Add Google Finance

app.py

@@ -1,71 +1,110 @@
-import pandas as pd
-import gradio as gr
-import gradio as gr; print(gr.__version__)
-
-# Replace with path to your ESG data (CSV or other supported format)
-data_path = "ESG_data.csv"
-company_ratings = [
-  {"Company Name": "Apple Inc.", "Rating": 4.5},
-  {"Company Name": "Amazon.com, Inc.", "Rating": 4.2},
-  {"Company Name": "Microsoft Corporation", "Rating": 4.7},
-  {"Company Name": "Alphabet Inc. (Google)", "Rating": 4.8},
-  {"Company Name": "Tesla, Inc.", "Rating": 3.9},
-  {"Company Name": "Meta Platforms Inc. (Facebook)", "Rating": 3.1},
-]
-
-# Load ESG data
-esg_data = pd.DataFrame(company_ratings)
 import gradio as gr
 import pandas as pd
+import requests
+from bs4 import BeautifulSoup
+
+example_tickers = [
+    {"Ticker": "PLD", "Company Name": "Prologis Inc"},
+    {"Ticker": "PSA", "Company Name": "Public Storage"},
+    {"Ticker": "O", "Company Name": "Realty Income Corp"},
+    {
+        "Ticker": "META",
+        "Company Name": "Meta Platforms",
+    },
+    {"Ticker": "AMZN", "Company Name": "Amazon.com"},
+    {"Ticker": "MSFT", "Company Name": "Microsoft Corporation"},
+]
 
 
-inputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(4,"dynamic"), label="Input Data", interactive=1)]
-
-outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Predictions", headers=["Failures"])]
-
-
-
-def infer(input_dataframe):
-  return pd.DataFrame(input_dataframe)
-
-gr.Interface(fn = infer, inputs = inputs, outputs = outputs, examples = [[esg_data.head(2)]]).launch()
-
-# def get_esg_scores(ticker):
-#   """
-#   Finds ESG scores for a given ticker symbol in the loaded data.
+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
+    return row
+
+
+def get_esg_score_from_google_finance(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"
+    }
+
+    html = requests.get(
+        f"https://www.google.com/finance/quote/META:NASDAQ", headers=headers, timeout=30
+    ).text
+    soup = BeautifulSoup(html, "html.parser")
+    scores = soup.find_all("div", {"class": "IPIeJ"})
+
+    row["CDP Score"] = scores[0].find("div", {"class": "P6K39c"}).text
+
+    return row
+
+
+example_input_data = pd.DataFrame(example_tickers)
+
+
+inputs = [
+    gr.Dataframe(
+        row_count=(6, "dynamic"),
+        col_count=(1, "dynamic"),
+        label="Input Data",
+        interactive=1,
+    )
+]
 
-#   Args:
-#       ticker (str): Ticker symbol of the company.
+outputs = [
+    gr.Dataframe(
+        row_count=(6, "dynamic"),
+        col_count=(7, "fixed"),
+        label="ESG Scores",
+        headers=[
+            "Ticker",
+            "Company Name",
+            "Total ESG Risk Score",
+            "Environmental Risk Score",
+            "Social Risk Score",
+            "Governance Risk Score",
+            "CDP Score",
+        ],
+    )
+]
 
-#   Returns:
-#       pandas.DataFrame: Subset of ESG data for the ticker, 
-#           containing ESG scores if found, or an empty DataFrame 
-#           if not found.
-#   """
-#   filtered_data = esg_data[esg_data["Ticker Symbol"] == ticker.upper()]
-#   return filtered_data if not filtered_data.empty else pd.DataFrame()
 
-# def display_esg_scores(dataframe):
-#   """
-#   Displays ESG scores in a table format if a DataFrame is provided, 
-#   otherwise displays a message indicating no data found.
+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
+    )
 
-#   Args:
-#       dataframe (pandas.DataFrame): DataFrame containing ESG scores.
-#   """
-#   if dataframe.empty:
-#     return "No ESG data found for this ticker."
-#   else:
-#     # Select relevant ESG score columns (adjust based on your data)
-#     esg_scores = dataframe[["Ticker Symbol", "Governance Score", "Social Score", "Environmental Score"]]
-#     return gr.DataTable(dataframe=esg_scores.to_dict())
+    return input_dataframe
 
-# iface = gr.Interface(
-#   fn=get_esg_scores,
-#   inputs=gr.inputs.Textbox(label="Ticker Symbol"),
-#   outputs=esg_data,
-#   title="ESG Score Lookup",
-#   description="Enter a company ticker symbol to view its ESG scores (if available).",
-# )
 
-# iface.launch()
+gr.Interface(
+    fn=get_esg_scores,
+    inputs=inputs,
+    outputs=outputs,
+    title="🌳ESG  Data Scraper🌳\n\nIt scrapes ESG ratings from Yahoo Finance and Google Finance!",
+    examples=[[example_input_data.head(6)]],
+).launch()

flagged/log.csv

@@ -1,9 +0,0 @@
-name,Output,timestamp
-,Hello !!,2024-05-20 02:22:21.273157
-,Hello !!,2024-05-20 02:22:22.248193
-,Hello !!,2024-05-20 02:22:23.235456
-,Hello !!,2024-05-20 02:22:23.675012
-,Hello !!,2024-05-20 02:22:27.643848
-,Hello !!,2024-05-20 02:22:28.585934
-,Hello !!,2024-05-20 02:22:29.303183
-,Hello !!,2024-05-20 02:22:30.054843