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from ludwig.api import LudwigModel
import pandas

df = pandas.read_csv('rotten_tomatoes.csv')
model = LudwigModel(config='rotten_tomatoes.yaml')
results = model.train(dataset=df)

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input_features:
    - name: genres
      type: set
      preprocessing:
          tokenizer: comma
    - name: content_rating
      type: category
    - name: top_critic
      type: binary
    - name: runtime

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input_features:
    - name: genres
      type: set
    - name: content_rating
      type: category
    - name: top_critic
      type: binary
    - name: runtime
      type: number
    - name: review_content

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model_type: llm
base_model: meta-llama/Llama-2-7b-hf

quantization:
  bits: 4

adapter:
  type: lora

prompt:

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import os
import requests
import json
from apiKey import apikey
from apiKey import serpapi
import yfinance as yf
from yahooquery import Ticker
import openai

os.environ["SERPAPI_API_KEY"] = serpapi

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import streamlit as st
import matplotlib.pyplot as plt
from financial_analyst import financial_analyst

def main():
    st.title("AI Financial Analyst App")

    company_name = st.text_input("Company name:")
    analyze_button = st.button("Analyze")

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require 'nokogiri'
require 'selenium-webdriver'
require 'webdrivers'
require 'yaml'
require 'json'

chromedriver_path = '/Users/fsndzomga/Downloads/chromedriver-mac-arm64/chromedriver'
Selenium::WebDriver::Chrome::Service.driver_path = chromedriver_path

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require 'dotenv'
require 'ruby/openai'

Dotenv.load

class IdeaGeneratorService
  def initialize(industry, location)
    @industry = industry
    @location = location
  end

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class WebApp:
    def __init__(self):
        self.data = {}

    def fetch_data(self, url):
        # code to fetch data from a URL
        self.data = {"example": "data"}

    def process_data(self):
        # code to process data

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class DataFetcher:
    def fetch_data(self, url):
        # code to fetch data from a URL
        return {"example": "data"}

class DataProcessor:
    def process_data(self, data):
        # code to process data
        return {k: v.upper() for k, v in data.items()}