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| import requests | |
| import os | |
| from dotenv import load_dotenv | |
| import streamlit as st | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| # Load your API key from .env file | |
| load_dotenv() | |
| API_KEY = os.getenv("OPENWEATHER_API_KEY") | |
| def get_weatherData(city, unit): | |
| url = "https://api.openweathermap.org/data/2.5/weather" | |
| params = { | |
| "q": city, | |
| "appid": API_KEY, | |
| "units": unit | |
| } | |
| response = requests.get(url, params=params) | |
| data = response.json() | |
| return data | |
| # Up to 40 hours worth, 3-hour intervals for 5 days | |
| def get_dailyForecast(city, unit, count): | |
| url = "https://api.openweathermap.org/data/2.5/forecast?" | |
| params = { | |
| "q": city, | |
| "appid": API_KEY, | |
| "units": unit, | |
| "cnt": count | |
| } | |
| response = requests.get(url, params = params) | |
| data = response.json() | |
| return data["list"] | |
| def forecastToDF(data_list): | |
| temps = [] | |
| times = [] | |
| for item in data_list: | |
| times.append(item["dt_txt"]) | |
| temps.append(item["main"]["temp"]) | |
| df = pd.DataFrame({"Time": times, "Temperature": temps}) | |
| df["Time"] = pd.to_datetime(df["Time"]) | |
| return df | |
| def get_weather(data, units): | |
| city_name = data["name"] | |
| temp = data["main"]["temp"] | |
| humidity = data["main"]["humidity"] | |
| description = data["weather"][0]["description"] | |
| if (units == "Kelvin"): | |
| return f"In {city_name}, it is {temp} {units[0]} with {description}" | |
| else: | |
| return f"In {city_name}, it is {temp} \u00B0{units[0]} with {description}" | |
| def get_icon(data): | |
| icon_code = data["weather"][0]["icon"] | |
| return f"http://openweathermap.org/img/wn/{icon_code}@2x.png" | |
| # STREAMLIT SECTION STARTS HERE | |
| st.title("Weather App") | |
| st.sidebar.header("Weather Settings") | |
| # Sidebar configs (User input) | |
| city = st.sidebar.text_input(label="City", value="Durham") | |
| units = st.sidebar.selectbox("Units:", ["Kelvin", "Celsius", "Fahrenheit"]) | |
| unit_map = {"Celsius": "metric", "Fahrenheit": "imperial", "Kelvin": "standard"} | |
| count = st.sidebar.slider("Forecast Step (3-hour intervals)", 1, 40, 20) | |
| # Data retrieval | |
| data = get_weatherData(city, unit_map[units]) | |
| data_list = get_dailyForecast(city, unit_map[units], count) | |
| icon_url = get_icon(data) | |
| # Displaying information | |
| st.image(icon_url, width=100) | |
| st.write(get_weather(data, units)) | |
| df = forecastToDF(data_list) | |
| # line plot | |
| fig, ax = plt.subplots(figsize=(8, 4)) | |
| ax.plot(df["Time"], df["Temperature"], marker="o", linestyle="-", color="r") | |
| ax.set_title(f"Temperature Forecast for {city} (3-Hour Intervals)", fontsize=14) | |
| ax.set_xlabel("Time", fontsize=12) | |
| ax.set_ylabel(f"Temperature ({units})", fontsize=12) | |
| plt.xticks(rotation=90) | |
| st.pyplot(fig) | |