Upload model.py

model.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from geopy.distance import geodesic
+from sklearn.cluster import KMeans
+
+# Load the dataset
+@st.cache_data
+def load_data():
+    file_path = r"C:\Users\hp\Desktop\project\predictive crime\preprocessed_df1.csv"  # Update with your file path
+    return pd.read_csv(file_path, low_memory=False)
+
+# Function to calculate distance between two coordinates
+def calculate_distance(coord1, coord2):
+    return geodesic(coord1, coord2).km
+
+# Function to generate deployment plan
+def generate_deployment_plan(df, centroids):
+    deployment_plan = {}
+    for cluster in centroids.keys():
+        cluster_data = df[df['Cluster'] == cluster]
+        number_of_incidents = len(cluster_data)
+        suggestions = ""
+        if number_of_incidents == 0:
+            suggestions = "Increase police presence and conduct thorough investigations to prevent future incidents."
+        elif number_of_incidents <= 10:
+            suggestions = "Continue monitoring the area and conduct periodic patrols to maintain security."
+        else:
+            suggestions = "<span style='color:red;font-weight:bold'>Intensify surveillance efforts</span> and consider implementing community policing strategies."
+
+        deployment_plan[cluster] = {
+            'cluster_centroid': centroids[cluster],
+            'number_of_incidents': number_of_incidents,
+            'suggestions': suggestions
+        }
+    return deployment_plan
+
+# Function to predict the number of incidents based on input latitude and longitude
+def predict_incidents(input_latitude, input_longitude, df, centroids):
+    offense_location = (input_latitude, input_longitude)
+    closest_centroid = min(centroids.keys(), key=lambda x: calculate_distance(offense_location, (centroids[x]['Latitude'], centroids[x]['Longitude'])))
+    return closest_centroid
+
+# Streamlit app
+def main():
+    st.title("Crime Deployment Plan")
+
+    # Load the data
+    df = load_data()
+
+    # Preprocessing
+    df = df[(df['Latitude'] != 0.0) & (df['Longitude'] != 0.0)]
+
+    # Clustering
+    features = ['Latitude', 'Longitude']
+    X = df[features]
+    X = (X - X.mean()) / X.std()
+    kmeans = KMeans(n_clusters=5, random_state=42)
+    df['Cluster'] = kmeans.fit_predict(X)
+
+    # Define the latitude and longitude of the cluster centroids
+    centroids = {
+        1: {'Latitude': -0.26350117329990513, 'Longitude': 0.20905869782346226},
+        2: {'Latitude': 5.807340212426832, 'Longitude': -5.187200322116295},
+        3: {'Latitude': -1.2370956602478118, 'Longitude': -5.921660553559399},
+        4: {'Latitude': -0.2676250405697857, 'Longitude': 59.52073073890124},
+        5: {'Latitude': 0.023547431083855475, 'Longitude': 0.09870306054587759}
+    }
+
+    # User input for latitude and longitude
+    st.header("Input Latitude and Longitude")
+    input_latitude = st.number_input("Latitude", value=0.0)
+    input_longitude = st.number_input("Longitude", value=0.0)
+
+    # Button to trigger the calculation
+    if st.button("Predict and Generate Suggestions"):
+        # Predict the cluster and get suggestions
+        cluster_prediction = predict_incidents(input_latitude, input_longitude, df, centroids)
+        deployment_plan = generate_deployment_plan(df, centroids)
+        suggestions = deployment_plan[cluster_prediction]['suggestions']
+
+        # Calculate the number of incidents based on input value
+        offense_location = (input_latitude, input_longitude)
+        closest_centroid_data = df[df['Cluster'] == cluster_prediction]
+        number_of_incidents = len(closest_centroid_data)
+        
+        # Display suggestions and number of incidents
+        col1, col2 = st.columns(2)
+        with col1:
+            st.subheader("Suggestions")
+            st.markdown(suggestions, unsafe_allow_html=True)
+        with col2:
+            st.subheader("Number of Incidents")
+            st.write(number_of_incidents)
+
+if __name__ == "__main__":
+    main()