Added meteo API functionality to the MongoDB agent and LLM summary
This commit is contained in:
Shiva Pochampally
377
llm/gemini_mongo_mateo.py
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377
llm/gemini_mongo_mateo.py
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#MONGO_URI=mongodb+srv://Admin:HelloKitty420@geobase.tyxsoir.mongodb.net/crashes
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import os
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import requests
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import time
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from datetime import datetime
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from pymongo import MongoClient
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from langchain_google_genai import ChatGoogleGenerativeAI
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from math import radians, sin, cos, sqrt, atan2
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# Configuration
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GEMINI_API_KEY = "AIzaSyBCbEOo4aK72507hqvpYkE9zXUe-z5aSXA"
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MONGO_URI = "mongodb+srv://Admin:HelloKitty420@geobase.tyxsoir.mongodb.net/crashes"
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llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash-lite", api_key=GEMINI_API_KEY)
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def connect_to_mongodb():
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"""
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Connect to MongoDB database and return the collection.
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"""
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try:
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print("Connecting to MongoDB...")
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client = MongoClient(MONGO_URI)
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# Test the connection
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client.admin.command('ping')
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print("✅ Successfully connected to MongoDB!")
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db = client.crashes # Database name
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collection = db.crashes # Collection name - corrected to 'crashes'
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# Get collection stats
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total_count = collection.estimated_document_count()
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print(f"📊 Found {total_count:,} total crash records in database")
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# Check specifically for 2020+ data
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filter_2020_plus = {"reportDate": {"$gte": datetime(2020, 1, 1)}}
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count_2020_plus = collection.count_documents(filter_2020_plus)
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print(f"📅 Found {count_2020_plus:,} crash records from 2020 onward")
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return collection
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except Exception as e:
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print(f"❌ Failed to connect to MongoDB: {e}")
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return None
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def get_crashes_within_radius_mongodb(collection, center_lat, center_lon, radius_km):
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"""
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Query MongoDB for crashes within specified radius using geospatial query.
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Filters for crashes from 2020 onward only.
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Args:
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collection: MongoDB collection object
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center_lat: Latitude of center point
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center_lon: Longitude of center point
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radius_km: Radius in kilometers
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Returns:
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List of crash documents within radius from 2020 onward
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"""
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try:
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print(f"🔍 Querying crashes within {radius_km}km of ({center_lat:.6f}, {center_lon:.6f}) from 2020 onward...")
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# MongoDB geospatial query using $geoWithin and $centerSphere
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# $centerSphere uses radians, so convert km to radians (divide by Earth's radius in km)
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radius_radians = radius_km / 6371 # Earth's radius in km
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# Combined query: geospatial AND date filter for 2020+
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query = {
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"location": {
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"$geoWithin": {
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"$centerSphere": [[center_lon, center_lat], radius_radians]
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}
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},
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"reportDate": {
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"$gte": datetime(2020, 1, 1) # Only crashes from 2020 onward
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}
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}
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# Execute the query
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cursor = collection.find(query)
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crashes = list(cursor)
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print(f"📍 Found {len(crashes)} crashes within {radius_km}km radius (from 2020 onward)")
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# Add distance calculation to each crash for sorting
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for crash in crashes:
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if crash.get('location', {}).get('coordinates'):
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crash_lon, crash_lat = crash['location']['coordinates']
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distance = haversine_distance(center_lat, center_lon, crash_lat, crash_lon)
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crash['distance_km'] = distance
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# Sort by distance
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crashes.sort(key=lambda x: x.get('distance_km', float('inf')))
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return crashes
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except Exception as e:
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print(f"❌ Error querying MongoDB: {e}")
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return []
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def haversine_distance(lat1, lon1, lat2, lon2):
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"""
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Calculate the great circle distance between two points
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on the earth (specified in decimal degrees)
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Returns distance in kilometers
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"""
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# Convert decimal degrees to radians
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lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
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# Haversine formula
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dlat = lat2 - lat1
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dlon = lon2 - lon1
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a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
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c = 2 * atan2(sqrt(a), sqrt(1-a))
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# Radius of earth in kilometers
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r = 6371
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return c * r
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def get_current_weather(lat, lon):
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"""
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Get current weather data from Open-Meteo API.
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"""
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try:
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url = "https://api.open-meteo.com/v1/forecast"
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response = requests.get(
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url,
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params={
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"latitude": lat,
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"longitude": lon,
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"current": "precipitation,wind_speed_10m,is_day,weather_code"
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},
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timeout=10
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)
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response.raise_for_status()
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data = response.json()
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current = data.get("current", {})
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# Map weather codes to descriptions (WMO Weather interpretation codes)
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weather_code_map = {
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0: "Clear sky",
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1: "Mainly clear",
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2: "Partly cloudy",
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3: "Overcast",
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45: "Fog",
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48: "Depositing rime fog",
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51: "Light drizzle",
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53: "Moderate drizzle",
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55: "Dense drizzle",
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56: "Light freezing drizzle",
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57: "Dense freezing drizzle",
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61: "Slight rain",
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63: "Moderate rain",
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65: "Heavy rain",
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66: "Light freezing rain",
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67: "Heavy freezing rain",
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71: "Slight snow fall",
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73: "Moderate snow fall",
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75: "Heavy snow fall",
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77: "Snow grains",
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80: "Slight rain showers",
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81: "Moderate rain showers",
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82: "Violent rain showers",
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85: "Slight snow showers",
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86: "Heavy snow showers",
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95: "Thunderstorm",
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96: "Thunderstorm with slight hail",
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99: "Thunderstorm with heavy hail"
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}
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weather_code = current.get("weather_code", 0)
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weather_desc = weather_code_map.get(weather_code, "Unknown weather")
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precipitation = current.get("precipitation", 0)
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wind_speed = current.get("wind_speed_10m", 0)
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is_day = current.get("is_day", 1)
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day_night = "day" if is_day else "night"
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summary_parts = []
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summary_parts.append(f"Conditions: {weather_desc}")
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summary_parts.append(f"Precipitation: {precipitation}mm/h")
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summary_parts.append(f"Wind: {wind_speed} km/h")
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summary_parts.append(f"Time: {day_night}")
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summary = " | ".join(summary_parts)
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return data, summary
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except Exception as e:
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return None, f"Weather API failed: {str(e)}"
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def analyze_mongodb_crash_patterns(crashes, center_lat, center_lon, radius_km, weather_summary=None):
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"""
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Analyze crash patterns from MongoDB data and generate safety assessment.
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"""
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if not crashes:
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return "No crash data available for the specified location and radius."
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total_crashes = len(crashes)
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avg_distance = sum(crash.get('distance_km', 0) for crash in crashes) / total_crashes if crashes else 0
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# Analyze crash patterns from MongoDB structure
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crash_analysis = {
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'severity_counts': {},
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'total_fatalities': 0,
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'total_major_injuries': 0,
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'total_minor_injuries': 0,
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'speeding_involved': 0,
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'impaired_involved': 0,
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'pedestrian_crashes': 0,
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'bicyclist_crashes': 0,
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'vehicle_counts': {}
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}
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# Analyze each crash
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for crash in crashes:
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# Severity analysis
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severity = crash.get('severity', 'Unknown')
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crash_analysis['severity_counts'][severity] = crash_analysis['severity_counts'].get(severity, 0) + 1
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# Casualty analysis
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casualties = crash.get('casualties', {})
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# Count fatalities and injuries across all categories
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for category in ['bicyclists', 'drivers', 'pedestrians', 'passengers']:
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if category in casualties:
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crash_analysis['total_fatalities'] += casualties[category].get('fatal', 0)
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crash_analysis['total_major_injuries'] += casualties[category].get('major_injuries', 0)
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crash_analysis['total_minor_injuries'] += casualties[category].get('minor_injuries', 0)
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# Count vulnerable road user involvement
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if casualties.get('pedestrians', {}).get('total', 0) > 0:
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crash_analysis['pedestrian_crashes'] += 1
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if casualties.get('bicyclists', {}).get('total', 0) > 0:
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crash_analysis['bicyclist_crashes'] += 1
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# Circumstances analysis
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circumstances = crash.get('circumstances', {})
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if circumstances.get('speeding_involved', False):
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crash_analysis['speeding_involved'] += 1
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# Check for impairment
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if (circumstances.get('pedestrians_impaired', False) or
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circumstances.get('bicyclists_impaired', False) or
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circumstances.get('drivers_impaired', False)):
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crash_analysis['impaired_involved'] += 1
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# Vehicle analysis
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vehicles = crash.get('vehicles', {})
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total_vehicles = vehicles.get('total', 0)
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crash_analysis['vehicle_counts'][str(total_vehicles)] = crash_analysis['vehicle_counts'].get(str(total_vehicles), 0) + 1
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# Create comprehensive summary for LLM
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crash_summary = f"""
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SEVERITY BREAKDOWN: {dict(crash_analysis['severity_counts'])}
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CASUALTIES:
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- Fatal injuries: {crash_analysis['total_fatalities']}
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- Major injuries: {crash_analysis['total_major_injuries']}
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- Minor injuries: {crash_analysis['total_minor_injuries']}
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VULNERABLE ROAD USERS:
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- Crashes involving pedestrians: {crash_analysis['pedestrian_crashes']}
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- Crashes involving bicyclists: {crash_analysis['bicyclist_crashes']}
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RISK FACTORS:
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- Crashes involving speeding: {crash_analysis['speeding_involved']}
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- Crashes with impairment: {crash_analysis['impaired_involved']}
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VEHICLE INVOLVEMENT: {dict(crash_analysis['vehicle_counts'])}"""
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# Add current weather information if available
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weather_info = ""
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if weather_summary:
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weather_info = f"""
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CURRENT WEATHER CONDITIONS:
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{weather_summary}"""
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# Create prompt for LLM
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prompt = f"""You are a traffic safety expert analyzing recent crash data (2020 onward) and current conditions for location ({center_lat:.6f}, {center_lon:.6f}) within a {radius_km}km radius.
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CRASH STATISTICS (2020-Present):
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- Total crashes in area: {total_crashes}
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- Average distance from center: {avg_distance:.2f} km
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- Search area: {radius_km}km radius (approximately {3.14159 * radius_km**2:.1f} km²)
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DETAILED CRASH ANALYSIS:{crash_summary}{weather_info}
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Based on this comprehensive recent MongoDB crash data (2020 onward), provide:
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1. A danger level assessment (Low, Moderate, High, Very High)
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2. Key safety concerns based on recent crash patterns AND current weather conditions
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3. Specific recommendations for someone traveling to this location RIGHT NOW
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4. Notable patterns in recent crash data (severity, vulnerable users, risk factors)
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5. How current weather conditions may affect driving safety
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Focus on practical, actionable safety advice based on recent trends. Be specific about identified risks and provide clear recommendations."""
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try:
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response = llm.invoke(prompt)
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return response.content
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except Exception as e:
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return f"Error analyzing crash data with LLM: {e}"
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def main():
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"""
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Main function to analyze crash danger using MongoDB geospatial queries.
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"""
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print("🚗 MongoDB Traffic Crash Danger Analysis Tool (2020+ Data)")
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print("=" * 65)
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# Connect to MongoDB
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collection = connect_to_mongodb()
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if collection is None:
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print("❌ Could not connect to MongoDB. Exiting...")
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return
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# Get user input for location and radius
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try:
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center_lat = float(input("Enter latitude: "))
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center_lon = float(input("Enter longitude: "))
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radius_km = float(input("Enter search radius in kilometers (default: 1.0): ") or "1.0")
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print(f"\n🔍 Analyzing recent crashes (2020+) within {radius_km}km of ({center_lat:.6f}, {center_lon:.6f})...")
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# Query MongoDB for nearby crashes using geospatial indexing
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nearby_crashes = get_crashes_within_radius_mongodb(collection, center_lat, center_lon, radius_km)
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if len(nearby_crashes) > 0:
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print(f"🔴 Closest crash: {nearby_crashes[0]['distance_km']:.3f}km away")
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print(f"🔴 Furthest crash: {nearby_crashes[-1]['distance_km']:.3f}km away")
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# Display sample crash details from MongoDB structure
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print("📊 Sample crash details from MongoDB:")
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sample = nearby_crashes[0]
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print(f" - ID: {sample.get('crashId', 'N/A')}")
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print(f" - Severity: {sample.get('severity', 'N/A')}")
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print(f" - Address: {sample.get('address', 'N/A')}")
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print(f" - Ward: {sample.get('ward', 'N/A')}")
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casualties = sample.get('casualties', {})
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total_casualties = 0
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for cat in ['bicyclists', 'drivers', 'pedestrians', 'passengers']:
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cat_data = casualties.get(cat, {})
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total_casualties += (cat_data.get('fatal', 0) +
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cat_data.get('major_injuries', 0) +
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cat_data.get('minor_injuries', 0))
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print(f" - Total casualties: {total_casualties}")
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else:
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print("ℹ️ No crashes found within the specified radius.")
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# Get current weather conditions
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print("\n🌤️ Fetching current weather conditions...")
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weather_data, weather_summary = get_current_weather(center_lat, center_lon)
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if weather_data is None:
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print(f"⚠️ Weather data unavailable: {weather_summary}")
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weather_summary = None
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else:
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print(f"🌤️ Current conditions: {weather_summary}")
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# Generate comprehensive safety analysis using LLM
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print("\n🤖 Generating comprehensive safety assessment...")
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analysis = analyze_mongodb_crash_patterns(nearby_crashes, center_lat, center_lon, radius_km, weather_summary)
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print("\n" + "="*65)
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print("🚨 RECENT CRASH SAFETY ASSESSMENT REPORT (2020-Present)")
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print("="*65)
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print(analysis)
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except ValueError:
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print("❌ Please enter valid numerical values for coordinates and radius.")
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except KeyboardInterrupt:
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print("\n⚠️ Analysis cancelled by user.")
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except Exception as e:
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print(f"❌ An error occurred: {e}")
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user