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import json | |
import random | |
from typing import List, Optional | |
import pandas as pd | |
def get_current_df(dfs: List[pd.DataFrame], current: int) -> pd.DataFrame: | |
if len(dfs) == 0: | |
return pd.DataFrame() | |
else: | |
return dfs[current] | |
def query_llm_mock( | |
messages, | |
history: List, | |
df: pd.DataFrame, | |
llm_type: str, | |
api_key: str, | |
system_prompt: str, | |
): | |
"""Chat function that streams responses using mock llm. | |
Args: | |
messages (str or list): User input message(s). | |
history (list): Conversation history. | |
dfs (List[pd.DataFrame): a representation of the data already obtained | |
system_prompt (str): The syste prompt | |
openai_client (OpenAI): The OpenAI client | |
Returns: | |
str: The assistant's response. | |
""" | |
mock_json = json.dumps( | |
{ | |
"Medications": [ | |
{ | |
"Medication name": "Tamsulosin", | |
"Passes_RBB": "Yes", | |
"Random": random.random(), | |
}, | |
{ | |
"Medication name": "Metoprolol", | |
"Passes_RBB": "Yes", | |
"Random": random.random(), | |
}, | |
{ | |
"Medication name": "Bromocriptine", | |
"Passes_RBB": "Yes", | |
"Random": random.random(), | |
}, | |
{ | |
"Medication name": "Reserpine", | |
"Passes_RBB": "Yes", | |
"Random": random.random(), | |
}, | |
{ | |
"Medication name": "Rasagiline", | |
"Passes_RBB": "Yes", | |
"Random": random.random(), | |
}, | |
] | |
} | |
) | |
yield ( | |
f"Good question!\n" | |
f"Here's the data frame in JSON format:\n" | |
f"```json\n" | |
f"{mock_json if random.random() > 0.5 else ''}\n" | |
f"```\n\n" | |
f"Hope this is useful." | |
) | |
def llm_extract_table_mock(chat_output, llm_type, api_key) -> str: | |
dic = { | |
"Medications": [ | |
{ | |
"Name": "Medication Name", | |
"key1": "value1", | |
"key2": "value2", | |
"Random": random.random(), | |
}, | |
{ | |
"Name": "Medication Name", | |
"key1": "value1", | |
"key2": "value2", | |
"Random": random.random(), | |
}, | |
] | |
} | |
return json.dumps(dic) | |