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def get_response_sql(user_query, chat_history, plot=False):
    
    # Specify the path to the SQLite database
    db_path = "metadataDB/output_database.db"

    # Connect to the SQLite database
    db = SQLDatabase.from_uri(f"sqlite:///{db_path}")

    underspecified = classify_underspecified_query(user_query , chat_history)
    

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def get_response_sql(user_query, chat_history, plot=False):
    
    # Specify the path to the SQLite database
    db_path = "metadataDB/output_database.db"

    # Connect to the SQLite database
    db = SQLDatabase.from_uri(f"sqlite:///{db_path}")

    underspecified = classify_underspecified_query(user_query , chat_history)
    

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#!/usr/bin/env python
from typing import Iterable
from collections import Counter
import os
import logging
import sys
import json
import click
import datasets
import numpy as np

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import json
import argparse
from typing import Optional, Union, Tuple
import torch

torch.manual_seed(0)

from transformers import BertModel, BertTokenizer, PreTrainedModel, BertConfig
from transformers.modeling_outputs import MultipleChoiceModelOutput

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import json
import os

all_types_to_idx = {
    'Task': 0,
    'Method': 1,
    'Material': 2,
    'Metric': 3,
    'OtherScientificTerm': 4,
    'Generic': 5

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import random
from collections import Counter
from urllib.parse import urlparse
import json
import os
import re

from tqdm import tqdm

urls_counts = {}

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from typing import List
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy
import random

class NormalGammaPrior():
    """"
    Suppose X is distributed according to a normal distribution:  X ~ N(mu, tau^{-1})

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import statistics as st

import scipy.stats
import numpy as np

def metric1(scores, row_aggregator, column_aggregator, cell_aggregator):
    row_values = []
    for row_idx, row1 in enumerate(scores):
        diagonal_x = row1[row_idx]
        row_values.append(

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show_unpublished_scores: true
datasets:
  blind_labels: danielk/genie_labels
evaluator:
  image: jbragg/genie-evaluator
  input_path: /preds/
  predictions_filename: predictions.json
  label_path: /labels/
  output_path: /results
  arguments:

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import statistics as st

def metric1(scores, row_aggregator, column_aggregator, cell_aggregator):
    row_values = []
    for row_idx, row1 in enumerate(scores):
        diagonal_x = row1[row_idx]
        row_values.append(
            column_aggregator(
                [cell_aggregator(diagonal_x, x, abs(col_idx - row_idx)) for col_idx, x in enumerate(row1) if col_idx !=  row_idx]
            )