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"""
Obtain data from https://sectors.app
Accompanying course material: https://sectors.app/bulletin/ai-search
Easily generalizes to multiple csvs, or sqlites since we just load
them into one table
- this is how GPT-4o-turbo cheat in the gun-to-knife-fight:
- https://sectors.app/bulletin/deepseek
Usage: python pre/repl.py
- sub-industries in our df where the parent industry is coal-related
- industries in our db with more than 45 total number of companies
- top 5 largest industries in our db by sum of market cap
"""
import os
from dotenv import load_dotenv
import pandas as pd
from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate
from langchain_experimental.tools import PythonAstREPLTool
from langchain_core.output_parsers.openai_tools import JsonOutputKeyToolsParser
load_dotenv()
SECTORS_API_KEY = os.getenv("SECTORS_API_KEY")
df = pd.read_csv('./datasets/industry-leaders-full.csv')
industry = df.loc[:, ['industry', 'sub_industry', 'total_market_cap', 'num_of_companies']]
python_repl = PythonAstREPLTool(locals={"df": industry})
llm = ChatGroq(
model_name="llama-3.3-70b-versatile" # Updated from deprecated llama3-70b-8192
)
system_prompt = f""" Here is the output of `df.head().to_markdown()`: \\
\\`\\`\\`
{industry.head().to_markdown()}
\\`\\`\\`
top_mcap_gainers are for Top Market Cap Gainers; Use Python code to answer whenever possible. Answer with pretty print and in tabular format as much as possible, in the neatest way possible.
"""
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
tools = [
python_repl
]
llm_with_tools = llm.bind_tools(tools, tool_choice=python_repl.name)
parser = JsonOutputKeyToolsParser(key_name=python_repl.name, first_tool_only=True)
if __name__ == "__main__":
query_string = input("🤖: Enter a query: ")
# the last python_repl tool is the one that will be used to execute python code
# remove it if you want to see what it will execute but not actually execute it
# chain = prompt | llm_with_tools | parser
chain = prompt | llm_with_tools | parser | python_repl
response = chain.invoke({"input": query_string})
print(response)