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# import pdb
# pdb.set_trace()
import os
import sqlite3
import warnings
import pandas as pd
from flask import Flask, render_template, request
from langchain_community.llms import Ollama
from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain
import subprocess
# command = "ollama list"
# subprocess.run(command, shell=True, check=True, text=True)
print("Starting the server...")
app = Flask(__name__)
# Suppressing warnings
warnings.filterwarnings("ignore")
# Initializing the language model
# Ollama model
print("Initializing the language model...")
llm = Ollama(model="pxlksr/defog_sqlcoder-7b-2:Q4_K")
UPLOAD_FOLDER = 'uploads'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
# Prompt template for language model
print("Initializing the prompt template...")
template = """
### Task
Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]
### Instructions
- If you cannot answer the question with the available database schema, return 'I do not know'
### Database Schema
The query will run on a database with the following schema:
{table_metadata_string}
### Answer
Given the database schema, here is the SQL query that answers [QUESTION]{user_question}[/QUESTION]
[SQL]
"""
prompt = PromptTemplate(template=template, input_variables=["user_question", "table_metadata_string"])
# Function to get response from language model
def get_llm_response(user_question, table_metadata_string):
llm_chain = LLMChain(prompt=prompt, llm=llm)
response = llm_chain.run({"user_question": user_question, "table_metadata_string": table_metadata_string})
return response
def run_sql_query(csv_files, sql_query):
conn = sqlite3.connect(':memory:')
for file_path in csv_files:
df_name = os.path.splitext(os.path.basename(file_path))[0] # Extract file name without extension
df = pd.read_csv(file_path)
df.to_sql(df_name, conn, index=False)
result = pd.read_sql_query(sql_query, con=conn)
return result.to_html()
history = []
history_dll = []
print("Server started successfully!")
@app.route('/', methods=['GET', 'POST'])
def ddl_query():
ddl = None
if request.method == 'POST':
ddl = request.form['ddl']
user_question = request.form.get('user_question', None)
if user_question:
output = get_llm_response(user_question, ddl)
# Insert the new history item at the beginning of the list
history_dll.insert(0, {'query': user_question, 'response': output})
return render_template('index.html', history=history_dll, ddl=ddl)
@app.route('/dbms_query', methods=['GET', 'POST'])
def index():
result = None
query = None
if request.method == 'POST':
uploaded_files = request.files.getlist('file')
csv_files = []
for file in uploaded_files:
if file.filename != '':
file_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
file.save(file_path)
csv_files.append(file_path)
sql_query = request.form['sql_query']
query = sql_query
# Execute SQL query and store result in history
result = run_sql_query(csv_files, sql_query)
history.append({'query': sql_query, 'result': result})
return render_template('database_selection_index.html', result=result, query=query, history=history)
if __name__ == '__main__':
print("Running the server...")
app.run(debug=True, port = 7860, host = '0.0.0.0')
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