frontend.py

import streamlit as st
import pandas as pd
import requests
import plotly.express as px
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, f1_score
import sqlite3
import base64
import logging
import os
import numpy as np

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

# Database connection
DB_PATH = "crime_records.db"

def get_db_connection():
    if not os.path.exists(DB_PATH):
        logger.error(f"Database file {DB_PATH} does not exist")
        raise Exception(f"Database file {DB_PATH} not found. Run init_db.py to create it.")
    conn = sqlite3.connect(DB_PATH)
    conn.row_factory = sqlite3.Row
    return conn

# Data analysis function
def load_crime_data(search_query=""):
    try:
        conn = get_db_connection()
        query = "SELECT LOWER(crime_type) AS crime_type, LOWER(description) AS description, LOWER(location) AS location, date, LOWER(officer_in_charge) AS officer_in_charge, LOWER(status) AS status FROM Crimes"
        if search_query:
            query += " WHERE LOWER(crime_type) LIKE ? OR LOWER(location) LIKE ?"
            df = pd.read_sql(query, conn, params=(f"%{search_query.lower()}%", f"%{search_query.lower()}%"))
        else:
            df = pd.read_sql(query, conn)
        conn.close()
        if df.empty:
            logger.warning("No data found in Crimes table")
            st.warning("No crime data available. Please add crimes via 'Add Crime'.")
        return df
    except Exception as e:
        logger.error(f"Error loading crime data: {e}")
        st.error(f"Error loading crime data: {e}. Ensure the database and Crimes table are initialized.")
        return pd.DataFrame()

# ML model training and prediction
def train_ml_models():
    df = load_crime_data()
    if df.empty or len(df) < 5:
        logger.warning("Insufficient data for ML training")
        st.error("Insufficient data for ML training. Please add at least 5 crime records.")
        return None, None, None, None, None, 0, 0, 0, 0
    df = df.dropna()
    if len(df['status'].unique()) < 2:
        logger.warning("Only one status value found; ML models require multiple classes")
        st.error("ML models require at least two different status values (e.g., 'open' and 'closed').")
        return None, None, None, None, None, 0, 0, 0, 0
    le_crime = LabelEncoder()
    le_location = LabelEncoder()
    le_status = LabelEncoder()
    df['crime_type_encoded'] = le_crime.fit_transform(df['crime_type'])
    df['location_encoded'] = le_location.fit_transform(df['location'])
    df['status_encoded'] = le_status.fit_transform(df['status'])
    features = ['crime_type_encoded', 'location_encoded']
    X = df[features]
    y = df['status_encoded']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Random Forest
    rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
    rf_model.fit(X_train, y_train)
    rf_pred = rf_model.predict(X_test)
    rf_accuracy = accuracy_score(y_test, rf_pred)
    rf_f1 = f1_score(y_test, rf_pred, average='weighted')

    # XGBoost
    xgb_model = XGBClassifier(use_label_encoder=False, eval_metric='mlogloss', random_state=42)
    xgb_model.fit(X_train, y_train)
    xgb_pred = xgb_model.predict(X_test)
    xgb_accuracy = accuracy_score(y_test, xgb_pred)
    xgb_f1 = f1_score(y_test, xgb_pred, average='weighted')

    return rf_model, xgb_model, le_crime, le_location, le_status, rf_accuracy, rf_f1, xgb_accuracy, xgb_f1

# Function to set modern styling with background image and white text
def set_background_and_text(image_file):
    try:
        with open(image_file, "rb") as image:
            encoded_image = base64.b64encode(image.read()).decode()
        css = f"""

        <style>

        @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;500;700&display=swap');

        @import url('https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css');



        .stApp {{

            background-image: url("data:image/png;base64,{encoded_image}");

            background-size: cover;

            background-position: center;

            background-repeat: no-repeat;

            background-attachment: fixed;

            font-family: 'Montserrat', sans-serif;

            color: white !important;

        }}

        .stApp > div {{

            background-color: rgba(0, 0, 0, 0.7);

            border-radius: 15px;

            padding: 20px;

            margin: 10px;

            box-shadow: 0 4px 15px rgba(0, 0, 0, 0.5);

        }}

        .stApp * {{

            color: white !important;

        }}

        .stApp h1, .stApp h2, .stApp h3, .stApp h4, .stApp h5, .stApp h6 {{

            color: #00b7eb !important;

            font-weight: 700;

        }}

        .stApp input, .stApp select, .stApp textarea {{

            color: white !important;

            background-color: rgba(30, 30, 30, 0.8) !important;

            border: 2px solid #00b7eb !important;

            border-radius: 8px !important;

            padding: 8px;

            transition: border-color 0.3s ease;

        }}

        .stApp input:focus, .stApp select:focus, .stApp textarea:focus {{

            border-color: #ff073a !important;

            box-shadow: 0 0 8px rgba(255, 7, 58, 0.5);

        }}

        .stApp .stButton>button {{

            color: white !important;

            background-color: #00b7eb !important;

            border: 2px solid #00b7eb !important;

            border-radius: 8px;

            padding: 12px 24px;

            font-weight: 500;

            font-size: 16px;

            transition: all 0.3s ease;

            display: flex;

            align-items: center;

            gap: 8px;

        }}

        .stApp .stButton>button:hover {{

            background-color: #ff073a !important;

            border-color: #ff073a !important;

            transform: translateY(-2px);

            box-shadow: 0 4px 10px rgba(255, 7, 58, 0.3);

        }}

        .stApp .stDataFrame, .stApp table {{

            color: white !important;

            background-color: rgba(30, 30, 30, 0.8) !important;

            border-radius: 8px;

            border: 1px solid #00b7eb;

        }}

        .stApp .stMarkdown p, .stApp .stMarkdown div {{

            color: white !important;

        }}

        .stApp .stSelectbox > div > div > div {{

            color: white !important;

            background-color: rgba(30, 30, 30, 0.8) !important;

            border: 2px solid #00b7eb !important;

            border-radius: 8px;

        }}

        .stSidebar {{

            background-color: rgba(20, 20, 30, 0.9) !important;

            border-right: 2px solid #00b7eb;

        }}

        .stSidebar * {{

            color: white !important;

        }}

        .stSidebar .stSelectbox > div > div > div {{

            background-color: rgba(30, 30, 30, 0.8) !important;

            border: 2px solid #00b7eb !important;

            border-radius: 8px;

        }}

        .stPlotlyChart {{

            background-color: rgba(30, 30, 30, 0.8) !important;

            border-radius: 8px;

            padding: 10px;

        }}

        .search-container {{

            display: flex;

            align-items: center;

            gap: 10px;

        }}

        .search-container input {{

            flex-grow: 1;

        }}

        .fa-icon {{

            margin-right: 8px;

        }}

        .prediction-output {{

            background-color: rgba(30, 30, 30, 0.9);

            border: 2px solid #00b7eb;

            border-radius: 8px;

            padding: 15px;

            margin-top: 20px;

        }}

        </style>

        """
        st.markdown(css, unsafe_allow_html=True)
        logger.info(f"Modern styling and background image set for {image_file}")
    except FileNotFoundError:
        logger.error(f"Background image {image_file} not found")
        st.warning(f"Background image for {image_file} not found.")
        css = """

        <style>

        @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;500;700&display=swap');

        @import url('https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css');



        .stApp {

            font-family: 'Montserrat', sans-serif;

            color: white !important;

            background-color: #1a1a1a;

        }

        .stApp > div {

            background-color: rgba(0, 0, 0, 0.7);

            border-radius: 15px;

            padding: 20px;

            margin: 10px;

            box-shadow: 0 4px 15px rgba(0, 0, 0, 0.5);

        }

        .stApp * {

            color: white !important;

        }

        .stApp h1, .stApp h2, .stApp h3, .stApp h4, .stApp h5, .stApp h6 {

            color: #00b7eb !important;

            font-weight: 700;

        }

        .stApp input, .stApp select, .stApp textarea {

            color: white !important;

            background-color: rgba(30, 30, 30, 0.8) !important;

            border: 2px solid #00b7eb !important;

            border-radius: 8px !important;

            padding: 8px;

            transition: border-color 0.3s ease;

        }

        .stApp input:focus, .stApp select:focus, .stApp textarea:focus {

            border-color: #ff073a !important;

            box-shadow: 0 0 8px rgba(255, 7, 58, 0.5);

        }

        .stApp .stButton>button {

            color: white !important;

            background-color: #00b7eb !important;

            border: 2px solid #00b7eb !important;

            border-radius: 8px;

            padding: 12px 24px;

            font-weight: 500;

            font-size: 16px;

            transition: all 0.3s ease;

            display: flex;

            align-items: center;

            gap: 8px;

        }

        .stApp .stButton>button:hover {

            background-color: #ff073a !important;

            border-color: #ff073a !important;

            transform: translateY(-2px);

            box-shadow: 0 4px 10px rgba(255, 7, 58, 0.3);

        }

        .stApp .stDataFrame, .stApp table {

            color: white !important;

            background-color: rgba(30, 30, 30, 0.8) !important;

            border-radius: 8px;

            border: 1px solid #00b7eb;

        }

        .stApp .stMarkdown p, .stApp .stMarkdown div {

            color: white !important;

        }

        .stApp .stSelectbox > div > div > div {

            color: white !important;

            background-color: rgba(30, 30, 30, 0.8) !important;

            border: 2px solid #00b7eb !important;

            border-radius: 8px;

        }

        .stSidebar {

            background-color: rgba(20, 20, 30, 0.9) !important;

            border-right: 2px solid #00b7eb;

        }

        .stSidebar * {

            color: white !important;

        }

        .stSidebar .stSelectbox > div > div > div {

            background-color: rgba(30, 30, 30, 0.8) !important;

            border: 2px solid #00b7eb !important;

            border-radius: 8px;

        }

        .stPlotlyChart {

            background-color: rgba(30, 30, 30, 0.8) !important;

            border-radius: 8px;

            padding: 10px;

        }

        .search-container {

            display: flex;

            align-items: center;

            gap: 10px;

        }

        .search-container input {

            flex-grow: 1;

        }

        .fa-icon {

            margin-right: 8px;

        }

        .prediction-output {

            background-color: rgba(30, 30, 30, 0.9);

            border: 2px solid #00b7eb;

            border-radius: 8px;

            padding: 15px;

            margin-top: 20px;

        }

        </style>

        """
        st.markdown(css, unsafe_allow_html=True)

# Initialize session state for user authentication
if 'user' not in st.session_state:
    st.session_state.user = None
    st.session_state.role = None
if 'page' not in st.session_state:
    st.session_state.page = "Login"

# Role-based menu options
ROLE_MENUS = {
    "admin": ["Dashboard", "Add Crime", "View Crimes", "Add FIR", "View FIRs", "Data Analysis", "ML Predictions", "Sign Up", "Login", "Logout"],
    "police": ["Dashboard", "View Crimes", "Add FIR", "View FIRs", "ML Predictions", "Login", "Logout"],
    None: ["Login", "Sign Up"]
}

# Sidebar navigation
st.sidebar.header("User Authentication")
auth_choice = st.sidebar.selectbox("Action", ["Login", "Sign Up", "Logout"] if st.session_state.user else ["Login", "Sign Up"], key="auth_choice")

# Signup
if auth_choice == "Sign Up":
    st.header("Sign Up")
    username = st.text_input("Username", help="Enter a unique username")
    password = st.text_input("Password", type="password", help="Enter a secure password")
    role = st.selectbox("Role", ["admin", "police"], help="Select user role: admin or police")
    if st.button("<i class='fas fa-user-plus fa-icon'></i> Register", key="signup", help="Create a new user account"):
        if username and password:
            try:
                response = requests.post("http://localhost:8000/api/users", json={"username": username, "password": password, "role": role})
                response.raise_for_status()
                st.success("User registered successfully! Please log in.")
                st.session_state.page = "Login"
            except requests.RequestException as e:
                st.error(f"Error: {e}")
        else:
            st.error("Please fill in all fields.")

# Login
elif auth_choice == "Login":
    st.header("Login")
    username = st.text_input("Username", help="Enter your username")
    password = st.text_input("Password", type="password", help="Enter your password")
    if st.button("<i class='fas fa-sign-in-alt fa-icon'></i> Login", key="login", help="Log in to the system"):
        if username and password:
            try:
                response = requests.post("http://localhost:8000/api/login", json={"username": username, "password": password})
                response.raise_for_status()
                data = response.json()
                st.session_state.user = data["username"]
                st.session_state.role = data["role"]
                st.success(f"Logged in as {data['username']} ({data['role']})")
                st.session_state.page = "Dashboard"
            except requests.RequestException as e:
                st.error(f"Error: {e}")
        else:
            st.error("Please fill in all fields.")

# Logout
elif auth_choice == "Logout":
    st.session_state.user = None
    st.session_state.role = None
    st.session_state.page = "Login"
    st.success("Logged out successfully!")
    st.rerun()

# Set background image and modern styling
background_images = {
    "Dashboard": "static\mainmenu.jpg",
    "Add Crime": "static\AddCrimes.jpg",
    "View Crimes": "static\ViewCrimes.jpg",
    "Add FIR": "static\AddFirs.jpg",
    "View FIRs": "static\ViewFirs.jpg",
    "Data Analysis": "static\mainmenu.jpg",
    "ML Predictions": "static\AddCrimes.jpg",
    "Sign Up": "static\ViewCrimes.jpg",
    "Login": "static\ViewFirs.jpg"
}

# Apply background and styling
if st.session_state.page in background_images:
    set_background_and_text(background_images[st.session_state.page])
else:
    set_background_and_text(None)

# Main menu navigation (only shown if logged in)
if st.session_state.user:
    st.sidebar.header("Navigation")
    menu = ROLE_MENUS[st.session_state.role]
    choice = st.sidebar.selectbox("Menu", menu, key="main_menu")
else:
    choice = st.session_state.page

# Main app logic
if choice == "Dashboard" and st.session_state.user:
    st.header("Dashboard")
    st.write(f"Welcome, {st.session_state.user} ({st.session_state.role.title()})! Navigate using the sidebar.")

elif choice == "Add Crime" and st.session_state.user and st.session_state.role == "admin":
    st.header("Add Crime")
    crime_type = st.text_input("Crime Type", help="e.g., Cyber Crimes, Theft, Assault")
    description = st.text_area("Description", help="Brief description of the crime")
    location = st.text_input("Location", help="e.g., New York, Chicago")
    date = st.date_input("Date")
    officer = st.text_input("Officer In Charge", help="Name of the assigned officer")
    if st.button("<i class='fas fa-save fa-icon'></i> Submit Crime", key="add_crime", help="Save the crime record"):
        if crime_type and description and location and officer:
            crime_data = {
                "crime_type": crime_type.lower(),
                "description": description.lower(),
                "location": location.lower(),
                "date": str(date),
                "officer_in_charge": officer.lower()
            }
            try:
                response = requests.post("http://localhost:8000/api/crimes", json=crime_data)
                response.raise_for_status()
                st.success("Crime added successfully!")
            except requests.RequestException as e:
                st.error(f"Error: {e}")
        else:
            st.error("Please fill in all fields.")

elif choice == "View Crimes" and st.session_state.user:
    st.header("View Crimes")
    with st.container():
        st.markdown('<div class="search-container">', unsafe_allow_html=True)
        search_query = st.text_input("Search Crimes", placeholder="Search by crime type or location...", help="e.g., Cyber Crimes, New York")
        if st.button("<i class='fas fa-search fa-icon'></i> Search", key="search_crimes"):
            try:
                response = requests.get("http://localhost:8000/api/crimes", params={"search": search_query})
                response.raise_for_status()
                crimes = response.json()
                df = pd.DataFrame(crimes)
                for col in ['crime_type', 'description', 'location', 'officer_in_charge', 'status']:
                    if col in df.columns:
                        df[col] = df[col].str.title()
                st.dataframe(df)
            except requests.RequestException as e:
                st.error(f"Error loading crimes: {e}")
        st.markdown('</div>', unsafe_allow_html=True)

elif choice == "Add FIR" and st.session_state.user:
    st.header("Add FIR")
    crime_id = st.number_input("Crime ID", min_value=1, step=1, help="ID of the associated crime")
    complainant_name = st.text_input("Complainant Name", help="Name of the person filing the FIR")
    complainant_contact = st.text_input("Complainant Contact", help="Email or phone number")
    filing_date = st.date_input("Filing Date")
    if st.button("<i class='fas fa-save fa-icon'></i> Submit FIR", key="add_fir", help="Save the FIR record"):
        if complainant_name and complainant_contact:
            fir_data = {
                "crime_id": crime_id,
                "complainant_name": complainant_name.lower(),
                "complainant_contact": complainant_contact.lower(),
                "filing_date": str(filing_date)
            }
            try:
                response = requests.post("http://localhost:8000/api/firs", json=fir_data)
                response.raise_for_status()
                st.success("FIR added successfully!")
            except requests.RequestException as e:
                st.error(f"Error: {e}")
        else:
            st.error("Please fill in all fields.")

elif choice == "View FIRs" and st.session_state.user:
    st.header("View FIRs")
    with st.container():
        st.markdown('<div class="search-container">', unsafe_allow_html=True)
        search_query = st.text_input("Search FIRs", placeholder="Search by complainant name or contact...", help="e.g., Alice Brown, alice@example.com")
        if st.button("<i class='fas fa-search fa-icon'></i> Search", key="search_firs"):
            try:
                response = requests.get("http://localhost:8000/api/firs", params={"search": search_query})
                response.raise_for_status()
                firs = response.json()
                df = pd.DataFrame(firs)
                for col in ['complainant_name', 'complainant_contact']:
                    if col in df.columns:
                        df[col] = df[col].str.title()
                st.dataframe(df)
            except requests.RequestException as e:
                st.error(f"Error loading FIRs: {e}")
        st.markdown('</div>', unsafe_allow_html=True)

elif choice == "Data Analysis" and st.session_state.user and st.session_state.role == "admin":
    st.header("Data Analysis")
    with st.container():
        st.markdown('<div class="search-container">', unsafe_allow_html=True)
        search_query = st.text_input("Filter Crimes", placeholder="Filter by crime type or location...", help="e.g., Cyber Crimes, New York")
        if st.button("<i class='fas fa-filter fa-icon'></i> Filter", key="filter_analysis"):
            df = load_crime_data(search_query)
        else:
            df = load_crime_data()
        st.markdown('</div>', unsafe_allow_html=True)
        if not df.empty:
            st.subheader("Crime Type Distribution")
            fig = px.histogram(df, x="crime_type", title="Distribution of Crime Types")
            fig.update_layout(
                title_font_color="white",
                xaxis_title_font_color="white",
                yaxis_title_font_color="white",
                font_color="white",
                plot_bgcolor="rgba(0,0,0,0)",
                paper_bgcolor="rgba(0,0,0,0)"
            )
            st.plotly_chart(fig)
            st.subheader("Crime Trends Over Time")
            df['date'] = pd.to_datetime(df['date'])
            df['year_month'] = df['date'].dt.to_period('M')
            trend_data = df.groupby('year_month').size().reset_index(name='count')
            trend_data['year_month'] = trend_data['year_month'].astype(str)
            fig = px.line(trend_data, x='year_month', y='count', title="Crime Trends Over Time")
            fig.update_layout(
                title_font_color="white",
                xaxis_title_font_color="white",
                yaxis_title_font_color="white",
                font_color="white",
                plot_bgcolor="rgba(0,0,0,0)",
                paper_bgcolor="rgba(0,0,0,0)"
            )
            st.plotly_chart(fig)
        else:
            st.error("No crime data available for analysis. Please add crimes or check the database.")

elif choice == "ML Predictions" and st.session_state.user:
    st.header("Predict Crime Status")
    st.markdown("""

    This feature uses advanced machine learning models (Random Forest and XGBoost) to predict whether a crime case is likely to be 'Open' or 'Closed' based on its type and location. 

    The prediction can help prioritize investigations or allocate resources effectively. Select a model, crime type, and location, then click Predict to see the result.

    """)

    try:
        rf_model, xgb_model, le_crime, le_location, le_status, rf_accuracy, rf_f1, xgb_accuracy, xgb_f1 = train_ml_models()
        if rf_model is None:
            st.error("No data available for ML predictions. Please add at least 5 crime records with varied statuses (e.g., 'open' and 'closed').")
        else:
            # Model Performance
            st.subheader("Model Performance")
            st.write(f"**Random Forest**: Accuracy: {rf_accuracy:.2f}, F1-Score: {rf_f1:.2f} (measures model reliability)")
            st.write(f"**XGBoost**: Accuracy: {xgb_accuracy:.2f}, F1-Score: {xgb_f1:.2f} (measures model reliability)")
            st.markdown("**Accuracy**: Percentage of correct predictions. **F1-Score**: Balances precision and recall for robust evaluation.")

            # Training Data Preview
            st.subheader("Training Data Preview")
            df = load_crime_data()
            if not df.empty:
                preview_df = df[['crime_type', 'location', 'status']].copy()
                preview_df.columns = ['Crime Type', 'Location', 'Status']
                preview_df = preview_df.apply(lambda x: x.str.title() if x.dtype == "object" else x)
                st.dataframe(preview_df, height=200)
            else:
                st.warning("No data available to display.")

            # Prediction Inputs
            st.subheader("Make a Prediction")
            model_choice = st.selectbox("Select Model", ["Random Forest", "XGBoost"], help="Choose the machine learning model for prediction")
            crime_types = sorted(set(load_crime_data()['crime_type']))
            locations = sorted(set(load_crime_data()['location']))
            if len(crime_types) == 0 or len(locations) == 0:
                st.error("No crime data available for prediction. Please add crimes via 'Add Crime'.")
            else:
                crime_type = st.selectbox("Crime Type", [t.title() for t in crime_types], help="e.g., Cyber Crimes, Theft")
                location = st.selectbox("Location", [t.title() for t in locations], help="e.g., New York, Chicago")
                if st.button("<i class='fas fa-brain fa-icon'></i> Predict", key="predict", help="Predict the crime status"):
                    crime_type_encoded = le_crime.transform([crime_type.lower()])[0]
                    location_encoded = le_location.transform([location.lower()])[0]
                    model = rf_model if model_choice == "Random Forest" else xgb_model
                    input_data = np.array([[crime_type_encoded, location_encoded]])
                    prediction = model.predict(input_data)[0]
                    status = le_status.inverse_transform([prediction])[0]
                    # Get prediction probabilities
                    probs = model.predict_proba(input_data)[0]
                    prob_dict = {le_status.inverse_transform([i])[0].title(): f"{prob*100:.1f}%" for i, prob in enumerate(probs)}
                    # Get feature importance
                    feature_names = ['Crime Type', 'Location']
                    if model_choice == "Random Forest":
                        importance = model.feature_importances_
                    else:  # XGBoost
                        importance = model.feature_importances_
                    importance_dict = {name: f"{imp*100:.1f}%" for name, imp in zip(feature_names, importance)}

                    # Display results in a styled container
                    st.markdown('<div class="prediction-output">', unsafe_allow_html=True)
                    st.write(f"**Predicted Status**: {status.title()}")
                    st.write("**Confidence Scores**:")
                    for status, prob in prob_dict.items():
                        st.write(f"- {status}: {prob}")
                    st.write("**Feature Importance**: (How much each input affects the prediction)")
                    for name, imp in importance_dict.items():
                        st.write(f"- {name}: {imp}")
                    st.markdown('</div>', unsafe_allow_html=True)
    except Exception as e:
        st.error(f"Error in ML Predictions: {e}. Ensure the database is initialized with sufficient data.")

elif choice in ["Add Crime", "Data Analysis"] and st.session_state.user and st.session_state.role != "admin":
    st.error("Access Denied: This feature is restricted to admin users.")
elif choice not in ["Login", "Sign Up"] and not st.session_state.user:
    st.error("Please log in to access this feature.")