USER_MSG_WITH_JOIN = """ Given the following details:

- Attribute 1: "{attr1}" from Table 1: "{table1}"
- Attribute 2: "{attr2}" from Table 2: "{table2}"
- Join Key: "{join_key}"
- Granularity Level: "{granu}".

Predict the Pearson's correlation coefficient between the two attributes after joining Table 1 and Table 2 on the specified join key at the given granularity level.

Then, estimate your confidence in the predicted correlation by providing the standard deviation as a measure of uncertainty.

Respond with your predictions in the following format:

{{
  "coefficient": "<predicted correlation coefficient>",
  "standard deviation": "<predicted uncertainty>"
}}

"""


BASE_TASK_DESC = """
You are given two attributes from a tabular dataset. Your task is to predict the Pearson's correlation coefficient between the two attributes and estimate your confidence in the predicted correlation by providing the standard deviation as a measure of uncertainty. Note that the standard deviation cannot be zero.

Now, begin to solve the following problem:
"""

# USER_MSG_WITHOUT_JOIN = """ 
# {task_desc}

# The two attributes are 

# - X: "{attr1}"
# - Y: "{attr2}"

# They are from the table: "{table}". The description for the table and the attributes are as follows:

# {tbl_desc}

# Respond with your predictions in the following format:

# ```json
# {{
#   "coefficient": "<predicted correlation coefficient>",
#   "standard deviation": "<predicted uncertainty>"
# }}
# ```
# """

USER_MSG_WITHOUT_JOIN = """ 

<Task>:
{task_desc}

<Attributes>
- "{attr1}"
- "{attr2}"

<Source Table>: "{table}"

<Descriptions>:
- Dataset Description: {tbl_desc}
- Attribute Descriptions:
    - "{attr1}": {var1_desc}
    - "{attr2}": {var2_desc}

Respond with your predictions in the following format:

```json
{{
  "coefficient": "<predicted correlation coefficient>",
  "standard deviation": "<predicted uncertainty>",
}}
```
"""