CHAT GPT response seems to be limited to some # of lines

### Describe the bug

I am new user working with Chat GPT for ab out 1 1/2 months  I am generally using this to help me write python programs. It has been quite useful but makes lots of mistakes. eventually it can find the right answer if you give it a few chances and allow it time to reply. can be frustrating and scary to trust the results. 
I am however finding with enough effort I can get good help from this AI tool.

I notice a few things which must be bugs.
1) the reply in python window seems limited by the number of lines it reply's with this makes it very difficult to keep a coherent  train of thought. Second since the reply's from Chat GPT is limited even for people who pay for service asking the same question over and over and getting a partial reply wastes tome for us both.

2)  Yesterday I worked for several hours and the reply's coming back were not making any sense and suddenly it thought I wanted to write a program about tic tact toe and play a game, which I did not.

 3) I have noticed that in the last week or so that the chat engine seems to loos track of our conversation and expects me to re upload data we have been working on for several hours.




### Steps/Code to Reproduce

None I could provide you with chat topic 

### Expected Results

Print the python code we are working on with out stopping in the middle and then when asking it to finishe or not to stop it starts from beginning and generally stops at a similar place far from end

### Actual Results

here is an incomplete reply.

import pandas as pd
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt

# Class to compute statistics of the data
class DataStatistics:
    def __init__(self, data, column_name, units):
        self.data = data
        self.column_name = column_name
        self.units = units

    # Function to calculate statistics of the data
    def get_stats(self):
        mode = pd.Series(self.data).mode().values
        mean = np.mean(self.data)
        std_deviation = np.std(self.data, ddof=1)
        average_deviation = np.mean(np.abs(self.data - mean))
        missing_values = np.count_nonzero(np.isnan(self.data))
        minimum = np.min(self.data)
        maximum = np.max(self.data)
        skewness = pd.Series(self.data).skew()
        kurtosis = pd.Series(self.data).kurt()
        variance = np.var(self.data, ddof=1)
        possible_values = np.unique(self.data)
        median = np.median(self.data)
        cumulative_prob = norm.cdf(possible_values, loc=mean, scale=std_deviation)

        return mode, mean, std_deviation, average_deviation, missing_values, minimum, skewness, kurtosis, maximum, variance, possible_values, median, cumulative_prob


# Class to perform data clustering
class DataClustering:
    def __init__(self, data, k):
        self.data = data
        self.k = k
        self.cluster_centers = None
        self.labels = None
        self.inertia = None
        self.separated_data = []

        self._cluster_data()

    # Function to perform clustering
    def _cluster_data(self):
        from sklearn.cluster import KMeans

        k

### Versions

```shell
GPT 3.5 and GPT 4
```

COMMENT:
Not related to this project. You can report to wherever you got the models from.

Labels: Bug, Needs Triage