Flask application: Same source code working without error on VS Code but problematic with Amazon Lightsail Ubuntu instance

Hello.

Troubleshooting is difficult with only error messages on the browser.
How about trying logging to get the application log?
https://flask.palletsprojects.com/en/2.3.x/logging/

Thank you for sharing the code.
Judging from the error message and code, I think it occurred when trying to perform a direct multidimensional index operation on a Pandas DataFrame or Series.
I think the following stackoverflow answers will be helpful.
https://stackoverflow.com/questions/75939123/valueerror-multi-dimensional-indexing-e-g-obj-none-is-no-longer-suppor

I think the problem is in the following part of your code.

def compare_stocks(symbol1, symbol2):
    df1 = fetch_historical_data(symbol1)
    df2 = fetch_historical_data(symbol2)
    if df1 is None or df2 is None:
        return None, None, None, None, None, None, f"Could not fetch data for one or both symbols."
    common_start_date = max(df1.index.min(), df2.index.min())
    df1 = df1[df1.index >= common_start_date]
    df2 = df2[df2.index >= common_start_date]
    if df1.empty or df2.empty:
        return None, None, None, None, None, None, "No overlapping data available for the selected stocks."
    else:
        growth1, yearly_returns1 = calculate_growth(df1.copy())
        growth2, yearly_returns2 = calculate_growth(df2.copy())
        cumulative_returns1 = [0]
        cumulative_returns2 = [0]
        for val1, val2 in zip(growth1.values[1:], growth2.values[1:]):
            cumulative_returns1.append(((val1 - growth1.values[0]) / 100) * 100)
            cumulative_returns2.append(((val2 - growth2.values[0]) / 100) * 100)
        return growth1, growth2, yearly_returns1, yearly_returns2, cumulative_returns1, cumulative_returns2, None

So, why not try returning a numpy array with "to_numpy()" as shown below?
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy

def compare_stocks(symbol1, symbol2):
    df1 = fetch_historical_data(symbol1)
    df2 = fetch_historical_data(symbol2)
    if df1 is None or df2 is None:
        return None, None, None, None, None, None, f"Could not fetch data for one or both symbols."
    common_start_date = max(df1.index.min(), df2.index.min())
    df1 = df1[df1.index >= common_start_date]
    df2 = df2[df2.index >= common_start_date]
    if df1.empty or df2.empty:
        return None, None, None, None, None, None, "No overlapping data available for the selected stocks."
    else:
        growth1, yearly_returns1 = calculate_growth(df1.copy())
        growth2, yearly_returns2 = calculate_growth(df2.copy())
        cumulative_returns1 = [0]
        cumulative_returns2 = [0]
        growth1_values = growth1.to_numpy() # <-- add
        growth2_values = growth2.to_numpy() # <-- add
        for val1, val2 in zip(growth1_values[1:], growth2_values[1:]):
            cumulative_returns1.append(((val1 - rowth1_values[0]) / 100) * 100) # <-- edit
            cumulative_returns2.append(((val2 - growth2_values[0]) / 100) * 100) # <-- edit
        return growth1, growth2, yearly_returns1, yearly_returns2, cumulative_returns1, cumulative_returns2, None

These are the original codes on VS Code from where I started with launching Ubuntu instance, trying to make live the same project through Amazon Lightsail:

1. App.py

from flask import Flask, render_template, request
import requests
import pandas as pd
import matplotlib.pyplot as plt
from io import BytesIO, StringIO
import base64
import csv
import datetime

app = Flask(__name__)

# Replace with your actual API key from Alpha Vantage
API_KEY = "FI5B9EM5VOZTW6WJ"

def fetch_stock_symbols(exchange):
    url = f"https://www.alphavantage.co/query?function=LISTING_STATUS&apikey={API_KEY}&exchange={exchange}"
    try:
        response = requests.get(url)
        response.raise_for_status()

        if response.headers['Content-Type'] == 'application/x-download':
            csv_data = StringIO(response.text)
            csv_reader = csv.DictReader(csv_data)
            return [row['symbol'] for row in csv_reader if not row['symbol'].isdigit()]
        else:
            print("Unexpected response content type:", response.headers['Content-Type'])
            return []

    except requests.RequestException as e:
        print(f"Error fetching symbols: {e}")
        return []

def fetch_historical_data(symbol):
    ten_years_ago = datetime.datetime.now() - datetime.timedelta(days=10*365)
    start_date = ten_years_ago.strftime('%Y-%m-%d')

    url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&outputsize=full&apikey={API_KEY}"
    try:
        response = requests.get(url)
        response.raise_for_status()

        data = response.json()
        if "Time Series (Daily)" in data:
            df = pd.DataFrame.from_dict(data["Time Series (Daily)"], orient="index")
            df = df.rename(columns={"4. close": "close"})
            df["close"] = pd.to_numeric(df["close"])
            df.index = pd.to_datetime(df.index)
            df = df[df.index >= start_date]
            df.sort_index(inplace=True)
            return df

        return None

    except requests.RequestException as e:
        print(f"Error fetching data for {symbol}: {e}")
        return None

def calculate_growth(df, start_value=100):
    df = df.resample("Y").last()
    growth = (df["close"] / df["close"].iloc[0]) * start_value
    yearly_returns = (growth.pct_change() * 100).round(2)
    return growth, yearly_returns

def compare_stocks(symbol1, symbol2):
    df1 = fetch_historical_data(symbol1)
    df2 = fetch_historical_data(symbol2)

    if df1 is None or df2 is None:
        return None, None, None, None, None, None, f"Could not fetch data for one or both symbols."

    common_start_date = max(df1.index.min(), df2.index.min())

    df1 = df1[df1.index >= common_start_date]
    df2 = df2[df2.index >= common_start_date]

    if df1.empty or df2.empty:
        return None, None, None, None, None, None, "No overlapping data available for the selected stocks."
    else:
        growth1, yearly_returns1 = calculate_growth(df1.copy())
        growth2, yearly_returns2 = calculate_growth(df2.copy())

        cumulative_returns1 = [0]
        cumulative_returns2 = [0]
        for val1, val2 in zip(growth1.values[1:], growth2.values[1:]):
            cumulative_returns1.append(((val1 - growth1.values[0]) / 100) * 100)
            cumulative_returns2.append(((val2 - growth2.values[0]) / 100) * 100)

        return growth1, growth2, yearly_returns1, yearly_returns2, cumulative_returns1, cumulative_returns2, None

@app.route("/", methods=["GET", "POST"])
def index():
    symbols = []
    if request.method == "POST":
        exchange = request.form["exchange"]
        symbol1 = request.form["symbol1"]
        symbol2 = request.form["symbol2"]
        symbols = fetch_stock_symbols(exchange)

        growth1, growth2, yearly_returns1, yearly_returns2, cumulative_returns1, cumulative_returns2, error_message = compare_stocks(symbol1, symbol2)

        if error_message:
            return render_template("index.html", symbols=symbols, error_message=error_message)

        plt.figure(figsize=(10, 6))
        plt.plot(growth1.index, growth1.values, label=symbol1)
        plt.plot(growth2.index, growth2.values, label=symbol2)
        plt.xlabel("Year")
        plt.ylabel("Value of $100 Investment")
        plt.title("Stock Comparison")
        plt.legend()

        img = BytesIO()
        plt.savefig(img, format='png')
        img.seek(0)
        img_base64 = base64.b64encode(img.getvalue()).decode('utf-8')

        start_date = growth1.index[0].strftime('%Y-%m-%d')
        end_date = growth1.index[-1].strftime('%Y-%m-%d')
        final_value1 = growth1.values[-1]
        final_value2 = growth2.values[-1]

        comparison_message = (
            f"On {start_date}, $100 invested in {symbol1} would be worth ${final_value1:.2f} on {end_date}.\n"
            f"On {start_date}, $100 invested in {symbol2} would be worth ${final_value2:.2f} on {end_date}."
        )

        table_data = []
        for year, val1, ret1, val2, ret2, cum_ret1, cum_ret2 in zip(
            growth1.index.year,
            growth1.values,
            yearly_returns1.values,
            growth2.values,
            yearly_returns2.values,
            cumulative_returns1,
            cumulative_returns2
        ):
            table_data.append({
                "year": year,
                "val1": val1,
                "ret1": ret1,
                "val2": val2,
                "ret2": ret2,
                "cum_ret1": cum_ret1,
                "cum_ret2": cum_ret2
            })

        return render_template(
            "result.html",
            img_data=img_base64,
            comparison_message=comparison_message,
            table_data=table_data,
            start_date=start_date,
            end_date=end_date,
            symbol1=symbol1,
            symbol2=symbol2,
        )
    else:
        # Default to NASDAQ symbols on initial load
        symbols = fetch_stock_symbols('NASDAQ')
    return render_template("index.html", symbols=symbols)

if __name__ == "__main__":
    app.run(debug=True)

2. index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Stock Comparison</title>
</head>
<body>
    <h1>Compare Stocks</h1>
    <form method="POST">
        <label for="exchange">Select Stock Exchange:</label>
        <select name="exchange" id="exchange" onchange="this.form.submit()">
            <option value="NASDAQ" {% if request.form.get('exchange') == 'NASDAQ' %}selected{% endif %}>NASDAQ</option>
            <option value="NYSE" {% if request.form.get('exchange') == 'NYSE' %}selected{% endif %}>NYSE</option>
            <option value="AMEX" {% if request.form.get('exchange') == 'AMEX' %}selected{% endif %}>AMEX</option>
        </select>
        <br><br>
        <label for="symbol1">Stock Symbol 1:</label>
        <select name="symbol1" id="symbol1" required>
            {% for symbol in symbols %}
            <option value="{{ symbol }}">{{ symbol }}</option>
            {% endfor %}
        </select>
        <br><br>
        <label for="symbol2">Stock Symbol 2:</label>
        <select name="symbol2" id="symbol2" required>
            {% for symbol in symbols %}
            <option value="{{ symbol }}">{{ symbol }}</option>
            {% endfor %}
        </select>
        <br><br>
        <button type="submit">Compare</button>
    </form>
    {% if error_message %}
    <p style="color: red;">{{ error_message }}</p>
    {% endif %}
</body>
</html>

3. result.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Stock Comparison Result</title>
</head>
<body>
    <h1>Stock Comparison Result</h1>
    <p>{{ comparison_message }}</p>
    <img src="data:image/png;base64,{{ img_data }}" alt="Stock Comparison Graph">
    <table border="1">
        <thead>
            <tr>
                <th>Year</th>
                <th>{{ symbol1 }} Value</th>
                <th>{{ symbol1 }} Yearly Return (%)</th>
                <th>{{ symbol2 }} Value</th>
                <th>{{ symbol2 }} Yearly Return (%)</th>
                <th>{{ symbol1 }} Cumulative Return (%)</th>
                <th>{{ symbol2 }} Cumulative Return (%)</th>
            </tr>
        </thead>
        <tbody>
            {% for row in table_data %}
            <tr>
                <td>{{ row.year }}</td>
                <td>{{ row.val1 }}</td>
                <td>{{ row.ret1 }}</td>
                <td>{{ row.val2 }}</td>
                <td>{{ row.ret2 }}</td>
                <td>{{ row.cum_ret1 }}</td>
                <td>{{ row.cum_ret2 }}</td>
            </tr>
            {% endfor %}
        </tbody>
    </table>
    <br>
    <a href="/">Back to Comparison</a>
</body>
</html>

While troubleshooting in order to make live for Ubuntu, here are the 2 versions of app.py (with errors):

1. app.py

from flask import Flask, render_template, request
import requests
import pandas as pd
import matplotlib.pyplot as plt
from io import BytesIO, StringIO
import base64
import csv
import datetime

app = Flask(__name__)


# Replace with your actual API key from Alpha Vantage
API_KEY = "FI5B9EM5VOZTW6WJ"

def fetch_stock_symbols(exchange):
    url = f"https://www.alphavantage.co/query?function=LISTING_STATUS&apikey={API_KEY}&exchange={exchange}"
    try:
        response = requests.get(url)
        response.raise_for_status()

        if response.headers['Content-Type'] == 'application/x-download':
            csv_data = StringIO(response.text)
            csv_reader = csv.DictReader(csv_data)
            return [row['symbol'] for row in csv_reader if not row['symbol'].isdigit()]
        else:
            print("Unexpected response content type:", response.headers['Content-Type'])
            return []

    except requests.RequestException as e:
        print(f"Error fetching symbols: {e}")
        return []

def fetch_historical_data(symbol):
    ten_years_ago = datetime.datetime.now() - datetime.timedelta(days=10*365)
    start_date = ten_years_ago.strftime('%Y-%m-%d')

    url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&outputsize=full&apikey={API_KEY}"
    try:
        response = requests.get(url)
        response.raise_for_status()

        data = response.json()
        if "Time Series (Daily)" in data:
            df = pd.DataFrame.from_dict(data["Time Series (Daily)"], orient="index")
            df = df.rename(columns={"4. close": "close"})
            df["close"] = pd.to_numeric(df["close"])
            df.index = pd.to_datetime(df.index)
            df = df[df.index >= start_date]
            df.sort_index(inplace=True)
            return df

        return None

    except requests.RequestException as e:
        print(f"Error fetching data for {symbol}: {e}")
        return None

def calculate_growth(df, start_value=100):
    df = df.resample("Y").last()
    growth = (df["close"] / df["close"].iloc[0]) * start_value
    yearly_returns = (growth.pct_change() * 100).round(2)
    return growth, yearly_returns

def compare_stocks(symbol1, symbol2):
    df1 = fetch_historical_data(symbol1)
    df2 = fetch_historical_data(symbol2)

    if df1 is None or df2 is None:
        return None, None, None, None, None, None, f"Could not fetch data for one or both symbols."

    common_start_date = max(df1.index.min(), df2.index.min())

    df1 = df1[df1.index >= common_start_date]
    df2 = df2[df2.index >= common_start_date]

    if df1.empty or df2.empty:
        return None, None, None, None, None, None, "No overlapping data available for the selected stocks."
    else:
        growth1, yearly_returns1 = calculate_growth(df1.copy())
        growth2, yearly_returns2 = calculate_growth(df2.copy())

        cumulative_returns1 = [0]
        cumulative_returns2 = [0]
        for val1, val2 in zip(growth1.values[1:], growth2.values[1:]):
            cumulative_returns1.append(((val1 - growth1.values[0]) / 100) * 100)
            cumulative_returns2.append(((val2 - growth2.values[0]) / 100) * 100)

        return growth1, growth2, yearly_returns1, yearly_returns2, cumulative_returns1, cumulative_returns2, None

@app.route("/", methods=["GET", "POST"])
def index():
    symbols = []
    if request.method == "POST":
        exchange = request.form["exchange"]
        symbol1 = request.form["symbol1"]
        symbol2 = request.form["symbol2"]
        symbols = fetch_stock_symbols(exchange)

        growth1, growth2, yearly_returns1, yearly_returns2, cumulative_returns1, cumulative_returns2, error_message = compare_stocks(symbol1, symbol2)

        if error_message:
            return render_template("index.html", symbols=symbols, error_message=error_message)

        plt.figure(figsize=(10, 6))
        plt.plot(growth1.index, growth1.values, label=symbol1)
        plt.plot(growth2.index, growth2.values, label=symbol2)
        plt.xlabel("Year")
        plt.ylabel("Value of $100 Investment")
        plt.title("Stock Comparison")
        plt.legend()

        img = BytesIO()
        plt.savefig(img, format='png')
        img.seek(0)
        img_base64 = base64.b64encode(img.getvalue()).decode('utf-8')

        start_date = growth1.index[0].strftime('%Y-%m-%d')
        end_date = growth1.index[-1].strftime('%Y-%m-%d')
        final_value1 = growth1.values[-1]
        final_value2 = growth2.values[-1]

        comparison_message = (
            f"On {start_date}, $100 invested in {symbol1} would be worth ${final_value1:.2f} on {end_date}.\n"
            f"On {start_date}, $100 invested in {symbol2} would be worth ${final_value2:.2f} on {end_date}."
        )

        table_data = []
        for year, val1, ret1, val2, ret2, cum_ret1, cum_ret2 in zip(
            growth1.index.year,
            growth1.values,
            yearly_returns1.values,
            growth2.values,
            yearly_returns2.values,
            cumulative_returns1,
            cumulative_returns2
        ):
            table_data.append({
                "year": year,
                "val1": val1,
                "ret1": ret1,
                "val2": val2,
                "ret2": ret2,
                "cum_ret1": cum_ret1,
                "cum_ret2": cum_ret2
            })

        return render_template(
            "result.html",
            img_data=img_base64,
            comparison_message=comparison_message,
            table_data=table_data,
            start_date=start_date,
            end_date=end_date,
            symbol1=symbol1,
            symbol2=symbol2,
        )
    else:
        # Default to NASDAQ symbols on initial load
        symbols = fetch_stock_symbols('NASDAQ')
    return render_template("index.html", symbols=symbols)


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

2. app.py

from flask import Flask, render_template, request
import requests
import pandas as pd
import matplotlib.pyplot as plt
from io import BytesIO, StringIO
import base64
import csv
import datetime
import numpy as np

app = Flask(__name__)

API_KEY = "FI5B9EM5VOZTW6WJ"

def fetch_stock_symbols(exchange):
    url = f"https://www.alphavantage.co/query?function=LISTING_STATUS&apikey={API_KEY}&exchange={exchange}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        if response.headers['Content-Type'] == 'application/x-download':
            csv_data = StringIO(response.text)
            csv_reader = csv.DictReader(csv_data)
            return [row['symbol'] for row in csv_reader if not row['symbol'].isdigit()]
        else:
            print("Unexpected response content type:", response.headers['Content-Type'])
            return []
    except requests.RequestException as e:
        print(f"Error fetching symbols: {e}")
        return []

def fetch_historical_data(symbol):
    ten_years_ago = datetime.datetime.now() - datetime.timedelta(days=10*365)
    start_date = ten_years_ago.strftime('%Y-%m-%d')
    url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&outputsize=full&apikey={API_KEY}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()
        if "Time Series (Daily)" in data:
            df = pd.DataFrame.from_dict(data["Time Series (Daily)"], orient="index")
            df = df.rename(columns={"4. close": "close"})
            df["close"] = pd.to_numeric(df["close"])
            df.index = pd.to_datetime(df.index)
            df = df[df.index >= start_date]
            df.sort_index(inplace=True)
            return df
        return None
    except requests.RequestException as e:
        print(f"Error fetching data for {symbol}: {e}")
        return None

def calculate_growth(df, start_value=100):
    df = df.resample("Y").last()
    growth = (df["close"] / df["close"].iloc[0]) * start_value
    yearly_returns = (growth.pct_change() * 100).round(2)
    return growth, yearly_returns

def compare_stocks(symbol1, symbol2):
    df1 = fetch_historical_data(symbol1)
    df2 = fetch_historical_data(symbol2)
    if df1 is None or df2 is None:
        return None, None, None, None, None, None, f"Could not fetch data for one or both symbols."
    common_start_date = max(df1.index.min(), df2.index.min())
    df1 = df1[df1.index >= common_start_date]
    df2 = df2[df2.index >= common_start_date]
    if df1.empty or df2.empty:
        return None, None, None, None, None, None, "No overlapping data available for the selected stocks."
    else:
        growth1, yearly_returns1 = calculate_growth(df1.copy())
        growth2, yearly_returns2 = calculate_growth(df2.copy())
        cumulative_returns1 = [0]
        cumulative_returns2 = [0]
        for val1, val2 in zip(growth1.values[1:], growth2.values[1:]):
            cumulative_returns1.append(((val1 - growth1.values[0]) / 100) * 100)
            cumulative_returns2.append(((val2 - growth2.values[0]) / 100) * 100)
        return growth1, growth2, yearly_returns1, yearly_returns2, cumulative_returns1, cumulative_returns2, None

@app.before_request
def validate_request():
    if request.method == "POST":
        print("Form data received:", request.form)
        if not request.form.get("exchange") or not request.form.get("symbol1") or not request.form.get("symbol2"):
            return {"error": "Missing form data"}, 400

@app.route("/", methods=["GET", "POST"])
def index():
    symbols = []
    if request.method == "POST":
        exchange = request.form["exchange"]
        symbol1 = request.form["symbol1"]
        symbol2 = request.form["symbol2"]
        symbols = fetch_stock_symbols(exchange)
        growth1, growth2, yearly_returns1, yearly_returns2, cumulative_returns1, cumulative_returns2, error_message = compare_stocks(symbol1, symbol2)
        if error_message:
            return render_template("index.html", symbols=symbols, error_message=error_message)
        plt.figure(figsize=(10, 6))
        plt.plot(np.array(growth1.index), np.array(growth1.values), label=symbol1)
        plt.plot(np.array(growth2.index), np.array(growth2.values), label=symbol2)
        plt.xlabel("Year")
        plt.ylabel("Value of $100 Investment")
        plt.title("Stock Comparison")
        plt.legend()
        img = BytesIO()
        plt.savefig(img, format='png')
        img.seek(0)
        img_base64 = base64.b64encode(img.getvalue()).decode('utf-8')
        start_date = growth1.index[0].strftime('%Y-%m-%d')
        end_date = growth1.index[-1].strftime('%Y-%m-%d')
        final_value1 = growth1.values[-1]
        final_value2 = growth2.values[-1]
        comparison_message = (
            f"On {start_date}, $100 invested in {symbol1} would be worth ${final_value1:.2f} on {end_date}.\n"
            f"On {start_date}, $100 invested in {symbol2} would be worth ${final_value2:.2f} on {end_date}."
        )
        table_data = []
        for year, val1, ret1, val2, ret2, cum_ret1, cum_ret2 in zip(
            growth1.index.year,
            growth1.values,
            yearly_returns1.values,
            growth2.values,
            yearly_returns2.values,
            cumulative_returns1,
            cumulative_returns2
        ):
            table_data.append({
                "year": year,
                "val1": val1,
                "ret1": ret1,
                "val2": val2,
                "ret2": ret2,
                "cum_ret1": cum_ret1,
                "cum_ret2": cum_ret2
            })
        return render_template(
            "result.html",
            img_data=img_base64,
            comparison_message=comparison_message,
            table_data=table_data,
            start_date=start_date,
            end_date=end_date,
            symbol1=symbol1,
            symbol2=symbol2,
        )
    else:
        symbols = fetch_stock_symbols('NASDAQ')
    return render_template("index.html", symbols=symbols)

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

I understand it is not safe to share API but since the same not mission critical, sharing for easing troubleshooting. Will later delete this and generate another API key.