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def create_data(description):
from langchain_core.prompts import ChatPromptTemplate ### To create a chatbot, chatprompttemplate used
from langchain_openai import ChatOpenAI ##### For using chat openai features
from langchain_core.output_parsers import StrOutputParser ### Default output parser. Custom parser can also be created
import os
from dotenv import load_dotenv
load_dotenv()
### Set all api keys:
os.environ["OPENAI_API_KEY"]=os.getenv('OPENAI_API')
### Create Prompt Template:
prompt=ChatPromptTemplate.from_messages(
{
("system", "You are a helpful assistant, please respond to the queries"), ### We need both system and users in prompt
("user","question: {question}")
}
)
df2=description
#### Create OpenAI llm:
llm=ChatOpenAI(model="gpt-3.5-turbo-16k")
### Create an output parser:
output_parser=StrOutputParser()
#### Creating chain: The concept is- output of action before | symbol will be passed as input in action after the symbol.
#### Here we have created three actions: The prompt, llm and output parser:
chain=prompt|llm|output_parser
### A function to invoke the llm. For some reason phi3 doesn't give accurate result sometimes if used directly in dj.append()
def res(i):
response=chain.invoke({"question" : df2['Description'][i]+" Is the news referring to a specific accident incident or accident in general? Answer only in a word: 'specific' or 'general'. No other words are allowed in your answer"})
return response
#### dj list contains type of report 'General' or 'Specific'
dj=[]
for i in range(len(df2)):
dj.append(res(i))
df2['Report Type']=dj
def drp(p):
df2.drop([p],inplace=True)
### Removing the general accident types:
for p in range(len(df2)):
if "General" in df2['Report Type'][p] or "general" in df2['Report Type'][p]:
drp(p)
### Reseting index of df3:
df2.reset_index(drop=True,inplace=True)
### Splitting dj2 string based on comma position:
Date=[]
Time=[]
Killed=[]
Injured=[]
Location=[]
Road_Characteristic=[]
Pedestrian_Involved=[]
vehicles=[]
#Weather=[]
for i in range(len(df2)):
Date.append(chain.invoke({"question" : "Read the accident report carefully and provide only the answer of the question asked. Do not add any extra sentences or words except the answer: What is the date of accident occurrence in Day-Month-Year format. Keep in mind that news publish date and accident occurrence date may be different. If you cannot find or deduce the answer, simply reply Not Available" + df2['Description'][i]}))
Time.append(chain.invoke({"question" : "Read the accident report carefully and provide only the answer of the question asked. Do not add any extra sentences or words except the answer: What is the time of accident occurrence in 24-hour format. If you cannot find or deduce the answer, simply reply Not Available" + df2['Description'][i]}))
Killed.append(chain.invoke({"question" : "Read the accident report carefully and provide only the answer of the question asked. Do not add any extra sentences or words except the answer: How many people were killed in the accident?. If you cannot find or deduce the answer, simply reply Not Available" + df2['Description'][i]}))
Injured.append(chain.invoke({"question" : "Read the accident report carefully and provide only the answer of the question asked. Do not add any extra sentences or words except the answer: How many people were injured in the accident?. If you cannot find or deduce the answer, simply reply Not Available" + df2['Description'][i]}))
Location.append(chain.invoke({"question" : "Read the accident report carefully and provide only the answer of the question asked. Do not add any extra sentences or words except the answer: What is the name of the location where accident took place?. If you cannot find or deduce the answer, simply reply Not Available" + df2['Description'][i]}))
Road_Characteristic.append(chain.invoke({"question" : "Read the accident report carefully and provide only the answer of the question asked. Do not add any extra sentences or words except the answer: What is the type of road where accident took place?. If you cannot find or deduce the answer, simply reply Not Available" + df2['Description'][i]}))
Pedestrian_Involved.append(chain.invoke({"question" : "Read the accident report carefully and provide only the answer of the question asked. Do not add any extra sentences or words except the answer: Was there any pedestrian involved in the accident?. If you cannot find or deduce the answer, simply reply Not Available" + df2['Description'][i]}))
vehicles.append(chain.invoke({"question" : "Only name the type of vehicles involved in the accident. If multiple vehicles are involved, seperate them by hyphens(-). Example answers: Bus, Truck-Bus etc. If no vehicles are mentioned, your answer will be: Not Available. Your answer should only contain the vehicle name, do not include any extra sentences" + df2['Description'][i]}))
#### Probable type of final dataframe:
df2["Date"]=Date
df2["Time"]=Time
df2["Killed"]=Killed
df2["Injured"]=Injured
df2["Location"]=Location
df2["Road_Characteristic"]=Road_Characteristic
df2["Pedestrian_Involved"]=Pedestrian_Involved
df2["Vehicles Involved"]=vehicles
df3=df2.drop(columns=['Description','Report Type','Date + Desc'])
return df3 |