Delete LLM_automation_GPT35.py

LLM_automation_GPT35.py

@@ -1,87 +0,0 @@
-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")
-
-    ### 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
-
-    dj=[]
-    for i in range(len(df2)):
-        dj.append(chain.invoke({"question" : df2['Description'][i]+" Is the news about road accident? If no, then reply 'General'. Else if the news is about road accident then check if the news is referring to a specific accident incident or accident in general? Answer only in a word: Either specific or general."}))
-
-    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]}))
-        time.sleep(30)
-        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]}))
-        time.sleep(30)
-        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]}))
-        time.sleep(30)
-        
-    #### 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