can you tell me how to calculate  what will be cost for this input tokens in prompt and output tokens in prompt 
Labels: name: auto:question, name: area: models

### Issue with current documentation:

this is intput token (import pandas as pd
import json
from IPython.display import Markdown, display
from langchain.agents import create_csv_agent
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
import os

os.environ["OPENAI_API_KEY"] = ""


# Load the dataset
df = pd.read_csv('Loan Collections - Sheet1.csv')

# Function to convert date and time columns to float
def convert_date_time_columns_to_float(df):
    for column in df.select_dtypes(include=['object']).columns:
        # Check if the column name contains 'date' or 'time'
        if 'date' in column.lower() or 'time' in column.lower():
            try:
                # Convert the column to datetime
                df[column] = pd.to_datetime(df[column], errors='coerce')
                
                # Convert datetime to numerical representation (e.g., days since a reference date)
                reference_date = pd.to_datetime('1900-01-01')
                df[column] = (df[column] - reference_date).dt.total_seconds() / (24 * 60 * 60)

            except ValueError:
                # Handle errors during conversion
                print(f"Error converting column '{column}' to float.")

# Convert 'date' and 'time' columns to float
convert_date_time_columns_to_float(df)


# Extract unique values for each column
unique_values_per_column = {}
for column in df.select_dtypes(include=['object']).columns:
    unique_values_per_column[column] = df[column].unique().tolist()

# Convert the dictionary to JSON
json_data_train = json.dumps(unique_values_per_column, indent=4)


testData_fname = "Sample Retail Stores Data.csv"
# Load the dataset
df2 = pd.read_csv(testData_fname)


convert_date_time_columns_to_float(df2)



# Extract unique values for each column
unique_values_per_column = {}
for column in df2.select_dtypes(include=['object']).columns:
    unique_values_per_column[column] = df2[column].unique().tolist()

# Convert the dictionary to JSON
json_data_test = json.dumps(unique_values_per_column, indent=4)

# Define user's question
user_question = "Percentage share of State by Value?"

# Define the prompt template
prompt_template = f'''If the dataset has the following columns: {json_data_train}'''+''' Understand user questions with different column names and convert them to a JSON format.

Question might not even mentioned column name at all, it would probably mention value of the column. so it has to figure it out columnn name based on that value.

Example1:
User Question1:  top zone in the year 2019 with Loan Amt between 10k and 20k and tenure > 12 excluding Texas region?


{
    "start_date": "01-01-2019",
    "end_date": "31-12-2019",
    "time_stamp_col": "Due Date",
    "agg_columns": [],
    "trend": "null",
    "to_start_date": "null",
    "to_end_date": "null",
    "growth": "null",
    "variables_grpby": ["Zone"],
    "filters": {},
    "not_in": {"Region": ["Texas"]},
    "num_filter": {
        "gt": [
            ["Loan Tenure", 12],
            ["Loan Amount", 10000]
        ],
        "lt": [
            ["Loan Amount", 20000]
        ]
    },
    "percent": "false",
    "top": "1",
    "bottom": "null"
}

Note the following in the above example
- The word "top" in the User Question made the "top" key have the value as "1". If "highest" is mentioned in the User Question, even then "top" would have the value as "1". If "top" is not mentioned or not implied in the User Question, then it takes on the value "null". Similarly for "bottom" key in the System Response.
- The word "zone" in the User Question refers to a column "Zone" in the dataset and since it is a non-numeric column and we have to group by that column, the system response has it as one of the values of the list of the key "variables_grpby"
- The key "start_date" and "end_date" Since it is mentioned 2019 in the User Question as the timeframe, the "start_date" assumes the beginning of the year 2019 and "end_date" assumes the end of the year 2019. If no date related words are mentioned in the question, "start_date" would be "null" and "end_date" would be "null".
- The key "time_stamp_col" in the System Response should mention the relevant time related column name from the dataset according to the question if the question mentions a time related word.
- The key "agg_columns" in the System Response is a list of columns to be aggregated which should mention the numeric column names on which the question wants us to aggregate on.
- The key "trend" in the System Response, "trend" is set to "null" since the user question doesn't imply any trend analysis . If the question were about trends over time, this key would contain information about the trend, such as "upward," "downward," or "null" if no trend is specified.
- The key "filters" An empty dictionary in this case, as there are no explicit filters mentioned in the user question. If the user asked to filter data based on certain conditions (e.g. excluding a specific region), this key would contain the relevant filters.
- The key "to_start_date" and "to_end_date" Both set to "null" in this example because the user question specifies a single timeframe (2019). If the question mentioned a range (e.g. "from January 2019 to March 2019"), these keys would capture the specified range.
- The key "growth" Set to "null" in this example as there is no mention of growth in the user question. If the user inquired about growth or change over time, this key would provide information about the type of growth (e.g."monthly","yearly"," "absolute") or be set to "null" if not applicable.
- The key "not_in" Contains information about exclusion criteria based on the user's question. In this example, it excludes the "Texas" region. If the user question doesn't involve exclusions, this key would be an empty dictionary.
- The key "num_filter" Specifies numerical filters based on conditions in the user question. In this example, it filters loans with a tenure greater than 12 and loan amounts between 10k and 20k. If the user question doesn't involve numerical filters, this key would be an empty dictionary.
- The key "percent" Set to "false" in this example as there is no mention of percentage in the user question. If the user inquired about percentages, this key would contain information about the use of percentages in the response.

Similarly, below are more examples of user questions and their corresponding expected System Responses.

Example 2:
User Question: What is the Highest Loan Amount and Loan Outstanding by RM Name James in January 2020

{
    "start_date": "01-01-2020",
    "end_date": "31-01-2020",
    "time_stamp_col": "Due Date",
    "agg_columns": ["Loan Amount", "Loan Outstanding"],
    "trend":  "null",
    "to_start_date":  "null",
    "to_end_date":  "null",
    "growth":  "null",
    "variables_grpby": [],
    "filters": {"RM Name": ["James"]},
    "not_in": {},
    "num_filter": {},
    "percent": "false",
    "top": "1",
    "bottom": "null"
}

Example 3:
User Question: Which RM Name with respect to Region has the Highest Interest Outstanding and Principal Outstanding in the year 2019

{
    "start_date": "01-01-2019",
    "end_date": "31-12-2019",
    "time_stamp_col": "Due Date",
    "agg_columns": ["Interest Outstanding", "Principal Outstanding"],
    "trend":  "null",
    "to_start_date":  "null",
    "to_end_date":  "null",
    "growth":  "null",
    "variables_grpby": ["RM Name", "Region"],
    "filters": {},
    "not_in": {},
    "num_filter": {},
    "percent": "false",
    "top": "1",
    "bottom":  "null"
}

Example 4:
User Question: Which Branch in North Carolina with respect to Cibil Score Bucket has the Highest Cibil Score in 2019

{
    "start_date": "01-01-2019",
    "end_date": "31-12-2019",
    "time_stamp_col": "Due Date",
    "agg_columns": ["Cibil Score", "DPD Bucket"],
    "trend":  "null",
    "to_start_date":  "null",
    "to_end_date":  "null",
    "growth":  "null",
    "variables_grpby": ["Branch"],
    "filters": {"Region": ["North Carolina"]},
    "not_in": {},
    "num_filter": {},
    "percent": "false",
    "top": "1",
    "bottom":  "null"
}
''
Example 5:
User Question: With respect to Zone, Region, Branch, RM Name what is the Highest Loan Amount, Loan Tenure, Loan Outstanding, EMI Pending, Principal Outstanding

{
    "start_date":  "null",
    "end_date":  "null",
    "time_stamp_col":  "null",
    "agg_columns": ["Loan Amount", "Loan Tenure", "Loan Outstanding", "EMI Pending", "Principal Outstanding"],
    "trend":  "null",
    "to_start_date":  "null",
    "to_end_date":  "null",
    "growth":  "null",
    "variables_grpby": ["Zone", "Region", "Branch", "RM Name"],
    "filters": {},
    "not_in": {},
    "num_filter": {},
    "percent": "false",
    "top": "1",
    "bottom":  "null"
}

Example 6:
User Question: Top 2 zones by Housing Loan in the year 2019

{
    "start_date": "01-01-2019",
    "end_date": "31-12-2019",
    "time_stamp_col": "Due Date",
    "agg_columns": ["Housing Loan"],
    "trend":  "null",
    "to_start_date":  "null",
    "to_end_date":  "null",
    "growth":  "null",
    "variables_grpby": ["Zone"],
    "filters": {"Product": ["Home Loan"]},
    "not_in": {},
    "num_filter": {},
    "percent": "false",
    "top": "2",
    "bottom":  "null"
}

'''+ f'''Our test dataset has the following columns: {json_data_test}

User Question (to be converted): {user_question}'''



# Load the agent
agent = create_csv_agent(OpenAI(temperature=0), testData_fname, verbose=True)
gpt4_agent = create_csv_agent(ChatOpenAI(temperature=0, model_name="gpt-4-1106-preview"), testData_fname)

# Use the formatted question as the input to your agent
response = gpt4_agent.run(prompt_template)

# Print the response
print(user_question)
print(response))   and this is output token ( Percentage share of State by Value?
{
    "start_date": "null",
    "end_date": "null",
    "time_stamp_col": "null",
    "agg_columns": ["Value"],
    "trend": "null",
    "to_start_date": "null",
    "to_end_date": "null",
    "growth": "null",
    "variables_grpby": ["State"],
    "filters": {},
    "not_in": {},
    "num_filter": {},
    "percent": "true",
    "top": "null",
    "bottom": "null"
}) 


### Idea or request for content:

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