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import re
import openai
import pandas as pd
from geopy.geocoders import Nominatim
import sqlite3
import ast
from climateqa.engine.llm import get_llm
def detect_location_with_openai(sentence):
"""
Detects locations in a sentence using OpenAI's API via LangChain.
"""
llm = get_llm()
prompt = f"""
Extract all locations (cities, countries, states, or geographical areas) mentioned in the following sentence.
Return the result as a Python list. If no locations are mentioned, return an empty list.
Sentence: "{sentence}"
"""
response = llm.invoke(prompt)
location_list = ast.literal_eval(response.content.strip("```python\n").strip())
if location_list:
return location_list[0]
else:
return ""
def detectTable(sql_query):
pattern = r'(?i)\bFROM\s+((?:`[^`]+`|"[^"]+"|\'[^\']+\'|\w+)(?:\.(?:`[^`]+`|"[^"]+"|\'[^\']+\'|\w+))*)'
matches = re.findall(pattern, sql_query)
return matches
def loc2coords(location : str):
geolocator = Nominatim(user_agent="city_to_latlong")
location = geolocator.geocode(location)
return (location.latitude, location.longitude)
def coords2loc(coords : tuple):
geolocator = Nominatim(user_agent="coords_to_city")
try:
location = geolocator.reverse(coords)
return location.address
except Exception as e:
print(f"Error: {e}")
return "Unknown Location"
def nearestNeighbourSQL(db: str, location: tuple, table : str):
conn = sqlite3.connect(db)
long = round(location[1], 3)
lat = round(location[0], 3)
cursor = conn.cursor()
cursor.execute(f"SELECT lat, lon FROM {table} WHERE lat BETWEEN {lat - 0.3} AND {lat + 0.3} AND lon BETWEEN {long - 0.3} AND {long + 0.3}")
results = cursor.fetchall()
return results[0]
def detect_relevant_tables(user_question, llm):
table_names_list = [
"Frequency_of_rainy_days_index",
"Winter_precipitation_total",
"Summer_precipitation_total",
"Annual_precipitation_total",
# "Remarkable_daily_precipitation_total_(Q99)",
"Frequency_of_remarkable_daily_precipitation",
"Extreme_precipitation_intensity",
"Mean_winter_temperature",
"Mean_summer_temperature",
"Number_of_tropical_nights",
"Maximum_summer_temperature",
"Number_of_days_with_Tx_above_30C",
"Number_of_days_with_Tx_above_35C",
"Drought_index"
]
prompt = (
f"You are helping to build a sql query to retrieve relevant data for a user question."
f"The different tables are {table_names_list}."
f"The user question is {user_question}. Write the relevant tables to use. Answer only a python list of table name."
)
table_names = ast.literal_eval(llm.invoke(prompt).content.strip("```python\n").strip())
return table_names
def replace_coordonates(coords, query, coords_tables):
n = query.count(str(coords[0]))
for i in range(n):
query = query.replace(str(coords[0]), str(coords_tables[i][0]),1)
query = query.replace(str(coords[1]), str(coords_tables[i][1]),1)
return query |