import gradio as gr
import pandas as pd
import requests
from bs4 import BeautifulSoup

example_tickers = [
    {"Ticker": "PLD", "Company Name": "Prologis Inc", "Market": "NYSE"},
    {"Ticker": "PSA", "Company Name": "Public Storage", "Market": "NYSE"},
    {"Ticker": "META", "Company Name": "Meta Platforms", "Market": "NASDAQ"},
    {"Ticker": "MSFT", "Company Name": "Microsoft Corporation", "Market": "NASDAQ"},
    {"Ticker": "AMZN", "Company Name": "Amazon.com", "Market": "NASDAQ"},
]


def get_esg_from_yahoo_finance(row):
    elements = []
    # This is a standard user-agent of Chrome browser running on Windows 10
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36"
    }
    html = requests.get(
        "https://finance.yahoo.com/quote/" + row.Ticker + "/sustainability",
        headers=headers,
    ).text
    soup = BeautifulSoup(html, "html.parser")

    scores = soup.find_all("div", {"class": "content svelte-y3c2sq"})
    for score in scores:
        elements.append(float(score.find("h4").text.strip()))
    if elements:
        row["Total ESG Risk Score"] = elements[0]
        row["Environmental Risk Score"] = elements[1]
        row["Social Risk Score"] = elements[2]
        row["Governance Risk Score"] = elements[3]
    else:
        row["Total ESG Risk Score"] = None
        row["Environmental Risk Score"] = None
        row["Social Risk Score"] = None
        row["Governance Risk Score"] = None

    row["Yahoo Finance Link"] = (
        f"[Yahoo Link](https://finance.yahoo.com/quote/"
        + row.Ticker
        + "/sustainability)"
    )
    return row


def get_esg_score_from_google_finance(row):
    print(row)
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.5060.134 Safari/537.36"
    }
    link = f"https://www.google.com/finance/quote/{row.Ticker}:{row.Market}"

    html = requests.get(link, headers=headers, timeout=30).text

    soup = BeautifulSoup(html, "html.parser")
    scores = soup.find_all("div", {"class": "IPIeJ"})
    if scores:
        row["CDP Score"] = scores[0].find("div", {"class": "P6K39c"}).text

    else:
        row["CDP Score"] = None
    row["Google Finance Link"] = f"[Google Finance Link]({link})"

    return row


example_input_data = pd.DataFrame(example_tickers)


inputs = [
    gr.Dataframe(
        row_count=(5, "dynamic"),
        col_count=(3, "dynamic"),
        label="Input Data",
        interactive=1,
        headers=[
            "Ticker",
            "Company Name",
            "Market",
        ],
    )
]

outputs = [
    gr.Dataframe(
        row_count=(5, "dynamic"),
        col_count=(10, "fixed"),
        label="ESG Scores",
        headers=[
            "Ticker",
            "Company Name",
            "Market",
            "Total ESG Risk Score",
            "Environmental Risk Score",
            "Social Risk Score",
            "Governance Risk Score",
            "Yahoo Finance Link",
            "CDP Score",
            "Google Finance Link",
        ],
        datatype=[
            "str",
            "str",
            "str",
            "str",
            "str",
            "str",
            "str",
            "markdown",
            "str",
            "markdown",
        ],
    )
]


def get_esg_scores(input_dataframe):
    input_dataframe = input_dataframe.apply(
        lambda x: get_esg_from_yahoo_finance(x), axis=1
    )
    input_dataframe = input_dataframe.apply(
        lambda x: get_esg_score_from_google_finance(x), axis=1
    )

    return input_dataframe


gr.Interface(
    fn=get_esg_scores,
    inputs=inputs,
    outputs=outputs,
    title="🌳ESG  Data Scraper🌳 It scrapes ESG ratings from Yahoo Finance and Google Finance!",
    examples=[[example_input_data.head(6)]],
).launch()