
+    def check_non_negative_numeric(cls, v: Any, info: ValidationInfo) -> Any:
+        if not isinstance(v, (int, float)):
+            raise ValueError(f"{info.field_name} must be a number")
+        if v < 0:
+            raise ValueError(f"{info.field_name} cannot be negative")
+        return v
+
+    class Config:
+        from_attributes = True
+
+class RuleBasedModelInputSchema(BaseModel):
+    Lifecycle_Stage: str
+    Risk_Appetite: str
+    Investment_Horizon: str
+    Annual_Salary_Package: float
+    Monthly_In_hand_Salary: float
+    Total_Monthly_Expenses: float
+
+    @field_validator('Annual_Salary_Package', 'Monthly_In_hand_Salary', 'Total_Monthly_Expenses')
+    @classmethod
+    def check_non_negative_numeric(cls, v: Any, info: ValidationInfo) -> Any:
+        if not isinstance(v, (int, float)):
+            raise ValueError(f"{info.field_name} must be a number")
+        if v < 0:
+            raise ValueError(f"{info.field_name} cannot be negative")
+        return v
+    
+    class Config:
+        from_attributes = True
+
+# For the chatbot interaction, the form will likely submit a dictionary.
+# The specific schema can be used within the service layer before passing to the model.
+class ChatbotInteractionRequest(BaseModel):
+    model_type: str # "base", "enhanced", "rule_based"
+    inputs: Dict[str, Any]
+
+class ChatbotInteractionResponse(BaseModel):
+    model_type: str
+    user_inputs: Dict[str, Any]
+    recommendation: Dict[str, Any] # e.g., portfolio allocation
+    justification: Optional[List[str]] = None # For rule-based
+    tips: Optional[List[str]] = None # For rule-based

app/services/chatbot_service.py

@@ -0,0 +1,312 @@
+import joblib
+import pandas as pd
+import os
+from typing import Dict, Any, Tuple, List, Optional
+from app import schemas # Assuming schemas.py is in the same 'app' directory
+import logging
+
+logger = logging.getLogger(__name__)
+
+# --- Model Loading ---
+# Define paths to the model files, assuming they are in app/ml_models/
+MODEL_DIR = os.path.join(os.path.dirname(__file__), "..", "ml_models") # app/ml_models
+BASE_MODEL_PATH = os.path.join(MODEL_DIR, "basic_portfolio_model.pkl")
+ENHANCED_MODEL_PATH = os.path.join(MODEL_DIR, "enhanced_portfolio_model.pkl")
+
+# Ensure the ml_models directory exists
+os.makedirs(MODEL_DIR, exist_ok=True)
+
+# Placeholder for loaded models
+base_model_pipeline = None
+enhanced_model_pipeline = None
+
+def load_models():
+    global base_model_pipeline, enhanced_model_pipeline
+    try:
+        if os.path.exists(BASE_MODEL_PATH):
+            base_model_pipeline = joblib.load(BASE_MODEL_PATH)
+            logger.info("Base model loaded successfully.")
+        else:
+            logger.error(f"Base model file not found at {BASE_MODEL_PATH}")
+
+        if os.path.exists(ENHANCED_MODEL_PATH):
+            enhanced_model_pipeline = joblib.load(ENHANCED_MODEL_PATH)
+            logger.info("Enhanced model loaded successfully.")
+        else:
+            logger.error(f"Enhanced model file not found at {ENHANCED_MODEL_PATH}")
+    except Exception as e:
+        logger.error(f"Error loading ML models: {e}")
+
+# Call load_models when this module is imported so they are ready.
+# load_models() # Will be called from main.py or an init step
+
+# --- Base Model Prediction ---
+def predict_base_model(input_data: schemas.BaseModeInputSchema) -> Dict[str, float]:
+    if not base_model_pipeline:
+        logger.error("Base model not loaded. Cannot predict.")
+        # Consider raising an exception or returning an error state
+        return {"error": "Base model not available"}
+
+    try:
+        data = input_data.dict()
+        
+        # Convert categorical features using mappings from the loaded pipeline
+        # These mappings should exist in the .pkl file if saved correctly
+        processed_data = data.copy()
+        processed_data['Lifecycle_Stage'] = base_model_pipeline['mappings']['lifecycle'][data['Lifecycle_Stage']]
+        processed_data['Risk_Appetite'] = base_model_pipeline['mappings']['risk'][data['Risk_Appetite']]
+        processed_data['Investment_Horizon'] = base_model_pipeline['mappings']['horizon'][data['Investment_Horizon']]
+
+        # Create feature array in correct order (must match training)
+        # Order from Reference/basemodel.py: Salary, Expenses, Savings, Lifecycle Stage, Risk Appetite, Investment Horizon
+        X = [
+            processed_data['Salary'],
+            processed_data['Expenses'],
+            processed_data['Savings'],
+            processed_data['Lifecycle_Stage'], # This is now numerical
+            processed_data['Risk_Appetite'],   # This is now numerical
+            processed_data['Investment_Horizon'] # This is now numerical
+        ]
+
+        X_scaled = base_model_pipeline['scaler'].transform([X])
+        pred = base_model_pipeline['model'].predict(X_scaled)[0]
+
+        total = pred.sum()
+        if total == 0: # Avoid division by zero
+            return {'Equity': 0, 'Debt': 0, 'Gold': 0, 'FD/Cash': 0, "error": "Prediction resulted in zero total"}
+
+        final_allocation = {
+            'Equity': round((pred[0]/total)*100, 1),
+            'Debt': round((pred[1]/total)*100, 1),
+            'Gold': round((pred[2]/total)*100, 1),
+            'FD/Cash': round((pred[3]/total)*100, 1)
+        }
+        return final_allocation
+    except KeyError as e:
+        logger.error(f"KeyError during base model prediction: {e}. Check mappings in pipeline or input data keys.")
+        return {"error": f"Missing data or incorrect mapping for {e}"}
+    except Exception as e:
+        logger.error(f"Error in base model prediction: {e}")
+        return {"error": str(e)}
+
+
+# --- Enhanced Model Prediction ---
+def predict_enhanced_model(input_data: schemas.EnhancedModelInputSchema) -> Dict[str, float]:
+    if not enhanced_model_pipeline:
+        logger.error("Enhanced model not loaded. Cannot predict.")
+        return {"error": "Enhanced model not available"}
+
+    try:
+        data = input_data.dict()
+        processed_data = data.copy()
+
+        # Convert categorical features
+        processed_data['Profession'] = enhanced_model_pipeline['profession_encoder'].transform([data['Profession']])[0]
+        processed_data['City'] = enhanced_model_pipeline['city_encoder'].transform([data['City']])[0]
+        processed_data['Lifecycle_Stage'] = enhanced_model_pipeline['mappings']['lifecycle'][data['Lifecycle_Stage']]
+        processed_data['Risk_Appetite'] = enhanced_model_pipeline['mappings']['risk'][data['Risk_Appetite']]
+        processed_data['Investment_Horizon'] = enhanced_model_pipeline['mappings']['horizon'][data['Investment_Horizon']]
+        
+        # Order from Reference/enhancedmodel.py:
+        # ['Profession', 'City', 'Salary', 'Expenses', 'Savings', 'Lifecycle Stage', 'Risk Appetite', 'Investment Horizon']
+        X = [
+            processed_data['Profession'],
+            processed_data['City'],
+            processed_data['Salary'],
+            processed_data['Expenses'],
+            processed_data['Savings'],
+            processed_data['Lifecycle_Stage'],
+            processed_data['Risk_Appetite'],
+            processed_data['Investment_Horizon']
+        ]
+
+        X_scaled = enhanced_model_pipeline['scaler'].transform([X])
+        pred = enhanced_model_pipeline['model'].predict(X_scaled)[0]
+
+        total = pred.sum()
+        if total == 0:
+             return {'Equity': 0, 'Debt': 0, 'Gold': 0, 'FD/Cash': 0, "error": "Prediction resulted in zero total"}
+
+        final_allocation = {
+            'Equity': round((pred[0]/total)*100, 1),
+            'Debt': round((pred[1]/total)*100, 1),
+            'Gold': round((pred[2]/total)*100, 1),
+            'FD/Cash': round((pred[3]/total)*100, 1)
+        }
+        return final_allocation
+    except KeyError as e:
+        logger.error(f"KeyError during enhanced model prediction: {e}. Check mappings/encoders in pipeline or input data keys.")
+        return {"error": f"Missing data or incorrect mapping/encoding for {e}"}
+    except Exception as e:
+        logger.error(f"Error in enhanced model prediction: {e}")
+        return {"error": str(e)}
+
+# --- Rule-Based Model Logic ---
+# (Adapted from Reference/rulebasedmodel.py)
+
+def _allocate_savings_rule_based(
+    monthly_savings: float, 
+    risk_profile: str, 
+    horizon: str, 
+    lifecycle: str
+) -> Tuple[Dict[str, float], List[str]]:
+    allocations = {}
+    justification_points = [
+        f"This allocation considers your {risk_profile} risk profile, {horizon} horizon, and {lifecycle} stage."
+    ]
+    risk_profile = risk_profile.lower() # Ensure consistency
+    horizon = horizon.lower()
+    # Lifecycle stage is already in a good format from schema e.g. "Early Career"
+
+    if risk_profile == "low":
+        allocations = {
+            "Fixed Deposits / Recurring Deposits": 0.35,
+            "Debt Mutual Funds (Liquid/Short Duration)": 0.30,
+            "Gold (SGBs/ETFs)": 0.10,
+            "Equity MFs (Large Cap/Index Funds)": 0.15,
+            "Cash / Savings Account": 0.10
+        }
+        justification_points.append("- Focus on capital preservation and stable returns.")
+    elif risk_profile == "medium":
+        allocations = {
+            "Equity MFs (Diversified - Large & Mid Cap/Flexi Cap)": 0.45,
+            "Debt Mutual Funds": 0.25,
+            "Fixed Deposits / PPF": 0.10,
+            "Gold (SGBs/ETFs)": 0.10,
+            "Equity MFs (International)": 0.05, 
+            "Cash / Savings Account": 0.05
+        }
+        justification_points.append("- Aims for a balance between growth and stability.")
+    elif risk_profile == "high":
+        allocations = {
+            "Equity MFs (Aggressive Growth - Mid/Small Cap, Thematic)": 0.60,
+            "Equity MFs (International)": 0.15,
+            "Direct Stocks (If experienced)": 0.10, 
+            "Debt Mutual Funds (Strategic)": 0.05,
+            "Gold (SGBs/ETFs - Tactical)": 0.05,
+            "Alternative Inv. (REITs/InvITs)": 0.05
+        }
+        justification_points.append("- Focuses on maximizing long-term growth potential.")
+
+    # Normalize percentages (simplified for brevity, original script had more robust normalization)
+    current_total_percentage = sum(allocations.values())
+    if abs(current_total_percentage - 1.0) > 0.001 and current_total_percentage != 0:
+        factor = 1.0 / current_total_percentage
+        allocations = {k: round(v * factor, 3) for k, v in allocations.items()} 
+        # Adjust last item to make sum exactly 1.0 if needed due to rounding
+        sum_val = sum(allocations.values())
+        if sum_val != 1.0 and allocations:
+            key_to_adjust = list(allocations.keys())[-1]
+            allocations[key_to_adjust] += (1.0 - sum_val)
+            allocations[key_to_adjust] = round(allocations[key_to_adjust], 3)
+
+
+    # Convert percentage values to actual amounts based on monthly_savings
+    # The chatbot response schema expects percentages for recommendation, so we return percentages.
+    # The display logic in the template can calculate amounts.
+    
+    # For the ChatbotInteractionResponse, recommendation should be percentages
+    # Let's ensure the values are percentages (0 to 100)
+    final_percentage_allocations = {k: round(v * 100, 1) for k,v in allocations.items()}
+
+    return final_percentage_allocations, justification_points
+
+
+def _get_general_financial_tips(lifecycle_stage: str, monthly_savings: float, monthly_in_hand_salary: float) -> List[str]:
+    tips = []
+    if monthly_in_hand_salary > 0: # Avoid division by zero if salary is 0
+        if monthly_savings <= 0 :
+            tips.append("- Your expenses currently meet or exceed your income. Focus on creating a budget.")
+        else:
+            savings_rate = (monthly_savings / monthly_in_hand_salary) * 100
+            tips.append(f"- Your current savings rate is approximately {savings_rate:.1f}%. Aim for at least 20-30%.")
+    else: # No salary info or zero salary
+        if monthly_savings <= 0:
+             tips.append("- Your expenses currently meet or exceed your income. Focus on creating a budget.")
+
+
+    tips.extend([
+        "- Build & Maintain an Emergency Fund: Aim for 3-6 months of essential living expenses.",
+        "- Get Adequately Insured: Health and Term Life Insurance are crucial.",
+        "- Invest Regularly & Be Disciplined (e.g. SIPs).",
+        "- Review Periodically: Revisit your financial plan annually or on major life events.",
+        "- Understand Your Investments: Know the risks and costs."
+    ])
+    if lifecycle_stage == "Student" or lifecycle_stage == "Early Career":
+        tips.append("- Focus on upskilling and career growth.")
+    if lifecycle_stage == "Late Career/Pre-Retirement" or lifecycle_stage == "Retired": # Assuming "Retired" is a lifecycle stage
+        tips.append("- Plan for healthcare expenses in retirement.")
+    return tips
+
+def predict_rule_based_model(input_data: schemas.RuleBasedModelInputSchema) -> Tuple[Dict[str, float], List[str], List[str]]:
+    data = input_data.dict()
+    
+    monthly_savings = data['Monthly_In_hand_Salary'] - data['Total_Monthly_Expenses']
+
+    if monthly_savings <= 0:
+        allocation = {"message": "Savings are zero or negative. Focus on budgeting first."}
+        justification = ["Investment allocation is not applicable without positive savings."]
+        tips = _get_general_financial_tips(data['Lifecycle_Stage'], monthly_savings, data['Monthly_In_hand_Salary'])
+        tips.insert(0, "Priority: Increase savings or reduce expenses.")
+        return allocation, justification, tips
+
+    allocation, justification = _allocate_savings_rule_based(
+        monthly_savings,
+        data['Risk_Appetite'],
+        data['Investment_Horizon'],
+        data['Lifecycle_Stage']
+    )
+    tips = _get_general_financial_tips(data['Lifecycle_Stage'], monthly_savings, data['Monthly_In_hand_Salary'])
+    
+    return allocation, justification, tips
+
+
+# --- Main Chatbot Interaction Logic ---
+def process_chatbot_interaction(
+    request: schemas.ChatbotInteractionRequest
+) -> schemas.ChatbotInteractionResponse:
+    
+    model_type = request.model_type.lower()
+    inputs = request.inputs
+
+    recommendation: Dict[str, Any] = {}
+    justification: Optional[List[str]] = None
+    tips: Optional[List[str]] = None
+
+    try:
+        if model_type == "base":
+            # Validate inputs against BaseModeInputSchema
+            validated_inputs = schemas.BaseModeInputSchema(**inputs)
+            recommendation = predict_base_model(validated_inputs)
+        elif model_type == "enhanced":
+            validated_inputs = schemas.EnhancedModelInputSchema(**inputs)
+            recommendation = predict_enhanced_model(validated_inputs)
+        elif model_type == "rule_based":
+            validated_inputs = schemas.RuleBasedModelInputSchema(**inputs)
+            recommendation, justification, tips = predict_rule_based_model(validated_inputs)
+        else:
+            raise ValueError("Invalid model type specified")
+
+        # Check for errors from prediction functions
+        if "error" in recommendation:
+             # Propagate the error message
+            return schemas.ChatbotInteractionResponse(
+                model_type=model_type,
+                user_inputs=inputs,
+                recommendation=recommendation, # Contains the error message
+                justification=None,
+                tips=None
+            )
+
+    except Exception as e: # Catch validation errors or other issues
+        logger.error(f"Error processing chatbot interaction for {model_type}: {e}")
+        recommendation = {"error": f"Failed to process request: {str(e)}"}
+
+
+    return schemas.ChatbotInteractionResponse(
+        model_type=model_type,
+        user_inputs=inputs, # Return the original inputs for display
+        recommendation=recommendation,
+        justification=justification,
+        tips=tips
+    )

app/services/data_service.py

@@ -0,0 +1,129 @@
+import csv
+import os
+from datetime import datetime
+from typing import Dict, Any, List, Optional
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Define the path for the CSV file
+# Ensure this path is correct relative to where the application runs
+# For example, if main.py is in app/, and this service is called from there.
+CSV_FILE_PATH = "user_financial_data.csv" 
+# This will create the file in the same directory as main.py if it's in the root,
+# or in the 'app' directory if main.py is in 'app' and this path is used as is.
+# For consistency with the sqlite DB being in the root, let's adjust:
+CSV_FILE_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "user_financial_data.csv")
+# This places it in the project root directory (financial_advisor/)
+
+# Define the headers for the CSV file based on 'aboutdata.txt' and additional fields
+# Ensure all possible fields from all models are covered.
+CSV_HEADERS = [
+    'UserID', 'UserEmail', 'ModelType', 'Timestamp',
+    'Name', 'Age', 'City', 'Profession', 'Salary', 'Expenses', 'Savings',
+    'Lifecycle Stage', 'Risk Appetite', 'Investment Horizon',
+    'Annual Salary Package', 'Monthly In-hand Salary', 'Total Monthly Expenses', # For Rule-based
+    # Output fields from models (optional, but good for record keeping)
+    'Equity (%)', 'Debt (%)', 'Gold (%)', 'FD/Cash (%)'
+]
+
+
+def initialize_csv():
+    """Initializes the CSV file with headers if it doesn't exist."""
+    # Ensure the directory for the CSV file exists
+    os.makedirs(os.path.dirname(CSV_FILE_PATH), exist_ok=True)
+    
+    if not os.path.exists(CSV_FILE_PATH):
+        try:
+            with open(CSV_FILE_PATH, mode='w', newline='', encoding='utf-8') as file:
+                writer = csv.writer(file)
+                writer.writerow(CSV_HEADERS)
+            logger.info(f"CSV file initialized at {CSV_FILE_PATH}")
+        except IOError as e:
+            logger.error(f"Error initializing CSV file: {e}")
+
+def append_data_to_csv(user_id: int, user_email: str, model_type: str, input_data: Dict[str, Any], output_data: Optional[Dict[str, Any]] = None):
+    """
+    Appends a new row of data to the CSV file.
+    input_data should be a flat dictionary.
+    output_data is the portfolio allocation.
+    """
+    if not os.path.exists(CSV_FILE_PATH):
+        initialize_csv()
+
+    row_data = {header: '' for header in CSV_HEADERS} # Initialize with empty strings
+
+    # Populate common fields
+    row_data['UserID'] = user_id
+    row_data['UserEmail'] = user_email
+    row_data['ModelType'] = model_type
+    row_data['Timestamp'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    # Populate fields from input_data
+    # Normalize keys from input_data (e.g., 'Lifecycle_Stage' to 'Lifecycle Stage')
+    normalized_input_data = {key.replace('_', ' '): value for key, value in input_data.items()}
+
+    for key, value in normalized_input_data.items():
+        if key in row_data:
+            row_data[key] = value
+        # Handle specific keys that might have different names in input_data
+        elif key == "Annual Salary Package" and "Annual_Salary_Package" in normalized_input_data: # from RuleBasedModelInputSchema
+             row_data["Annual Salary Package"] = normalized_input_data["Annual_Salary_Package"]
+        # Add more specific mappings if needed
+
+    # Populate fields from output_data (portfolio allocation)
+    if output_data:
+        for key, value in output_data.items():
+            # Ensure the key from output_data matches a header like "Equity (%)"
+            header_key = f"{key} (%)" 
+            if header_key in row_data:
+                row_data[header_key] = value
+            elif key in row_data: # For keys like 'Equity', 'Debt' if not with '(%)'
+                 row_data[key] = value
+
+
+    # Ensure the order of data matches CSV_HEADERS
+    ordered_row_values = [row_data.get(header, '') for header in CSV_HEADERS]
+
+    try:
+        with open(CSV_FILE_PATH, mode='a', newline='', encoding='utf-8') as file:
+            writer = csv.writer(file)
+            writer.writerow(ordered_row_values)
+        logger.info(f"Data appended to CSV for user {user_email}, model {model_type}")
+    except IOError as e:
+        logger.error(f"Error appending data to CSV: {e}")
+    except Exception as e:
+        logger.error(f"An unexpected error occurred while writing to CSV: {e}")
+
+# Example usage (for testing this module independently):
+if __name__ == "__main__":
+    initialize_csv()
+    
+    # Test data
+    sample_input_base = {
+        'Salary': 60000, 'Expenses': 40000, 'Savings': 20000,
+        'Lifecycle_Stage': 'Early Career', 'Risk_Appetite': 'Medium', 'Investment_Horizon': 'Long-term'
+    }
+    sample_output = {'Equity': 50, 'Debt': 30, 'Gold': 10, 'FD/Cash': 10}
+    append_data_to_csv(1, "test@example.com", "base", sample_input_base, sample_output)
+
+    sample_input_enhanced = {
+        'Profession': 'Engineer', 'City': 'Metropolis',
+        'Salary': 120000, 'Expenses': 70000, 'Savings': 50000,
+        'Lifecycle_Stage': 'Mid-Career', 'Risk_Appetite': 'High', 'Investment_Horizon': 'Long-term'
+    }
+    append_data_to_csv(2, "another@example.com", "enhanced", sample_input_enhanced, sample_output)
+
+    sample_input_rule = {
+        'Lifecycle_Stage': 'Late Career', 
+        'Risk_Appetite': 'Low', 
+        'Investment_Horizon': 'Short-term',
+        'Annual_Salary_Package': 1000000, 
+        'Monthly_In_hand_Salary': 60000, 
+        'Total_Monthly_Expenses': 40000
+    }
+    append_data_to_csv(3, "ruleuser@example.com", "rule_based", sample_input_rule, sample_output)
+    
+    logger.info(f"Test data written to {CSV_FILE_PATH}")

app/static/css/style.css

@@ -0,0 +1,112 @@
+body {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    background-color: #f8f9fa; /* Light grey background */
+    display: flex;
+    flex-direction: column;
+    min-height: 100vh;
+}
+
+.container {
+    flex: 1;
+}
+
+.navbar {
+    box-shadow: 0 2px 4px rgba(0,0,0,.1);
+}
+
+.card {
+    border: none; /* Softer look, rely on shadow */
+    transition: transform .2s ease-in-out, box-shadow .2s ease-in-out;
+}
+
+.card:hover {
+    transform: translateY(-5px);
+    box-shadow: 0 0.5rem 1rem rgba(0,0,0,.15) !important;
+}
+
+.card-header {
+    font-weight: 500;
+}
+
+.btn-primary {
+    background-color: #007bff;
+    border-color: #007bff;
+}
+.btn-primary:hover {
+    background-color: #0056b3;
+    border-color: #0056b3;
+}
+
+.btn-success {
+    background-color: #28a745;
+    border-color: #28a745;
+}
+.btn-success:hover {
+    background-color: #1e7e34;
+    border-color: #1e7e34;
+}
+
+.btn-info {
+    background-color: #17a2b8;
+    border-color: #17a2b8;
+}
+.btn-info:hover {
+    background-color: #117a8b;
+    border-color: #117a8b;
+}
+
+
+/* Specific to chatbot results for better visual separation */
+#recommendationDetails ul .list-group-item span.badge {
+    font-size: 0.9em;
+}
+
+/* Footer styling */
+footer.bg-light {
+    background-color: #e9ecef !important; /* A slightly different shade for footer */
+    padding-top: 1rem;
+    padding-bottom: 1rem;
+}
+
+/* Responsive adjustments */
+@media (max-width: 768px) {
+    .display-4 {
+        font-size: 2.5rem;
+    }
+    h1 {
+        font-size: 1.75rem;
+    }
+}
+
+/* Ensure body takes full height for sticky footer */
+html, body {
+    height: 100%;
+}
+
+/* --- Chatbot Interface Styles --- */
+#chatbox { 
+    height: 400px; 
+    overflow-y: auto; 
+    border: 1px solid #ccc; 
+    padding: 10px; 
+    margin-bottom: 15px; 
+    background-color: #f9f9f9;
+    border-radius: 5px;
+}
+.chat-message { margin-bottom: 10px; }
+.bot-message { text-align: left; }
+.user-message { text-align: right; }
+.message-bubble { 
+    display: inline-block; 
+    padding: 8px 12px; 
+    border-radius: 15px; 
+    max-width: 80%; 
+    word-wrap: break-word; /* Ensure long words break */
+}
+.bot-message .message-bubble { background-color: #e9ecef; color: #333; }
+.user-message .message-bubble { background-color: #0d6efd; color: white; }
+#userInputArea button.option-button { margin: 3px; }
+#userInputArea { display: flex; gap: 10px; align-items: center; flex-wrap: wrap; } /* Added wrap */
+#userInputArea input, #userInputArea select { flex-grow: 1; min-width: 150px; } /* Added min-width */
+#optionsContainer { display: flex; flex-wrap: wrap; gap: 5px; } /* Styling for button container */
+.spinner-border-sm { width: 1rem; height: 1rem; }

app/static/js/script.js

@@ -0,0 +1,61 @@
+// Global JavaScript functions can be added here.
+
+document.addEventListener('DOMContentLoaded', function () {
+    // Example: Add a class to the body to indicate JS is enabled
+    document.body.classList.add('js-enabled');
+
+    // You could centralize common functions here if needed,
+    // for example, a function to make API calls with error handling.
+    // const apiRequest = async (url, method, body = null, headers = {}) => { ... }
+
+    const logoutButton = document.getElementById('logoutButton');
+    if (logoutButton) {
+        logoutButton.addEventListener('click', function (event) {
+            event.preventDefault(); // Prevent default anchor action
+            
+            localStorage.removeItem('accessToken');
+            localStorage.removeItem('tokenType');
+            
+            // Redirect to the server's /logout endpoint, which then redirects to login page
+            // This ensures any server-side session cleanup (if implemented later) could also occur.
+            window.location.href = '/logout'; 
+        });
+    }
+
+    // Client-side check to update navbar based on token presence
+    // This runs on every page load to set the correct initial state for navbar items.
+    const accessToken = localStorage.getItem('accessToken');
+    const navWelcomeItem = document.getElementById('navWelcomeItem');
+    const navLogoutItem = document.getElementById('navLogoutItem');
+    const navLoginItem = document.getElementById('navLoginItem');
+    const navSignupItem = document.getElementById('navSignupItem');
+
+    if (accessToken) {
+        // User is logged in (according to localStorage)
+        if (navWelcomeItem) navWelcomeItem.style.display = 'block'; // Or 'list-item' if needed
+        if (navLogoutItem) navLogoutItem.style.display = 'block';
+        if (navLoginItem) navLoginItem.style.display = 'none';
+        if (navSignupItem) navSignupItem.style.display = 'none';
+        
+        // Optional: Fetch user email to display in welcome message
+        // Could be done here or within specific page scripts like homepage.html
+        // fetch('/auth/users/me', { headers: {'Authorization': `Bearer ${accessToken}`} })
+        //   .then(response => response.ok ? response.json() : Promise.reject('Failed'))
+        //   .then(data => {
+        //       const welcomeSpan = document.getElementById('navUserWelcome');
+        //       if (welcomeSpan) welcomeSpan.textContent = `Welcome, ${data.email}`;
+        //   })
+        //   .catch(err => console.error("Error fetching user for navbar:", err));
+
+    } else {
+        // User is logged out
+        if (navWelcomeItem) navWelcomeItem.style.display = 'none';
+        if (navLogoutItem) navLogoutItem.style.display = 'none';
+        if (navLoginItem) navLoginItem.style.display = 'block';
+        if (navSignupItem) navSignupItem.style.display = 'block';
+    }
+});
+
+// Function to get CSRF token if using CSRF protection with forms not handled by FastAPI's default
+// function getCookie(name) { ... } 
+// Not strictly needed for this JWT setup unless forms post directly without JS.

app/templates/admin/dashboard.html

@@ -0,0 +1,175 @@
+{% extends "partials/base.html" %}
+
+{% block title %}Admin Dashboard - Financial Advisor{% endblock %}
+
+{% block content %}
+<div class="container mt-5">
+    <div class="d-flex justify-content-between align-items-center mb-4">
+        <h1>Admin Dashboard</h1>
+        <span id="adminWelcomeMessage">Welcome, Admin!</span> {# Placeholder #}
+    </div>
+     <div id="authErrorMessage" class="alert alert-danger" role="alert" style="display: none;">
+        Authentication failed or insufficient privileges. Redirecting...
+    </div>
+    <div id="loadingMessage" class="alert alert-info" role="alert">
+        Loading dashboard data...
+    </div>
+
+    <!-- Search Users -->
+    <div id="searchCard" class="card shadow-sm mb-4" style="display: none;">
+        <div class="card-body">
+            <h5 class="card-title">Search Users</h5>
+            <form id="searchForm" method="get"> {# Changed action, will be handled by JS or page reload #}
+                <div class="input-group">
+                    <input type="text" class="form-control" placeholder="Search by user email..." name="search" id="searchInput">
+                    <button class="btn btn-outline-primary" type="submit">Search</button>
+                </div>
+            </form>
+        </div>
+    </div>
+
+    <!-- Users Table -->
+    <div id="usersTableCard" class="card shadow-sm" style="display: none;">
+        <div class="card-header bg-secondary text-white">
+            <h4 class="mb-0">Registered Users</h4>
+        </div>
+        <div class="card-body">
+            <div class="table-responsive">
+                <table class="table table-striped table-hover">
+                    <thead>
+                        <tr>
+                            <th scope="col">ID</th>
+                            <th scope="col">Email</th>
+                            <th scope="col">Is Admin?</th>
+                            <th scope="col">Registered At</th>
+                            <th scope="col">Actions</th>
+                        </tr>
+                    </thead>
+                    <tbody id="usersTableBody">
+                        <!-- User rows will be inserted here by JS -->
+                    </tbody>
+                </table>
+            </div>
+            <p id="noUsersMessage" class="text-muted" style="display: none;"></p>
+        </div>
+    </div>
+    
+    <!-- Potentially add other admin functionalities here, like viewing all data inputs -->
+
+</div>
+{% endblock %}
+
+{% block scripts_extra %}
+<script>
+document.addEventListener('DOMContentLoaded', async function () {
+    const accessToken = localStorage.getItem('accessToken');
+    const loadingMessage = document.getElementById('loadingMessage');
+    const authErrorMessage = document.getElementById('authErrorMessage');
+    const searchCard = document.getElementById('searchCard');
+    const usersTableCard = document.getElementById('usersTableCard');
+    const usersTableBody = document.getElementById('usersTableBody');
+    const noUsersMessage = document.getElementById('noUsersMessage');
+    const adminWelcomeMessage = document.getElementById('adminWelcomeMessage');
+    const searchForm = document.getElementById('searchForm');
+    const searchInput = document.getElementById('searchInput');
+
+    // 1. Check Authentication and Admin Status
+    if (!accessToken) {
+        authErrorMessage.textContent = 'No authentication token found. Redirecting to login...';
+        authErrorMessage.style.display = 'block';
+        loadingMessage.style.display = 'none';
+        setTimeout(() => { window.location.href = "{{ url_for('login_page_render') }}"; }, 2000);
+        return;
+    }
+
+    try {
+        const userMeResponse = await fetch("{{ url_for('read_users_me') }}", {
+            headers: { 'Authorization': `Bearer ${accessToken}` }
+        });
+
+        if (!userMeResponse.ok) {
+            throw new Error(`Authentication failed: ${userMeResponse.status}`);
+        }
+
+        const currentUser = await userMeResponse.json();
+        if (!currentUser.is_admin) {
+            throw new Error('User is not authorized to view this page.');
+        }
+        
+        // Update welcome message
+        if(adminWelcomeMessage) adminWelcomeMessage.textContent = `Welcome, ${currentUser.email} (Admin)`;
+
+        // 2. Fetch Dashboard Data (handle search query)
+        const urlParams = new URLSearchParams(window.location.search);
+        const searchQuery = urlParams.get('search');
+        if (searchQuery && searchInput) {
+             searchInput.value = searchQuery; // Populate search box if query exists
+        }
+        
+        const dataUrl = searchQuery 
+            ? `/admin/api/dashboard-data?search=${encodeURIComponent(searchQuery)}` 
+            : "/admin/api/dashboard-data";
+
+        const dashboardDataResponse = await fetch(dataUrl, {
+            headers: { 'Authorization': `Bearer ${accessToken}` }
+        });
+
+        if (!dashboardDataResponse.ok) {
+            throw new Error(`Failed to fetch dashboard data: ${dashboardDataResponse.status}`);
+        }
+
+        const data = await dashboardDataResponse.json();
+
+        // 3. Populate Table
+        usersTableBody.innerHTML = ''; // Clear previous results
+        if (data.users_list && data.users_list.length > 0) {
+            noUsersMessage.style.display = 'none';
+            data.users_list.forEach(user => {
+                const row = usersTableBody.insertRow();
+                row.innerHTML = `
+                    <th scope="row">${user.id}</th>
+                    <td>${user.email}</td>
+                    <td>
+                        <span class="badge bg-${user.is_admin ? 'success' : 'secondary'}">
+                            ${user.is_admin ? 'Yes' : 'No'}
+                        </span>
+                    </td>
+                    <td>${new Date(user.created_at).toLocaleString()}</td>
+                    <td>
+                        <a href="/admin/users/${user.id}" class="btn btn-sm btn-info">View Details</a>
+                    </td>
+                `;
+                 // Note: Using hardcoded URL /admin/users/${user.id} as url_for isn't available client-side easily.
+                 // Ensure this matches the route defined in admin.py for admin_view_user_details_shell
+            });
+        } else {
+            noUsersMessage.textContent = searchQuery ? 'No users found matching your search.' : 'No users registered yet.';
+            noUsersMessage.style.display = 'block';
+        }
+
+        // Show content now that data is loaded
+        loadingMessage.style.display = 'none';
+        searchCard.style.display = 'block';
+        usersTableCard.style.display = 'block';
+
+    } catch (error) {
+        console.error('Error loading admin dashboard:', error);
+        loadingMessage.style.display = 'none';
+        authErrorMessage.textContent = `Error: ${error.message}. Redirecting...`;
+        authErrorMessage.style.display = 'block';
+        localStorage.removeItem('accessToken'); // Clear token on error
+        localStorage.removeItem('tokenType');
+        setTimeout(() => { window.location.href = "{{ url_for('login_page_render') }}"; }, 3000);
+    }
+    
+    // Handle search form submission (reloads the page with query param)
+    if(searchForm) {
+        searchForm.addEventListener('submit', function(event) {
+            // Default form submission with GET will reload the page with the query param
+            // The page load logic above will then handle fetching the filtered data.
+            // No preventDefault needed unless doing a fetch-based update.
+        });
+    }
+});
+</script>
+{% endblock %}

app/templates/admin/user_details.html

@@ -0,0 +1,170 @@
+{% extends "partials/base.html" %}
+
+{% block title %}{{ title }} - Financial Advisor{% endblock %}
+
+{% block content %}
+<div class="container mt-5">
+    <div class="d-flex justify-content-between align-items-center mb-4">
+        <h1 id="pageTitle">User Details</h1> {# Placeholder #}
+        <a href="{{ url_for('admin_dashboard_shell') }}" class="btn btn-secondary">&laquo; Back to Admin Dashboard</a>
+    </div>
+     <div id="authErrorMessage" class="alert alert-danger" role="alert" style="display: none;">
+        Authentication failed or insufficient privileges. Redirecting...
+    </div>
+    <div id="loadingMessage" class="alert alert-info" role="alert">
+        Loading user details...
+    </div>
+
+    <div id="userInfoCard" class="card shadow-sm mb-4" style="display: none;">
+        <div class="card-body">
+            <h5 class="card-title">User Information</h5>
+            <dl class="row" id="userInfoList">
+                {# User info will be populated here by JS #}
+            </dl>
+        </div>
+    </div>
+
+    <div id="userInputsCard" class="card shadow-sm" style="display: none;">
+        <div class="card-header bg-info text-white">
+            <h4 class="mb-0">Submitted Financial Inputs</h4>
+        </div>
+        <div class="card-body">
+            <div class="accordion" id="userDataAccordion">
+                {# Accordion items will be populated here by JS #}
+            </div>
+             <p id="noInputsMessage" class="text-muted" style="display: none;">This user has not submitted any financial inputs yet.</p>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts_extra %}
+<script>
+document.addEventListener('DOMContentLoaded', async function () {
+    const accessToken = localStorage.getItem('accessToken');
+    const loadingMessage = document.getElementById('loadingMessage');
+    const authErrorMessage = document.getElementById('authErrorMessage');
+    const userInfoCard = document.getElementById('userInfoCard');
+    const userInputsCard = document.getElementById('userInputsCard');
+    const userInfoList = document.getElementById('userInfoList');
+    const userDataAccordion = document.getElementById('userDataAccordion');
+    const noInputsMessage = document.getElementById('noInputsMessage');
+    const pageTitle = document.getElementById('pageTitle');
+    
+    const targetUserIdStr = "{{ user_id }}"; // Get user_id as string from server context
+    const targetUserId = parseInt(targetUserIdStr); // Parse it to integer
+
+    // 1. Check Authentication and Admin Status
+    if (!accessToken || isNaN(targetUserId)) { // Also check if targetUserId is valid
+        authErrorMessage.textContent = 'No authentication token found. Redirecting to login...';
+        authErrorMessage.style.display = 'block';
+        loadingMessage.style.display = 'none';
+        setTimeout(() => { window.location.href = "{{ url_for('login_page_render') }}"; }, 2000);
+        return;
+    }
+
+    try {
+        // Verify admin status first (optional but good practice)
+        const userMeResponse = await fetch("{{ url_for('read_users_me') }}", {
+            headers: { 'Authorization': `Bearer ${accessToken}` }
+        });
+        if (!userMeResponse.ok) throw new Error(`Authentication failed: ${userMeResponse.status}`);
+        const currentUser = await userMeResponse.json();
+        if (!currentUser.is_admin) throw new Error('User is not authorized to view this page.');
+
+        // 2. Fetch User Details Data
+        const detailsUrl = `/admin/api/users/${targetUserId}`;
+        const detailsResponse = await fetch(detailsUrl, {
+            headers: { 'Authorization': `Bearer ${accessToken}` }
+        });
+
+        if (!detailsResponse.ok) {
+            throw new Error(`Failed to fetch user details: ${detailsResponse.status}`);
+        }
+
+        const data = await detailsResponse.json();
+        const targetUser = data.target_user;
+        const userInputs = data.user_inputs;
+
+        // 3. Populate Page Content
+        loadingMessage.style.display = 'none';
+        
+        // Update Title
+        if(pageTitle && targetUser) pageTitle.textContent = `User Details: ${targetUser.email}`;
+        document.title = `User Details: ${targetUser.email} - Financial Advisor`; // Update browser tab title
+
+        // Populate User Info Card
+        if (userInfoList && targetUser) {
+            userInfoList.innerHTML = `
+                <dt class="col-sm-3">User ID:</dt>
+                <dd class="col-sm-9">${targetUser.id}</dd>
+                <dt class="col-sm-3">Email:</dt>
+                <dd class="col-sm-9">${targetUser.email}</dd>
+                <dt class="col-sm-3">Admin Status:</dt>
+                <dd class="col-sm-9">
+                    <span class="badge bg-${targetUser.is_admin ? 'success' : 'secondary'}">
+                        ${targetUser.is_admin ? 'Yes' : 'No'}
+                    </span>
+                </dd>
+                <dt class="col-sm-3">Registered At:</dt>
+                <dd class="col-sm-9">${new Date(targetUser.created_at).toLocaleString()}</dd>
+            `;
+            userInfoCard.style.display = 'block';
+        }
+
+        // Populate User Inputs Accordion
+        if (userDataAccordion) {
+            userDataAccordion.innerHTML = ''; // Clear previous
+            if (userInputs && userInputs.length > 0) {
+                noInputsMessage.style.display = 'none';
+                userInputs.forEach((inputItem, index) => {
+                    const itemId = `item-${index}`;
+                    const collapseId = `collapse-${index}`;
+                    const headingId = `heading-${index}`;
+
+                    let inputHtml = '<ul class="list-group">';
+                    for (const [key, value] of Object.entries(inputItem.input_data)) {
+                         inputHtml += `<li class="list-group-item"><strong>${key.replace(/_/g, ' ').replace(/\b\w/g, l => l.toUpperCase())}:</strong> ${value}</li>`;
+                    }
+                    inputHtml += '</ul>';
+                    
+                    // Add output data if available and needed
+                    // let outputHtml = '';
+                    // if (inputItem.output_data) { ... }
+
+                    const accordionItem = document.createElement('div');
+                    accordionItem.className = 'accordion-item';
+                    accordionItem.innerHTML = `
+                        <h2 class="accordion-header" id="${headingId}">
+                            <button class="accordion-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#${collapseId}" aria-expanded="false" aria-controls="${collapseId}">
+                                Input #${inputItem.id} - Model: <strong>${inputItem.model_type.charAt(0).toUpperCase() + inputItem.model_type.slice(1)}</strong> - Submitted: ${new Date(inputItem.timestamp).toLocaleString()}
+                            </button>
+                        </h2>
+                        <div id="${collapseId}" class="accordion-collapse collapse" aria-labelledby="${headingId}" data-bs-parent="#userDataAccordion">
+                            <div class="accordion-body">
+                                <h5>Input Data:</h5>
+                                ${inputHtml}
+                                <!-- Add outputHtml here if implemented -->
+                            </div>
+                        </div>
+                    `; // Removed Jinja2 comment from JS string literal
+                    userDataAccordion.appendChild(accordionItem);
+                });
+            } else {
+                noInputsMessage.style.display = 'block';
+            }
+             userInputsCard.style.display = 'block';
+        }
+
+    } catch (error) {
+        console.error('Error loading user details:', error);
+        loadingMessage.style.display = 'none';
+        authErrorMessage.textContent = `Error: ${error.message}. Redirecting...`;
+        authErrorMessage.style.display = 'block';
+        localStorage.removeItem('accessToken'); // Clear token on error
+        localStorage.removeItem('tokenType');
+        setTimeout(() => { window.location.href = "{{ url_for('login_page_render') }}"; }, 3000);
+    }
+});
+</script>
+{% endblock %}

app/templates/auth/login.html

@@ -0,0 +1,133 @@
+{% extends "partials/base.html" %}
+
+{% block title %}Login - Financial Advisor{% endblock %}
+
+{% block content %}
+<div class="row justify-content-center">
+    <div class="col-md-6 col-lg-4">
+        <div class="card shadow-sm">
+            <div class="card-body">
+                <h2 class="card-title text-center mb-4">Login</h2>
+                
+                {% if request.query_params.get("error") %}
+                <div class="alert alert-danger" role="alert">
+                    {{ request.query_params.get("error") }}
+                </div>
+                {% endif %}
+                {% if request.query_params.get("message") %}
+                <div class="alert alert-info" role="alert">
+                    {{ request.query_params.get("message") }}
+                </div>
+                {% endif %}
+
+                <form id="loginForm" method="post">
+                    <!-- FastAPI's OAuth2PasswordRequestForm expects 'username' and 'password' -->
+                    <div class="mb-3">
+                        <label for="email" class="form-label">Email address</label>
+                        <input type="email" class="form-control" id="email" name="username" required>
+                    </div>
+                    <div class="mb-3">
+                        <label for="password" class="form-label">Password</label>
+                        <div class="input-group">
+                            <input type="password" class="form-control" id="password" name="password" required>
+                            <button class="btn btn-outline-secondary" type="button" id="togglePassword">Show</button>
+                        </div>
+                    </div>
+                    <div class="d-grid">
+                        <button type="submit" class="btn btn-primary">Login</button>
+                    </div>
+                </form>
+                <p class="mt-3 text-center">
+                    Don't have an account? <a href="{{ url_for('signup_page_render') }}">Sign up here</a>
+                </p>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts_extra %}
+<script>
+document.addEventListener('DOMContentLoaded', function () {
+    const loginForm = document.getElementById('loginForm');
+    const togglePasswordButton = document.getElementById('togglePassword');
+    const passwordInput = document.getElementById('password');
+
+    if (togglePasswordButton && passwordInput) {
+        togglePasswordButton.addEventListener('click', function () {
+            const type = passwordInput.getAttribute('type') === 'password' ? 'text' : 'password';
+            passwordInput.setAttribute('type', type);
+            this.textContent = type === 'password' ? 'Show' : 'Hide';
+        });
+    }
+
+    if (loginForm) {
+        loginForm.addEventListener('submit', async function (event) {
+            event.preventDefault();
+            const formData = new FormData(loginForm);
+            
+            // We will post to /auth/token as expected by OAuth2PasswordRequestForm
+            try {
+                const response = await fetch("{{ url_for('login_for_access_token') }}", {
+                    method: 'POST',
+                    body: formData // FormData will be correctly encoded as application/x-www-form-urlencoded
+                });
+
+                const result = await response.json();
+
+                if (response.ok) {
+                    localStorage.setItem('accessToken', result.access_token);
+                    localStorage.setItem('tokenType', result.token_type);
+
+                    // Fetch user details to check if admin for redirect
+                    try {
+                        const userMeResponse = await fetch("{{ url_for('read_users_me') }}", {
+                            method: 'GET',
+                            headers: {
+                                'Authorization': `Bearer ${result.access_token}`
+                            }
+                        });
+                        if (userMeResponse.ok) {
+                            const userData = await userMeResponse.json();
+                            if (userData.is_admin) {
+                                window.location.href = "{{ url_for('admin_dashboard_shell') }}"; // Corrected route name
+                            } else {
+                                window.location.href = "{{ url_for('user_homepage') }}";
+                            }
+                        } else {
+                            // Fallback to user homepage if /users/me fails, or show error
+                            console.error("Failed to fetch user details for redirect.");
+                            window.location.href = "{{ url_for('user_homepage') }}";
+                        }
+                    } catch (e) {
+                        console.error("Error fetching user details:", e);
+                        window.location.href = "{{ url_for('user_homepage') }}"; // Fallback
+                    }
+                } else {
+                    // Display error message
+                    const errorDiv = document.createElement('div');
+                    errorDiv.className = 'alert alert-danger mt-3';
+                    errorDiv.textContent = result.detail || 'Login failed. Please check your credentials.';
+                    // Clear previous errors
+                    const existingError = loginForm.querySelector('.alert-danger');
+                    if (existingError) {
+                        existingError.remove();
+                    }
+                    loginForm.prepend(errorDiv);
+                }
+            } catch (error) {
+                console.error('Login error:', error);
+                const errorDiv = document.createElement('div');
+                errorDiv.className = 'alert alert-danger mt-3';
+                errorDiv.textContent = 'An unexpected error occurred. Please try again.';
+                const existingError = loginForm.querySelector('.alert-danger');
+                if (existingError) {
+                    existingError.remove();
+                }
+                loginForm.prepend(errorDiv);
+            }
+        });
+    }
+});
+</script>
+{% endblock %}

app/templates/auth/signup.html

@@ -0,0 +1,121 @@
+{% extends "partials/base.html" %}
+
+{% block title %}Sign Up - Financial Advisor{% endblock %}
+
+{% block content %}
+<div class="row justify-content-center">
+    <div class="col-md-6 col-lg-4">
+        <div class="card shadow-sm">
+            <div class="card-body">
+                <h2 class="card-title text-center mb-4">Create Account</h2>
+                <form id="signupForm" method="post">
+                    <div class="mb-3">
+                        <label for="email" class="form-label">Email address</label>
+                        <input type="email" class="form-control" id="email" name="email" required>
+                    </div>
+                    <div class="mb-3">
+                        <label for="password" class="form-label">Password</label>
+                        <div class="input-group">
+                            <input type="password" class="form-control" id="password" name="password" required>
+                            <button class="btn btn-outline-secondary" type="button" id="togglePassword">Show</button>
+                        </div>
+                    </div>
+                    <div class="mb-3">
+                        <label for="confirm_password" class="form-label">Confirm Password</label>
+                        <div class="input-group">
+                            <input type="password" class="form-control" id="confirm_password" name="confirm_password" required>
+                            <button class="btn btn-outline-secondary" type="button" id="toggleConfirmPassword">Show</button>
+                        </div>
+                    </div>
+                    <div class="d-grid">
+                        <button type="submit" class="btn btn-primary">Sign Up</button>
+                    </div>
+                </form>
+                <p class="mt-3 text-center">
+                    Already have an account? <a href="{{ url_for('login_page_render') }}">Login here</a>
+                </p>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts_extra %}
+<script>
+document.addEventListener('DOMContentLoaded', function () {
+    const signupForm = document.getElementById('signupForm');
+    
+    const togglePasswordButton = document.getElementById('togglePassword');
+    const passwordInput = document.getElementById('password');
+    const toggleConfirmPasswordButton = document.getElementById('toggleConfirmPassword');
+    const confirmPasswordInput = document.getElementById('confirm_password');
+
+    function setupPasswordToggle(button, input) {
+        if (button && input) {
+            button.addEventListener('click', function () {
+                const type = input.getAttribute('type') === 'password' ? 'text' : 'password';
+                input.setAttribute('type', type);
+                this.textContent = type === 'password' ? 'Show' : 'Hide';
+            });
+        }
+    }
+    setupPasswordToggle(togglePasswordButton, passwordInput);
+    setupPasswordToggle(toggleConfirmPasswordButton, confirmPasswordInput);
+
+    if (signupForm) {
+        signupForm.addEventListener('submit', async function (event) {
+            event.preventDefault();
+            
+            const email = document.getElementById('email').value;
+            const password = passwordInput.value;
+            const confirm_password = confirmPasswordInput.value;
+
+            if (password !== confirm_password) {
+                displayError('Passwords do not match.');
+                return;
+            }
+
+            const formData = {
+                email: email,
+                password: password,
+                confirm_password: confirm_password
+            };
+
+            try {
+                const response = await fetch("{{ url_for('signup_user') }}", { // Endpoint from auth.py
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json',
+                    },
+                    body: JSON.stringify(formData)
+                });
+
+                const result = await response.json();
+
+                if (response.ok) {
+                    // Redirect to login page with a success message
+                    window.location.href = "{{ url_for('login_page_render') }}?message=Signup successful! Please login.";
+                } else {
+                    displayError(result.detail || 'Signup failed. Please try again.');
+                }
+            } catch (error) {
+                console.error('Signup error:', error);
+                displayError('An unexpected error occurred. Please try again.');
+            }
+        });
+    }
+
+    function displayError(message) {
+        const errorDiv = document.createElement('div');
+        errorDiv.className = 'alert alert-danger mt-3';
+        errorDiv.textContent = message;
+        // Clear previous errors
+        const existingError = signupForm.querySelector('.alert-danger');
+        if (existingError) {
+            existingError.remove();
+        }
+        signupForm.prepend(errorDiv);
+    }
+});
+</script>
+{% endblock %}

app/templates/partials/base.html

@@ -0,0 +1,30 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>{{ title }} - Financial Advisor</title>
+    <!-- Bootstrap CSS CDN -->
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha1/dist/css/bootstrap.min.css" rel="stylesheet">
+    <!-- Custom CSS -->
+    <link rel="stylesheet" href="{{ url_for('static', path='/css/style.css') }}">
+    {% block head_extra %}{% endblock %}
+</head>
+<body>
+    {% include 'partials/navbar.html' %}
+
+    <div class="container mt-4">
+        {# Removed get_flashed_messages block as it's not standard in FastAPI/Jinja2 alone #}
+        {# Specific pages can handle messages via query params or JS #}
+        {% block content %}{% endblock %}
+    </div>
+
+    {% include 'partials/footer.html' %}
+
+    <!-- Bootstrap JS Bundle CDN (includes Popper) -->
+    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha1/dist/js/bootstrap.bundle.min.js"></script>
+    <!-- Custom JS -->
+    <script src="{{ url_for('static', path='/js/script.js') }}"></script>
+    {% block scripts_extra %}{% endblock %}
+</body>
+</html>

app/templates/partials/footer.html

@@ -0,0 +1,6 @@
+<footer class="bg-light text-center text-lg-start mt-auto py-3">
+    <div class="text-center p-3" style="background-color: rgba(0, 0, 0, 0.05);">
+        © 2024 Financial Advisor Chatbot. All Rights Reserved.
+        <p class="small mt-1">Disclaimer: This tool is for educational purposes only and does not constitute financial advice. Consult with a qualified professional before making investment decisions.</p>
+    </div>
+</footer>

app/templates/partials/navbar.html

@@ -0,0 +1,44 @@
+<nav class="navbar navbar-expand-lg navbar-dark bg-primary">
+    <div class="container-fluid">
+        <a class="navbar-brand" href="{{ url_for('read_root') }}">FinAdvisor</a>
+        <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
+            <span class="navbar-toggler-icon"></span>
+        </button>
+        <div class="collapse navbar-collapse" id="navbarNav">
+            <ul class="navbar-nav me-auto mb-2 mb-lg-0">
+                {% if request.state.user %} {# Check if user is available in request state (set by middleware or dependency) #}
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.url.path == url_for('user_homepage') %}active{% endif %}" href="{{ url_for('user_homepage') }}">Home</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.url.path == url_for('recommendations_page') %}active{% endif %}" href="{{ url_for('recommendations_page') }}">Market Trends</a>
+                    </li>
+                    {% if request.state.user.is_admin %}
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.url.path.startswith(url_for('admin_dashboard')) %}active{% endif %}" href="{{ url_for('admin_dashboard') }}">Admin Dashboard</a>
+                    </li>
+                    {% endif %}
+                {% endif %}
+            </ul>
+            <ul class="navbar-nav ms-auto">
+                {# Logged-in user items - Initially hidden, shown by JS if token exists #}
+                <li class="nav-item" id="navWelcomeItem" style="display: none;">
+                    <span class="navbar-text me-2" id="navUserWelcome">
+                        Welcome!
+                    </span>
+                </li>
+                <li class="nav-item" id="navLogoutItem" style="display: none;">
+                    <a class="nav-link" href="#" id="logoutButton">Logout</a>
+                </li>
+                
+                {# Logged-out user items - Initially shown, hidden by JS if token exists #}
+                <li class="nav-item" id="navLoginItem">
+                    <a class="nav-link {% if request.url.path == url_for('login_page_render') or request.url.path == url_for('read_root') %}active{% endif %}" href="{{ url_for('login_page_render') }}">Login</a>
+                </li>
+                <li class="nav-item" id="navSignupItem">
+                    <a class="nav-link {% if request.url.path == url_for('signup_page_render') %}active{% endif %}" href="{{ url_for('signup_page_render') }}">Sign Up</a>
+                </li>
+            </ul>
+        </div>
+    </div>
+</nav>

app/templates/user/chatbot.html

@@ -0,0 +1,362 @@
+{% extends "partials/base.html" %}
+
+{% block title %}{{ title }} - Chatbot{% endblock %}
+
+{% block head_extra %}
+{# Chat styles moved to static/css/style.css #}
+{% endblock %}
+
+{% block content %}
+<div class="container mt-5">
+    <div class="row justify-content-center">
+        <div class="col-md-8 col-lg-7">
+            <div class="card shadow-sm">
+                <div class="card-header bg-primary text-white d-flex justify-content-between align-items-center">
+                    <h4 class="mb-0">{{ title }}</h4>
+                    <button id="cancelButton" class="btn btn-sm btn-danger" style="display: none;">Cancel</button>
+                </div>
+                <div class="card-body">
+                    <div id="chatbox">
+                        <!-- Chat messages appear here -->
+                    </div>
+                    
+                    <div id="startArea">
+                         <p>Click "Start" to begin the financial assessment.</p>
+                         <button id="startButton" class="btn btn-success">Start</button>
+                    </div>
+
+                    <div id="userInputArea" class="mt-3" style="display: none;">
+                        {# Input elements will be dynamically added here by JS #}
+                        <input type="text" id="textInput" class="form-control" placeholder="Type your answer...">
+                        <div id="optionsContainer"></div> {# For buttons #}
+                        <button id="sendButton" class="btn btn-primary">Send</button>
+                    </div>
+
+                    <div id="resultArea" class="mt-4" style="display:none;">
+                        <h4>Recommendation:</h4>
+                        <div id="recommendationDetails" class="p-3 bg-light rounded"></div>
+                        <div id="justificationArea" class="mt-3" style="display:none;">
+                            <h5>Justification:</h5>
+                            <ul id="justificationList" class="list-group"></ul>
+                        </div>
+                        <div id="tipsArea" class="mt-3" style="display:none;">
+                            <h5>Financial Tips:</h5>
+                            <ul id="tipsList" class="list-group"></ul>
+                        </div>
+                         <div id="errorArea" class="alert alert-danger mt-3" style="display:none;"></div>
+                    </div>
+                </div>
+            </div>
+             <div class="mt-3">
+                <a href="{{ url_for('user_homepage') }}" class="btn btn-secondary">&laquo; Back to Model Selection</a>
+            </div>
+        </div>
+    </div>
+</div>
+
+{# Pass form fields data to JavaScript #}
+<script id="formFieldsData" type="application/json">
+    {{ form_fields | tojson | safe }}
+</script>
+{% endblock %}
+
+{% block scripts_extra %}
+<script>
+document.addEventListener('DOMContentLoaded', function () {
+    // --- DOM Elements ---
+    const chatbox = document.getElementById('chatbox');
+    const startArea = document.getElementById('startArea');
+    const startButton = document.getElementById('startButton');
+    const userInputArea = document.getElementById('userInputArea');
+    const textInput = document.getElementById('textInput');
+    const optionsContainer = document.getElementById('optionsContainer');
+    const sendButton = document.getElementById('sendButton');
+    const cancelButton = document.getElementById('cancelButton');
+    const resultArea = document.getElementById('resultArea');
+    const recommendationDetails = document.getElementById('recommendationDetails');
+    const justificationArea = document.getElementById('justificationArea');
+    const justificationList = document.getElementById('justificationList');
+    const tipsArea = document.getElementById('tipsArea');
+    const tipsList = document.getElementById('tipsList');
+    const errorArea = document.getElementById('errorArea');
+
+    // --- State Variables ---
+    let formFields = [];
+    let currentQuestionIndex = -1;
+    let collectedAnswers = {};
+    let isWaitingForUserInput = false;
+    const modelType = "{{ model_type }}"; // Get model type from Jinja2 context
+
+    // --- Initialization ---
+    function initializeChat() {
+        try {
+            const fieldsDataElement = document.getElementById('formFieldsData');
+            if (fieldsDataElement) {
+                formFields = JSON.parse(fieldsDataElement.textContent);
+            } else {
+                console.error("Form fields data not found.");
+                addBotMessage("Sorry, I couldn't load the questions. Please try again later.");
+                return;
+            }
+        } catch (e) {
+            console.error("Error parsing form fields data:", e);
+            addBotMessage("Sorry, there was an error setting up the chat. Please try again later.");
+            return;
+        }
+        
+        addBotMessage(`Hello! I'm the ${modelType.charAt(0).toUpperCase() + modelType.slice(1)} Model Advisor. Let's get started.`);
+        startArea.style.display = 'block';
+        userInputArea.style.display = 'none';
+        cancelButton.style.display = 'none';
+        resultArea.style.display = 'none';
+        currentQuestionIndex = -1;
+        collectedAnswers = {};
+        isWaitingForUserInput = false;
+    }
+
+    // --- Chat Helper Functions ---
+    function addMessage(sender, text) {
+        const messageDiv = document.createElement('div');
+        messageDiv.classList.add('chat-message', sender === 'bot' ? 'bot-message' : 'user-message');
+        
+        const bubble = document.createElement('div');
+        bubble.classList.add('message-bubble');
+        bubble.textContent = text;
+        
+        messageDiv.appendChild(bubble);
+        chatbox.appendChild(messageDiv);
+        chatbox.scrollTop = chatbox.scrollHeight; // Auto-scroll
+    }
+
+    function addBotMessage(text) {
+        addMessage('bot', text);
+    }
+
+    function addUserMessage(text) {
+        addMessage('user', text);
+    }
+
+    // --- Chat Flow Functions ---
+    function startChat() {
+        startArea.style.display = 'none';
+        cancelButton.style.display = 'block';
+        currentQuestionIndex = 0;
+        collectedAnswers = {}; // Reset answers
+        askNextQuestion();
+    }
+
+    function askNextQuestion() {
+        if (currentQuestionIndex >= formFields.length) {
+            submitAnswers();
+            return;
+        }
+
+        const field = formFields[currentQuestionIndex];
+        addBotMessage(field.label + (field.required ? "" : " (Optional)")); // Don't add asterisk, just ask
+
+        setupUserInput(field);
+        isWaitingForUserInput = true;
+    }
+
+    function setupUserInput(field) {
+        userInputArea.style.display = 'flex';
+        textInput.style.display = 'none';
+        optionsContainer.innerHTML = ''; // Clear previous options
+        optionsContainer.style.display = 'none';
+        sendButton.style.display = 'none'; // Hide send button by default
+
+        textInput.type = field.type === 'number' ? 'number' : 'text';
+        textInput.value = ''; // Clear previous input
+        textInput.min = field.min !== undefined ? field.min : '';
+        textInput.placeholder = `Enter ${field.label}...`;
+        
+        if (field.type === 'select') {
+            optionsContainer.style.display = 'block';
+            field.options.forEach(option => {
+                const button = document.createElement('button');
+                button.classList.add('btn', 'btn-outline-secondary', 'option-button');
+                button.textContent = option;
+                button.type = 'button'; // Prevent form submission
+                button.onclick = () => handleUserInput(option);
+                optionsContainer.appendChild(button);
+            });
+        } else {
+            textInput.style.display = 'block';
+            sendButton.style.display = 'block';
+            textInput.focus();
+        }
+    }
+
+    function handleUserInput(value) {
+        if (!isWaitingForUserInput) return;
+
+        const field = formFields[currentQuestionIndex];
+        let processedValue = value;
+
+        // --- Client-side Validation ---
+        if (field.required && (value === null || value === undefined || value === '')) {
+            addBotMessage("This field is required. Please provide a value.");
+            return; // Re-ask same question implicitly by not advancing index
+        }
+
+        if (field.type === 'number') {
+            processedValue = parseFloat(value);
+            if (isNaN(processedValue)) {
+                 addBotMessage("Please enter a valid number.");
+                 return;
+            }
+            if (field.min !== undefined && processedValue < field.min) {
+                 addBotMessage(`Value cannot be less than ${field.min}.`);
+                 return;
+            }
+             // Specific check for Salary/Expenses
+            if (field.name === 'Expenses' && (modelType === 'base' || modelType === 'enhanced')) {
+                const salary = collectedAnswers['Salary']; // Get previously collected salary
+                if (salary !== undefined && processedValue > salary) {
+                    addBotMessage("Warning: Your monthly expenses exceed your monthly salary. Please review your budget or update the values.");
+                    // Optional: Force re-entry or ask for confirmation
+                    // For now, just warn and proceed. A better UX might involve more steps.
+                }
+            }
+        }
+        
+        // --- Store Answer and Proceed ---
+        isWaitingForUserInput = false; // Stop accepting input until next question
+        userInputArea.style.display = 'none'; // Hide input area while processing/asking next
+        
+        addUserMessage(value); // Show user's valid input in chat
+        collectedAnswers[field.name] = processedValue;
+        
+        currentQuestionIndex++;
+        
+        // Add a slight delay before asking next question
+        setTimeout(askNextQuestion, 500); 
+    }
+
+    async function submitAnswers() {
+        addBotMessage("Thanks! Processing your information...");
+        cancelButton.style.display = 'none'; // Can't cancel now
+        userInputArea.style.display = 'none';
+
+        const requestPayload = {
+            model_type: modelType,
+            inputs: collectedAnswers
+        };
+
+        const accessToken = localStorage.getItem('accessToken');
+        if (!accessToken) {
+            addBotMessage('Authentication error. Please login again.');
+            // Redirect?
+            return;
+        }
+
+        try {
+            const response = await fetch("{{ url_for('api_chatbot_interact') }}", {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                    'Authorization': `Bearer ${accessToken}`
+                },
+                body: JSON.stringify(requestPayload)
+            });
+
+            const result = await response.json();
+            displayResults(result);
+
+        } catch (error) {
+            console.error('Chatbot submission error:', error);
+            displayError('An unexpected error occurred while getting your advice.');
+        }
+    }
+    
+    function displayResults(result) {
+        resultArea.style.display = 'block';
+        errorArea.style.display = 'none';
+        justificationArea.style.display = 'none';
+        tipsArea.style.display = 'none';
+        recommendationDetails.innerHTML = '';
+        justificationList.innerHTML = '';
+        tipsList.innerHTML = '';
+
+        if (result.recommendation && result.recommendation.error) {
+             displayError(`Error: ${result.recommendation.error}`);
+        } else if (result.detail) { // Handle FastAPI validation errors etc.
+             displayError(`Error: ${result.detail}`);
+        } else {
+            addBotMessage("Here is your personalized recommendation:");
+
+            // Display Recommendation
+             if (result.recommendation.message) { // For cases like no savings
+                 recommendationDetails.innerHTML = `<p class="text-info">${result.recommendation.message}</p>`;
+            } else {
+                const ul = document.createElement('ul');
+                ul.className = 'list-group';
+                for (const [asset, percentage] of Object.entries(result.recommendation)) {
+                    const li = document.createElement('li');
+                    li.className = 'list-group-item d-flex justify-content-between align-items-center';
+                    li.textContent = asset;
+                    const span = document.createElement('span');
+                    span.className = 'badge bg-primary rounded-pill';
+                    span.textContent = `${percentage}%`;
+                    li.appendChild(span);
+                    ul.appendChild(li);
+                }
+                recommendationDetails.appendChild(ul);
+            }
+
+            // Display Justification (Rule-based)
+            if (result.justification && result.justification.length > 0) {
+                result.justification.forEach(item => {
+                    const li = document.createElement('li');
+                    li.className = 'list-group-item';
+                    li.textContent = item;
+                    justificationList.appendChild(li);
+                });
+                justificationArea.style.display = 'block';
+            }
+
+            // Display Tips (Rule-based)
+            if (result.tips && result.tips.length > 0) {
+                result.tips.forEach(item => {
+                    const li = document.createElement('li');
+                    li.className = 'list-group-item';
+                    li.textContent = item;
+                    tipsList.appendChild(li);
+                });
+                tipsArea.style.display = 'block';
+            }
+        }
+         chatbox.scrollTop = chatbox.scrollHeight; // Scroll to show results
+    }
+
+    function displayError(message) {
+         errorArea.textContent = message;
+         errorArea.style.display = 'block';
+         resultArea.style.display = 'block'; // Show result area to display the error
+         recommendationDetails.innerHTML = ''; // Clear any partial results
+         justificationArea.style.display = 'none';
+         tipsArea.style.display = 'none';
+         chatbox.scrollTop = chatbox.scrollHeight;
+    }
+
+    function cancelChat() {
+         addBotMessage("Chat cancelled. No data was saved.");
+         initializeChat(); // Reset to initial state
+    }
+
+    // --- Event Listeners ---
+    startButton.addEventListener('click', startChat);
+    cancelButton.addEventListener('click', cancelChat);
+    sendButton.addEventListener('click', () => handleUserInput(textInput.value));
+    textInput.addEventListener('keypress', function (e) {
+        if (e.key === 'Enter') {
+            e.preventDefault(); // Prevent form submission if inside a form tag
+            handleUserInput(textInput.value);
+        }
+    });
+
+    // --- Initial Setup ---
+    initializeChat();
+});
+</script>
+{% endblock %}

app/templates/user/homepage.html

@@ -0,0 +1,137 @@
+{% extends "partials/base.html" %}
+
+{% block title %}User Dashboard - Financial Advisor{% endblock %}
+
+{% block content %}
+<div class="container mt-5">
+    <div class="row mb-4">
+        <div class="col">
+            <h1>Welcome, <span id="userEmailPlaceholder"></span>!</h1>
+            <p>Choose a financial advisory model to get started.</p>
+        </div>
+    </div>
+
+    <div id="modelSelectionCards" class="row row-cols-1 row-cols-md-3 g-4" style="display: none;">
+        <div class="col">
+            <div class="card h-100 shadow-sm">
+                <div class="card-body d-flex flex-column">
+                    <h5 class="card-title">Base Model Advisor</h5>
+                    <p class="card-text">Get a fundamental portfolio recommendation based on your salary, expenses, savings, lifecycle stage, risk appetite, and investment horizon.</p>
+                    <a href="{{ url_for('chatbot_page', model_type='base') }}" class="btn btn-primary mt-auto">Start with Base Model</a>
+                </div>
+            </div>
+        </div>
+        <div class="col">
+            <div class="card h-100 shadow-sm">
+                <div class="card-body d-flex flex-column">
+                    <h5 class="card-title">Enhanced Model Advisor</h5>
+                    <p class="card-text">Receive a more detailed portfolio recommendation, incorporating your profession and city in addition to the base model inputs.</p>
+                    <a href="{{ url_for('chatbot_page', model_type='enhanced') }}" class="btn btn-success mt-auto">Start with Enhanced Model</a>
+                </div>
+            </div>
+        </div>
+        <div class="col">
+            <div class="card h-100 shadow-sm">
+                <div class="card-body d-flex flex-column">
+                    <h5 class="card-title">Rule-Based Advisor</h5>
+                    <p class="card-text">Get advice based on a predefined set of financial rules and best practices, considering your profile and financial details.</p>
+                    <a href="{{ url_for('chatbot_page', model_type='rule_based') }}" class="btn btn-info mt-auto">Start with Rule-Based Model</a>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="quickLinksSection" class="row mt-5" style="display: none;">
+        <div class="col">
+            <h4>Quick Links</h4>
+            <ul class="list-group">
+                <li class="list-group-item"><a href="{{ url_for('recommendations_page') }}">View Market Trends & Recommendations</a></li>
+                <!-- Add more links here if needed, e.g., to view past interactions -->
+            </ul>
+        </div>
+    </div>
+    <div id="loadingMessage" class="alert alert-info" role="alert" style="display: none;">
+        Loading user data...
+    </div>
+    <div id="authErrorMessage" class="alert alert-danger" role="alert" style="display: none;">
+        Authentication failed. Redirecting to login...
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts_extra %}
+<script>
+document.addEventListener('DOMContentLoaded', async function () {
+    const userEmailPlaceholder = document.getElementById('userEmailPlaceholder');
+    const modelSelectionCards = document.getElementById('modelSelectionCards');
+    const quickLinksSection = document.getElementById('quickLinksSection');
+    const loadingMessage = document.getElementById('loadingMessage');
+    const authErrorMessage = document.getElementById('authErrorMessage');
+
+    const accessToken = localStorage.getItem('accessToken');
+
+    if (!accessToken) {
+        authErrorMessage.textContent = 'No authentication token found. Redirecting to login...';
+        authErrorMessage.style.display = 'block';
+        setTimeout(() => {
+            window.location.href = "{{ url_for('login_page_render') }}";
+        }, 2000);
+        return;
+    }
+
+    loadingMessage.style.display = 'block';
+
+    try {
+        const response = await fetch("{{ url_for('read_users_me') }}", { // /auth/users/me
+            method: 'GET',
+            headers: {
+                'Authorization': `Bearer ${accessToken}`,
+                'Accept': 'application/json'
+            }
+        });
+
+        loadingMessage.style.display = 'none';
+
+        if (response.ok) {
+            const userData = await response.json();
+            if (userEmailPlaceholder) {
+                userEmailPlaceholder.textContent = userData.email;
+            }
+            // Show content that depends on authentication
+            if(modelSelectionCards) modelSelectionCards.style.display = 'flex'; // Or 'block' if that's better for layout
+            if(quickLinksSection) quickLinksSection.style.display = 'block';
+
+            // Update navbar if needed (more complex, requires navbar to have placeholders)
+            // For example, if navbar had <span id="navUserEmail"></span>
+            // const navUserEmail = document.getElementById('navUserEmail');
+            // if (navUserEmail) navUserEmail.textContent = `Welcome, ${userData.email}`;
+            // const logoutLink = document.getElementById('navLogoutLink'); // Assuming logout link has an ID
+            // if (logoutLink) logoutLink.style.display = 'block';
+            // const loginLink = document.getElementById('navLoginLink');
+            // if (loginLink) loginLink.style.display = 'none';
+
+
+        } else {
+            // Handle 401 or other errors from /auth/users/me
+            localStorage.removeItem('accessToken');
+            localStorage.removeItem('tokenType');
+            authErrorMessage.textContent = `Session expired or invalid. Redirecting to login... (Status: ${response.status})`;
+            authErrorMessage.style.display = 'block';
+            setTimeout(() => {
+                window.location.href = "{{ url_for('login_page_render') }}";
+            }, 3000);
+        }
+    } catch (error) {
+        console.error('Error fetching user data:', error);
+        loadingMessage.style.display = 'none';
+        authErrorMessage.textContent = 'Error loading user data. Redirecting to login...';
+        authErrorMessage.style.display = 'block';
+        localStorage.removeItem('accessToken');
+        localStorage.removeItem('tokenType');
+        setTimeout(() => {
+            window.location.href = "{{ url_for('login_page_render') }}";
+        }, 3000);
+    }
+});
+</script>
+{% endblock %}

app/templates/user/recommendations.html

@@ -0,0 +1,240 @@
+{% extends "partials/base.html" %}
+
+{% block title %}Market Trends & Recommendations - Financial Advisor{% endblock %}
+
+{% block content %}
+<div class="container mt-5">
+    <div class="row mb-4">
+        <div class="col">
+            <h1>Market Trends & Recommendations</h1>
+            <p>Stay updated with the latest market information and our curated recommendations.</p>
+        </div>
+    </div>
+
+    <!-- Current Market Prices -->
+    <div class="row mb-4">
+        <div class="col-md-6">
+            <div class="card shadow-sm">
+                <div class="card-header bg-info text-white">
+                    <h4 class="mb-0">Current Stock Prices</h4>
+                </div>
+                <div class="card-body">
+                    <input type="text" id="stockSearch" class="form-control mb-3" placeholder="Search stocks...">
+                </div>
+                <ul class="list-group list-group-flush" id="stockList">
+                    <!-- Stock search results will be populated here -->
+                </ul>
+                 <!-- Placeholder for Price Information -->
+                <div id="stockPriceInfo" class="card-body" style="display: none;">
+                    <h5>Price Information:</h5>
+                    <div id="priceDetails"></div>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-6 mt-3 mt-md-0">
+            <div class="card shadow-sm">
+                <div class="card-header bg-warning text-dark">
+                    <h4 class="mb-0">Gold Price</h4>
+                </div>
+                <div class="card-body">
+                    {% if market_data.gold_price %}
+                    <p class="card-text fs-5">{{ market_data.gold_price }}</p>
+                    {% else %}
+                    <p class="card-text">Gold price data not available.</p>
+                    {% endif %}
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Recommended Stocks -->
+    <div class="row">
+        <div class="col">
+            <div class="card shadow-sm">
+                <div class="card-header bg-success text-white">
+                    <h4 class="mb-0">Recommended Stocks</h4>
+                </div>
+                <div class="card-body">
+                    {% if market_data.recommended_stocks %}
+                        {% for stock in market_data.recommended_stocks %}
+                        <div class="mb-3 p-2 border-bottom">
+                            <h5>{{ stock.name }}</h5>
+                            <p class="mb-1">{{ stock.reason }}</p>
+                        </div>
+                        {% endfor %}
+                    {% else %}
+                        <p>No specific stock recommendations at this time.</p>
+                    {% endif %}
+                </div>
+            </div>
+        </div>
+    </div>
+    <div class="mt-4">
+        <a href="{{ url_for('user_homepage') }}" class="btn btn-secondary">&laquo; Back to Homepage</a>
+    </div>
+</div>
+
+<!-- Fixed Deposits Section -->
+<div class="container mt-5">
+    <div class="row mb-4">
+        <div class="col">
+            <div class="card shadow-sm">
+                <div class="card-header bg-primary text-white">
+                    <h4 class="mb-0">Fixed Deposit (FD) Interest Rates in India</h4>
+                </div>
+                <div class="table-responsive">
+                    <table class="table table-hover mb-0">
+                        <thead class="table-light">
+                            <tr>
+                                <th scope="col">Bank Name</th>
+                                <th scope="col">Highest Interest Rate</th>
+                                <th scope="col">Tenure</th>
+                                <th scope="col">Senior Citizen Rate (Up to)</th>
+                            </tr>
+                        </thead>
+                        <tbody>
+                            <tr>
+                                <td>Airtel Bank</td>
+                                <td>9.1%</td>
+                                <td>36 to 60 months</td>
+                                <td>Varies (up to 9.1%)</td>
+                            </tr>
+                            <tr>
+                                <td>SBI (State Bank of India)</td>
+                                <td>7.25%</td>
+                                <td>2 to 3 years</td>
+                                <td>Up to 7.40%</td>
+                            </tr>
+                            <tr>
+                                <td>Axis Bank</td>
+                                <td>7.25%</td>
+                                <td>15 months to <2 years</td>
+                                <td>Up to 7.95%</td>
+                            </tr>
+                            <tr>
+                                <td>Indian Post (Post Office)</td>
+                                <td>7.50%</td>
+                                <td>5 years (Post Office TD)</td>
+                                <td>Not specified</td>
+                            </tr>
+                            <tr>
+                                <td>HDFC Bank</td>
+                                <td>7.20%</td>
+                                <td>4 years 7 months (55 months)</td>
+                                <td>Up to 7.75%</td>
+                            </tr>
+                            <tr>
+                                <td>ICICI Bank</td>
+                                <td>7.10%</td>
+                                <td>15 months to 2 years</td>
+                                <td>Up to 7.60%</td>
+                            </tr>
+                            <tr>
+                                <td>Canara Bank</td>
+                                <td>6.70%</td>
+                                <td>2 to 3 years</td>
+                                <td>Up to 7.20%</td>
+                            </tr>
+                            <tr>
+                                <td>PNB (Punjab National Bank)</td>
+                                <td>7.05%</td>
+                                <td>300 days</td>
+                                <td>Up to 7.55%</td>
+                            </tr>
+                        </tbody>
+                    </table>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+
+<script>
+document.addEventListener('DOMContentLoaded', function () {
+    const stockSearchInput = document.getElementById('stockSearch');
+    const stockList = document.getElementById('stockList');
+    const stockPriceInfoDiv = document.getElementById('stockPriceInfo');
+    const priceDetailsDiv = document.getElementById('priceDetails');
+
+    // Clear the initial mock stock list items
+    stockList.innerHTML = '';
+
+    if (stockSearchInput) {
+        stockSearchInput.addEventListener('input', async function () {
+            const searchTerm = stockSearchInput.value;
+            if (searchTerm.length > 1) { // Start searching after 2 characters
+                try {
+                    const response = await fetch(`/user/api/stocks/search?query=${encodeURIComponent(searchTerm)}`);
+                    if (!response.ok) {
+                        throw new Error(`HTTP error! status: ${response.status}`);
+                    }
+                    const tickers = await response.json();
+                    displayStockSearchResults(tickers);
+                } catch (error) {
+                    console.error("Error fetching stock search results:", error);
+                    stockList.innerHTML = '<li class="list-group-item">Error fetching results.</li>';
+                }
+            } else {
+                stockList.innerHTML = ''; // Clear list if search term is too short
+                stockPriceInfoDiv.style.display = 'none'; // Hide price info
+            }
+        });
+    }
+
+    function displayStockSearchResults(tickers) {
+        stockList.innerHTML = ''; // Clear previous results
+        if (tickers.length > 0) {
+            tickers.forEach(ticker => {
+                const listItem = document.createElement('li');
+                listItem.classList.add('list-group-item', 'stock-item');
+                listItem.textContent = ticker;
+                listItem.style.cursor = 'pointer'; // Indicate clickable
+                listItem.addEventListener('click', () => selectStock(ticker));
+                stockList.appendChild(listItem);
+            });
+        } else {
+            stockList.innerHTML = '<li class="list-group-item">No matching stocks found.</li>';
+        }
+    }
+
+    async function selectStock(tickerSymbol) {
+        stockSearchInput.value = tickerSymbol; // Set input value to selected ticker
+        stockList.innerHTML = ''; // Clear search results
+        stockPriceInfoDiv.style.display = 'block'; // Show price info section
+        priceDetailsDiv.innerHTML = 'Loading price data...'; // Loading message
+
+        try {
+            const response = await fetch(`/user/api/stocks/price/${encodeURIComponent(tickerSymbol)}`);
+            if (!response.ok) {
+                 // Attempt to read error message from response body
+                const errorText = await response.text();
+                throw new Error(`HTTP error! status: ${response.status} - ${errorText}`);
+            }
+            const priceData = await response.json();
+            displayStockPrice(tickerSymbol, priceData);
+        } catch (error) {
+            console.error("Error fetching stock price:", error);
+            priceDetailsDiv.innerHTML = `Error fetching price for ${tickerSymbol}.`;
+        }
+    }
+
+    function displayStockPrice(tickerSymbol, priceData) {
+        let html = `<h6>${tickerSymbol}</h6>`;
+        for (const label in priceData) {
+            const [price, date] = priceData[label];
+            if (date) {
+                html += `<p>${label} price on ${date}: <strong>${price}</strong></p>`;
+            } else {
+                 html += `<p>${label} price: <strong>${price}</strong></p>`;
+            }
+        }
+        priceDetailsDiv.innerHTML = html;
+    }
+
+    // Initial state: clear the list and hide price info on page load
+    stockList.innerHTML = '';
+    stockPriceInfoDiv.style.display = 'none';
+
+});
+</script>
+{% endblock %}

app/tickers.txt

@@ -0,0 +1,2099 @@
+20MICRONS.NS
+21STCENMGM.NS
+360ONE.NS
+3IINFOLTD.NS
+3MINDIA.NS
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app/user.py

@@ -0,0 +1,227 @@
+from fastapi import APIRouter, Request, Depends, HTTPException
+from fastapi.responses import HTMLResponse, RedirectResponse
+from sqlalchemy.orm import Session
+
+from app import models, schemas, crud
+from app.database import get_db
+from app.auth import get_current_active_user
+from app.main import templates # Import templates from main.py
+import logging
+import yfinance as yf
+from datetime import datetime, timedelta
+import pytz # for timezone handling
+from typing import List
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+# Define timezone (Asia/Kolkata)
+tz = pytz.timezone('Asia/Kolkata')
+
+# Function to read tickers from tickers.txt
+def get_all_tickers():
+    tickers = []
+    try:
+        with open("app/tickers.txt", "r") as f:
+            tickers = [line.strip() for line in f if line.strip()]
+    except FileNotFoundError:
+        logger.error("tickers.txt not found.")
+    return tickers
+
+# Function to find closest available trading day price on or before the target date
+def get_closest_price(hist, date):
+    # Filter dates in hist index that are <= target date
+    available_dates = hist.index[hist.index <= date]
+    if not available_dates.empty:
+        closest_date = available_dates[-1]
+        return hist.loc[closest_date]['Close'], closest_date.date()
+    else:
+        return None, None
+
+# Function to fetch historical data and calculate prices
+def fetch_stock_prices(ticker_symbol: str):
+    ticker = yf.Ticker(ticker_symbol)
+
+    # Get today's date as timezone-aware datetime
+    today = datetime.now(tz)
+
+    # Define the dates for 1 month, 6 months, 1 year, and 3 years ago (timezone-aware)
+    dates = {
+        "Current": today,
+        "1 Month Ago": today - timedelta(days=30),
+        "6 Months Ago": today - timedelta(days=182),
+        "1 Year Ago": today - timedelta(days=365),
+        "3 Years Ago": today - timedelta(days=3*365),
+    }
+
+    # Fetch historical data for the last 4 years to cover all dates
+    start_date = (today - timedelta(days=4*365)).strftime('%Y-%m-%d')
+    end_date = today.strftime('%Y-%m-%d')
+    hist = ticker.history(start=start_date, end=end_date)
+
+    # The index of hist is timezone-aware, ensure it matches tz
+    if hist.index.tz is None:
+        # If index is naive, localize it
+        hist.index = hist.index.tz_localize(tz)
+    else:
+        # Convert to desired timezone
+        hist.index = hist.index.tz_convert(tz)
+
+    # Retrieve prices for each date
+    prices = {}
+    for label, date in dates.items():
+        price, actual_date = get_closest_price(hist, date)
+        if price is not None:
+            prices[label] = (f"₹{price:.2f}", actual_date.strftime('%Y-%m-%d') if actual_date else None)
+        else:
+            prices[label] = ("No data", None)
+
+    return prices
+
+# API endpoint for searching tickers
+@router.get("/api/stocks/search", response_model=List[str])
+async def search_stocks(query: str):
+    all_tickers = get_all_tickers()
+    # Simple case-insensitive search
+    matching_tickers = [ticker for ticker in all_tickers if query.lower() in ticker.lower()]
+    return matching_tickers
+
+# API endpoint for fetching stock prices
+@router.get("/api/stocks/price/{ticker_symbol}")
+async def get_stock_price(ticker_symbol: str):
+    try:
+        prices = fetch_stock_prices(ticker_symbol)
+        return prices
+    except Exception as e:
+        logger.error(f"Error fetching price for {ticker_symbol}: {e}")
+        raise HTTPException(status_code=500, detail="Could not fetch stock price")
+
+
+@router.get("/home", response_class=HTMLResponse)
+async def user_homepage(request: Request): # Removed: current_user: models.User = Depends(get_current_active_user)
+    # User data will be fetched and displayed by client-side JavaScript
+    return templates.TemplateResponse("user/homepage.html", {
+        "request": request,
+        # "user": current_user, # This will be handled by client-side JS
+        "title": "User Homepage"
+    })
+
+@router.get("/chatbot/{model_type}", response_class=HTMLResponse)
+async def chatbot_page(request: Request, model_type: str): # Removed: current_user: models.User = Depends(get_current_active_user)
+    # Client-side JS on chatbot.html already handles token for API submission.
+    # This route now just serves the page structure.
+    valid_models = ["base", "enhanced", "rule_based"]
+    if model_type.lower() not in valid_models:
+        raise HTTPException(status_code=404, detail="Model type not found")
+
+    # Prepare context for the template based on model_type
+    # This context will help the template render the correct form fields
+    form_fields = []
+    page_title = ""
+
+    if model_type == "base":
+        page_title = "Base Model Advisor"
+        form_fields = [
+            {"name": "Salary", "label": "Monthly Salary (₹)", "type": "number", "required": True, "min": 0},
+            {"name": "Expenses", "label": "Monthly Expenses (₹)", "type": "number", "required": True, "min": 0},
+            {"name": "Savings", "label": "Monthly Savings (₹)", "type": "number", "required": True, "min": 0},
+            {"name": "Lifecycle_Stage", "label": "Lifecycle Stage", "type": "select", "required": True, "options": ["Student", "Early Career", "Mid-Career", "Late Career", "Retired"]},
+            {"name": "Risk_Appetite", "label": "Risk Appetite", "type": "select", "required": True, "options": ["Low", "Medium", "High"]},
+            {"name": "Investment_Horizon", "label": "Investment Horizon", "type": "select", "required": True, "options": ["Short-term", "Medium-term", "Long-term"]},
+        ]
+    elif model_type == "enhanced":
+        page_title = "Enhanced Model Advisor"
+        
+        # Define the options for dropdowns based on provided lists
+        cities = sorted([
+            'Mumbai', 'Delhi', 'Bangalore', 'Hyderabad', 'Pune', 'Chennai', 'Jaipur',
+            'Kochi', 'Kolkata', 'Ahmedabad', 'Gurgaon', 'Lucknow', 'Nagpur', 'Chandigarh',
+            'Surat', 'Indore', 'Bhopal'
+        ])
+        
+        professions = sorted([
+            'Software Engineer', 'Doctor', 'Retired Banker', 'Student', 'Marketing Manager', 'Teacher', 
+            'Freelancer', 'Architect', 'Business Owner', 'Nurse', 'Product Manager', 'CA', 'Data Analyst', 
+            'Retired Professor', 'Journalist', 'Graphic Designer', 'Sales Executive', 'HR Manager', 'Intern',
+            'Dentist', 'Lawyer', 'Content Creator', 'Pensioner', 'UX Designer', 'Government Employee', 
+            'Fashion Designer', 'Startup Founder', 'Homemaker (Investor)', 'Investment Banker', 'IT Consultant',
+            'College Student', 'Pharmacist', 'Textile Business Owner', 'Event Planner', 'Film Producer', 
+            'Nutritionist', 'Retired Army Officer', 'Social Media Manager', 'Airline Pilot', 'Biotech Researcher',
+            'Real Estate Agent', 'AI Engineer', 'Retired Teacher', 'Financial Analyst', 'Software Developer', 
+            'NGO Director', 'Fitness Trainer', 'Digital Marketer', 'Content Strategist', 'Retired Engineer', 
+            'UI Developer', 'Operations Manager', 'Corporate Lawyer', 'School Principal', 'AI Researcher', 
+            'Junior Doctor', 'Finance Manager', 'Fashion Blogger', 'HR Consultant', 'Video Editor', 
+            'Small Business Owner', 'Dietitian', 'Supply Chain Manager', 'Interior Designer', 'Sales Manager', 
+            'PR Executive', 'Logistics Head', 'Content Writer', 'Retired Govt. Employee', 'Marketing Lead', 
+            'IT Manager', 'Startup Intern', 'Export Manager', 'Fitness Instructor', 'Real Estate Broker', 
+            'Event Manager', 'Software Trainee', 'Data Scientist', 'HR Director', 'Hotel Manager', 
+            'Social Worker', 'Cybersecurity Expert', 'E-commerce Manager', 'Bank Manager', 'Retired IT Manager', 
+            'UX Researcher', 'Product Designer', 'Cybersecurity Analyst', 'Podcast Producer', 'E-commerce Seller',
+            'Cloud Architect', 'Corporate Trainer', 'AI Trainer', 'Supply Chain Head', 'Product Owner', 
+            'UI/UX Designer', 'Finance Director', 'Sustainability Consultant', 'Retired Bank Manager', 
+            'Social Media Influencer', 'Blockchain Developer', 'NGO Head', 'Data Engineer', 'Event Curator', 
+            'CTO', 'Content Marketer', 'Retired Army Major', 'AR/VR Developer', 'Logistics Manager', 'VP Sales',
+            'EdTech Founder', 'SEO Specialist', 'Yoga Instructor', 'IT Director', 'App Developer', 
+            'Consultant Cardiologist', 'Freelance Writer', 'Cloud Engineer', 'Retired CA', 'Digital Artist', 
+            'Retired Pilot', 'Graphic Animator', 'AI Ethicist', 'School Trustee', 'Robotics Engineer', 
+            'Fashion Stylist', 'Retired Nurse', 'AI Ethics Consultant', 'Drone Engineer', 'Retired Bank Clerk', 
+            'Digital Nomad', 'CFO', 'Sustainability Analyst', 'Retired Journalist', 'Social Entrepreneur', 
+            'DevOps Engineer', 'School Counselor', 'Retired Army Colonel', 'AR Developer', 'Sales Director', 
+            'EdTech Consultant', 'SEO Expert', 'Cardiologist', '3D Artist', 'HR Head', 'Animator','Other', 
+            'Fashion Influencer'
+        ])
+
+        form_fields = [
+            {"name": "Profession", "label": "Profession", "type": "select", "required": True, "options": professions},
+            {"name": "City", "label": "City", "type": "select", "required": True, "options": cities},
+            {"name": "Salary", "label": "Monthly Salary (₹)", "type": "number", "required": True, "min": 0},
+            {"name": "Expenses", "label": "Monthly Expenses (₹)", "type": "number", "required": True, "min": 0},
+            {"name": "Savings", "label": "Monthly Savings (₹)", "type": "number", "required": True, "min": 0},
+            {"name": "Lifecycle_Stage", "label": "Lifecycle Stage", "type": "select", "required": True, "options": ["Student", "Early Career", "Mid-Career", "Late Career", "Retired"]},
+            {"name": "Risk_Appetite", "label": "Risk Appetite", "type": "select", "required": True, "options": ["Low", "Medium", "High"]},
+            {"name": "Investment_Horizon", "label": "Investment Horizon", "type": "select", "required": True, "options": ["Short-term", "Medium-term", "Long-term"]},
+        ]
+    elif model_type == "rule_based":
+        page_title = "Rule-Based Advisor"
+        form_fields = [
+            {"name": "Lifecycle_Stage", "label": "Lifecycle Stage", "type": "select", "required": True, "options": ["Student", "Early Career", "Mid-Career", "Late Career", "Retired"]},
+            {"name": "Risk_Appetite", "label": "Risk Appetite", "type": "select", "required": True, "options": ["Low", "Medium", "High"]},
+            {"name": "Investment_Horizon", "label": "Investment Horizon", "type": "select", "required": True, "options": ["Short-term", "Medium-term", "Long-term"]},
+            {"name": "Annual_Salary_Package", "label": "Annual Salary Package (CTC) (₹)", "type": "number", "required": True, "min": 0},
+            {"name": "Monthly_In_hand_Salary", "label": "Actual Monthly In-hand Salary (₹)", "type": "number", "required": True, "min": 0},
+            {"name": "Total_Monthly_Expenses", "label": "Total Estimated Monthly Expenses (₹)", "type": "number", "required": True, "min": 0},
+        ]
+        
+    return templates.TemplateResponse("user/chatbot.html", {
+        "request": request,
+        # "user": current_user, # Client-side JS on this page handles API calls with token
+        "title": page_title,
+        "model_type": model_type,
+        "form_fields": form_fields
+    })
+
+@router.get("/recommendations", response_class=HTMLResponse)
+async def recommendations_page(request: Request): # Removed: current_user: models.User = Depends(get_current_active_user)
+    # Client-side JS could be added to this page if it needs to display user-specific info
+    # or make authenticated API calls for dynamic market data.
+    # For now, it serves static structure with mocked data.
+    # Data fetching logic would go into services/market_data_service.py
+    # For now, just rendering a template.
+    market_data = { # Mock data
+        "stocks": [
+            {"name": "Reliance Industries", "price": "2,850.50 INR", "change": "+0.5%"},
+            {"name": "Tata Consultancy Services", "price": "3,900.75 INR", "change": "-0.2%"},
+            {"name": "HDFC Bank", "price": "1,500.20 INR", "change": "+1.1%"},
+        ],
+        "gold_price": "96,172.27 -745.25 INR per 10g",
+        "recommended_stocks": [
+            {"name": "Infosys", "reason": "Strong growth potential in IT sector."},
+            {"name": "ICICI Bank", "reason": "Good financial performance and outlook."}
+        ]
+    }
+    return templates.TemplateResponse("user/recommendations.html", {
+        "request": request,
+        # "user": current_user, # Can be fetched by client-side JS if needed
+        "title": "Market Trends & Recommendations",
+        "market_data": market_data
+    })

app/user_financial_data.csv

@@ -0,0 +1,14 @@
+UserID,UserEmail,ModelType,Timestamp,Name,Age,City,Profession,Salary,Expenses,Savings,Lifecycle Stage,Risk Appetite,Investment Horizon,Annual Salary Package,Monthly In-hand Salary,Total Monthly Expenses,Equity (%),Debt (%),Gold (%),FD/Cash (%)
+2,user1@gmail.com,base,2025-05-08 14:48:49,,,,,60000,20000,40000,Early Career,Medium,Medium-term,,,,35.6,26.3,7.1,31.0
+2,user1@gmail.com,enhanced,2025-05-08 14:50:49,,,Indore,Software Engineer,60000,40000,20000,Late Career,Medium,Medium-term,,,,32.3,40.7,9.0,18.0
+2,user1@gmail.com,rule_based,2025-05-08 14:52:36,,,,,,,,Early Career,Medium,Medium-term,700000,,30000,,,,
+3,user2@gmail.com,base,2025-05-08 15:25:39,,,,,90000,40000,50000,Mid-Career,Medium,Short-term,,,,36.9,27.2,10.5,25.4
+3,user2@gmail.com,enhanced,2025-05-08 15:28:12,,,Pune,Software Engineer,80000,60000,20000,Late Career,High,Long-term,,,,49.5,34.8,3.9,11.8
+4,user3@gmail.com,enhanced,2025-05-08 15:36:16,,,Pune,AI Ethics Consultant,90000,60000,30000,Mid-Career,Medium,Short-term,,,,37.2,27.6,11.5,23.7
+5,user7@gmail.com,base,2025-05-09 19:10:38,,,,,70000,50000,20000,Mid-Career,Medium,Short-term,,,,37.9,26.9,11.7,23.5
+5,user7@gmail.com,enhanced,2025-05-09 19:11:47,,,Bangalore,AI Engineer,80000,40000,40000,Late Career,High,Medium-term,,,,49.8,34.0,4.0,12.2
+5,user7@gmail.com,rule_based,2025-05-09 19:13:52,,,,,,,,Mid-Career,High,Short-term,700000,,40000,,,,
+6,mandar@gmail.com,base,2025-05-09 19:55:19,,,,,60000,40000,20000,Mid-Career,High,Long-term,,,,60.7,19.1,5.0,15.2
+6,mandar@gmail.com,enhanced,2025-05-09 19:56:30,,,Pune,AI Researcher,70000,40000,30000,Mid-Career,High,Short-term,,,,57.4,15.0,4.5,23.1
+6,mandar@gmail.com,rule_based,2025-05-09 19:57:28,,,,,,,,Mid-Career,Medium,Short-term,700000,,40000,,,,
+2,user1@gmail.com,enhanced,2025-05-26 20:39:38,,,Pune,Other,80000,50000,30000,Mid-Career,Medium,Short-term,,,,37.1,27.3,10.7,24.9

diagramcontent.md

@@ -0,0 +1,54 @@
+# Project Module Block Diagrams
+
+## Overall Project
+
+```mermaid
+graph LR
+    A[admin.py] --> B(auth.py: get_current_user, get_current_active_user)
+    A --> C(crud.py: get_user, get_users)
+    B --> D(database.py: get_db)
+    C --> D
+    E[main.py] --> B
+    E --> C
+    E --> H[user.py]
+    F(models.py: User, UserDataInput) --> D
+    G(schemas.py: UserCreate, User) --> F
+    H --> B
+    H --> C
+    I[services: chatbot_service.py, data_service.py] --> E
+    J[ml_models] --> I
+    subgraph app
+    A
+    B
+    C
+    D
+    E
+    F
+    G
+    H
+    I
+    J
+    end
+```
+
+## Admin Side
+
+```mermaid
+graph LR
+    A[admin.py: admin_dashboard_shell, admin_view_user_details_shell] --> B(auth.py: get_current_admin_user)
+    A --> C(crud.py: get_user, get_users)
+    C --> D(database.py: get_db)
+    B --> D
+    style A fill:#f9f,stroke:#333,stroke-width:2px
+```
+
+## User Side
+
+```mermaid
+graph LR
+    H[user.py: user_homepage, chatbot_page, recommendations_page] --> B(auth.py: get_current_active_user)
+    H --> C(crud.py: get_user_data_inputs_by_user_id)
+    C --> D(database.py: get_db)
+    B --> D
+    E[main.py: api_chatbot_interact] --> H
+    style H fill:#ccf,stroke:#333,stroke-width:2px