Skip to content

Shameer UI branch #1

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
Shameer-R wants to merge 4 commits into
base: master
Choose a base branch
from
Open

Shameer UI branch #1

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
c514a3f
Create UI to analyze evidence
Shameer-R Apr 26, 2025
5fa5697
Create requirements.txt file
Shameer-R Apr 26, 2025
613cb5b
Create requirements.txt file
Shameer-R Apr 26, 2025
cfebd39
Add instructions to view UI
Shameer-R Apr 26, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 0 additions & 2 deletions case_files/requirements.txt
View file
Open in desktop

This file was deleted.

6 changes: 6 additions & 0 deletions requirements.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,6 @@
streamlit==1.32.0
pandas==2.1.0
folium==0.14.0
streamlit-folium==0.16.0
geopy==2.4.0
numpy==1.24.3
264 changes: 131 additions & 133 deletions src/shameer_main.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,14 +1,13 @@
from datetime import datetime

import streamlit as st
import pandas as pd
import json
import numpy as np
from datetime import datetime
from geopy.distance import geodesic
import folium
from streamlit_folium import folium_static


# Load data files
def load_data():
# Load Data
try:
with open('case_files/incident_reports.json', 'r') as f:
incidents = json.load(f)
Expand All @@ -19,66 +18,12 @@ def load_data():
with open('case_files/suspects.json', 'r') as f:
suspects = json.load(f)

# Load CSV Files
bike_logs = pd.read_csv('case_files/bike_logs.csv')
cam_snapshots = pd.read_csv('case_files/cam_snapshots_metadata.csv')

print("Data successfully loaded")

return incidents, phone_pings, suspects, bike_logs, cam_snapshots

return incidents, phone_pings, suspects, bike_logs
except Exception as e:
print(f"Error loading data: {e}")
return None


def explore_data(incidents, phone_pings, suspects, bike_logs, cam_snapshots):
# Explore Incidents

print("\nINCIDENT REPORTS:")
print(f"Number of incidents: {len(incidents)}")
for i, incident in enumerate(incidents):
print(f"\nIncident {i + 1}")
print(f"Date: {incident['date']}")
print(f"Address: {incident['address']}")
print(f"Entry Time: {incident['entry_time']}")
print(f"Exit Time: {incident['exit_time']}")
print(f"Notes: {incident['notes']}")

# Explore Phone Pings

print("\nPHONE PINGS:")
print(f"Number of phone pings: {len(phone_pings)}")
device_ids = set(ping['device_id'] for ping in phone_pings)
print(f"Unique device ids: {device_ids}")

# Explore Suspects
print("\nSUSPECTS:")
print(f"Number of suspects: {len(suspects)}")
for i, suspect in enumerate(suspects):
print(f"\nSuspect {i + 1}")
for key, value in suspect.items():
print(f"\t{key}: {value}")

print("\nBIKE LOGS:")
print(f"Total Bike Logs: {len(bike_logs)}")
print(f"Columns: {list(bike_logs.columns)}")

print("\nCAMERA SNAPSHOTS:")
print(f"Total Camera Snapshots: {len(cam_snapshots)}")
print(f"Columns: {list(cam_snapshots.columns)}")

# Connect Device IDS to Suspect
device_to_suspect = {}
for suspect in suspects:
if 'phone_id' in suspect and suspect['phone_id']:
device_to_suspect[suspect['phone_id']] = suspect['name']

print("\nDEVICE TO SUSPECT MAPPING:")
for device, name in device_to_suspect.items():
print(f"\t{device}: {name}")

return device_to_suspect
st.error(f"Error loading data: {e}")
return None, None, None, None


def parse_timestamps(timestamp_str):
Expand All @@ -88,17 +33,14 @@ def parse_timestamps(timestamp_str):
else:
return datetime.strptime(timestamp_str, '%Y-%m-%d')
except Exception as e:
print(f"Error parsing timestamp {timestamp_str}: {e}")
return None


def analyze_proximity(incidents, phone_pings, device_to_suspect):
# Start checking which devices were near which incidents
device_at_incidents = {device_id: set() for device_id in device_to_suspect.keys()}
evidence_log = {device_id: [] for device_id in device_to_suspect.keys()}

# Evidence constraints
proximity_threshold = 1 # miles
proximity_threshold = 1 # miles
time_window = 60 # minutes

for incident in incidents:
Expand All @@ -124,12 +66,12 @@ def analyze_proximity(incidents, phone_pings, device_to_suspect):

for ping in phone_pings:
device_id = ping['device_id']
if device_id not in device_to_suspect: # Device not linked to suspect
if device_id not in device_to_suspect:
continue

ping_time = parse_timestamps(ping['timestamp'])
if not ping_time:
continue # Skip if timestamp couldn't be parsed
continue

if time_before <= ping_time <= time_after:
ping_location = (ping['lat'], ping['lon'])
Expand Down Expand Up @@ -183,9 +125,8 @@ def analyze_bike_rentals(incidents, bike_logs, suspects):
rental_end = rental['end_time']

for incident in incident_times:
# Convert both to the same type for comparison
if (rental_start <= incident['exit_time'] and
rental_end >= incident['entry_time']):
rental_end >= incident['entry_time']):
suspect_rentals[suspect_name].add(incident['address'])

return suspect_rentals
Expand All @@ -196,96 +137,153 @@ def identify_primary_suspects(device_at_incidents, device_to_suspect, suspect_re

combined_evidence = {}

# Collect evidence from phone pings
for device_id, addresses in device_at_incidents.items():
suspect_name = device_to_suspect[device_id]
if suspect_name not in combined_evidence:
combined_evidence[suspect_name] = set()
combined_evidence[suspect_name].update(addresses)

# Add bike rental evidence
for suspect_name, addresses in suspect_rentals.items():
if suspect_name not in combined_evidence:
combined_evidence[suspect_name] = set()
combined_evidence[suspect_name].update(addresses)

# Calculate a score for each suspect
suspect_scores = []
suspects_scored = []
for suspect_name, addresses in combined_evidence.items():
# Calculate number of matching addresses
match_count = len(addresses)
suspect_scores.append((suspect_name, match_count))

# Sort by score (match count) in descending order
suspect_scores.sort(key=lambda x: x[1], reverse=True)
coverage = (match_count / len(all_addresses)) * 100
suspects_scored.append({
'name': suspect_name,
'match_count': match_count,
'coverage': coverage,
'addresses': list(addresses)
})

# Get top 3 suspects (or fewer if there aren't 3)
top_suspects = suspect_scores[:min(3, len(suspect_scores))]
suspects_scored.sort(key=lambda x: x['match_count'], reverse=True)

# Format the results
results = []
for suspect_name, match_count in top_suspects:
phone_evidence = []
for device_id, addresses in device_at_incidents.items():
if device_to_suspect[device_id] == suspect_name:
phone_evidence = list(addresses)
return suspects_scored[:3]

bike_evidence = list(suspect_rentals.get(suspect_name, []))

justification = f"{suspect_name} was present at {match_count} out of {len(all_addresses)} crime scenes)"
def create_map(incidents, phone_pings, top_suspect_devices):
incident_coords = []
for incident in incidents:
if "108 Linden St" in incident['address']:
incident_coords.append((40.695, -73.92, incident['address'], incident['date']))
elif "104 Linden St" in incident['address']:
incident_coords.append((40.696, -73.925, incident['address'], incident['date']))
elif "102 Linden St" in incident['address']:
incident_coords.append((40.697, -73.93, incident['address'], incident['date']))

center_lat = sum(coord[0] for coord in incident_coords) / len(incident_coords)
center_lon = sum(coord[1] for coord in incident_coords) / len(incident_coords)

m = folium.Map(location=[center_lat, center_lon], zoom_start=16)

for lat, lon, address, date in incident_coords:
folium.Marker(
location=[lat, lon],
popup=f"{address}<br>Date: {date}",
icon=folium.Icon(color='red', icon='home'),
).add_to(m)

for ping in phone_pings:
if ping['device_id'] in top_suspect_devices:
color = 'green'
radius = 30
fill_opacity = 0.7
else:
color = 'blue'
radius = 10
fill_opacity = 0.3

results.append({
'name': suspect_name,
'match_count': match_count,
'justification': justification,
'phone_evidence': phone_evidence,
'bike_evidence': bike_evidence
})
folium.CircleMarker(
location=[ping['lat'], ping['lon']],
radius=radius,
popup=f"Device: {ping['device_id']}<br>Time: {ping['timestamp']}",
color=color,
fill=True,
fill_opacity=fill_opacity
).add_to(m)

return results
return m


def main():
# Load Data
incidents, phone_pings, suspects, bike_logs, cam_snapshots = load_data()
st.title("Linden Street Burglaries Investigation")

if incidents is None or phone_pings is None or suspects is None:
print("Failed to load critical data files. Exiting.")
return
if st.button("Analyze Evidence"):
incidents, phone_pings, suspects, bike_logs = load_data()

device_to_suspect = explore_data(incidents, phone_pings, suspects, bike_logs, cam_snapshots)
device_at_incidents, evidence_log = analyze_proximity(incidents, phone_pings, device_to_suspect)
if incidents is None:
st.error("Failed to load data files. Check file paths.")
st.stop()

# Debug prints
print("\nDEVICES AT INCIDENTS:")
for device_id, addresses in device_at_incidents.items():
print(f"{device_id}: {addresses}")
device_to_suspect = {}
for suspect in suspects:
if 'phone_id' in suspect and suspect['phone_id']:
device_to_suspect[suspect['phone_id']] = suspect['name']

device_at_incidents, evidence_log = analyze_proximity(incidents, phone_pings, device_to_suspect)
suspect_rentals = analyze_bike_rentals(incidents, bike_logs, suspects)
top_suspects = identify_primary_suspects(device_at_incidents, device_to_suspect, suspect_rentals, incidents)

# Debug prints
print("\nSUSPECT RENTALS:")
for suspect, addresses in suspect_rentals.items():
print(f"{suspect}: {addresses}")
st.header("Top Suspects")

top_suspects = identify_primary_suspects(device_at_incidents, device_to_suspect, suspect_rentals, incidents)
col1, col2, col3 = st.columns(3)
columns = [col1, col2, col3]

# Print the top suspects
print("\n=== TOP 3 SUSPECTS ===")
for i, suspect in enumerate(top_suspects):
print(f"\n#{i + 1}: {suspect['name']}")
print(f"Evidence: {suspect['match_count']} crime scenes")
print(f"Justification: {suspect['justification']}")

if suspect['phone_evidence']:
print("Phone evidence at addresses:")
for address in suspect['phone_evidence']:
print(f" - {address}")

if suspect['bike_evidence']:
print("Bike rental evidence at addresses:")
for address in suspect['bike_evidence']:
print(f" - {address}")

if __name__ == "__main__":
main()
with columns[i]:
st.subheader(f"#{i + 1}: {suspect['name']}")
for s in suspects:
if s['name'] == suspect['name']:
st.write(f"**Occupation:** {s.get('occupation', 'Unknown')}")
st.write(f"**Alibi:** {s.get('alibi', 'None provided')}")
st.write(f"**Connections:** {suspect['match_count']} locations ({suspect['coverage']:.0f}%)")

st.header("Crime Scene Map")

top_suspect_devices = []
for suspect in top_suspects:
for s in suspects:
if s['name'] == suspect['name'] and 'phone_id' in s:
top_suspect_devices.append(s['phone_id'])

map_figure = create_map(incidents, phone_pings, top_suspect_devices)
folium_static(map_figure)

st.header("Incident Reports")
incident_df = pd.DataFrame([{
'Date': incident['date'],
'Address': incident['address'],
'Entry Time': incident['entry_time'],
'Exit Time': incident['exit_time'],
'Notes': incident['notes']
} for incident in incidents])

st.dataframe(incident_df)

st.header("Evidence Summary")

tabs = st.tabs(["Phone Evidence", "Bike Rentals"])

with tabs[0]:
for device_id, addresses in device_at_incidents.items():
if addresses:
st.write(f"**{device_to_suspect[device_id]}'s phone** detected at:")
for address in addresses:
st.write(f"- {address}")

with tabs[1]:
for suspect_name, addresses in suspect_rentals.items():
if addresses:
st.write(f"**{suspect_name}** rented bikes during crimes at:")
for address in addresses:
st.write(f"- {address}")

primary_suspect = top_suspects[0]['name'] if top_suspects else "No definitive suspect"
st.success(f"Primary suspect: **{primary_suspect}**")

else:
st.info("Click 'Analyze Evidence' to view results")

# Run 'streamlit run src/shameer_main.py' in terminal