initial commit

requirements.txt

@@ -0,0 +1,5 @@
+python-dotenv 
+pandas
+requests
+streamlit
+Pillow

weather_app.py

@@ -0,0 +1,216 @@
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import pandas as pd
+from dotenv import load_dotenv
+import os
+from PIL import Image
+from io import BytesIO
+
+# Load environment variables
+load_dotenv()
+API_KEY = os.getenv('WEATHER_API_KEY')
+BASE_URL = "http://api.weatherapi.com/v1"
+
+# Configure the app
+st.set_page_config(
+    page_title="Weather Dashboard",
+    page_icon="🌤️",
+    layout="wide"
+)
+
+# Custom CSS
+st.markdown("""
+    <style>
+    .weather-icon {
+        width: 64px;
+        height: 64px;
+        margin: 10px auto;
+    }
+    .stMetric {
+        background-color: rgba(255, 255, 255, 0.1);
+        padding: 10px;
+        border-radius: 5px;
+    }
+    .main-header {
+        text-align: center;
+        color: #1E88E5;
+        padding: 20px;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+def load_weather_icon(icon_url):
+    """Load and display weather icon from URL"""
+    try:
+        response = requests.get(icon_url)
+        img = Image.open(BytesIO(response.content))
+        return img
+    except Exception as e:
+        st.warning(f"Could not load weather icon: {e}")
+        return None
+
+def get_weather_data(location, is_historical=False, date=None):
+    """Fetch weather data from the API"""
+    try:
+        if is_historical:
+            endpoint = f"{BASE_URL}/history.json"
+            params = {
+                'key': API_KEY,
+                'q': location,
+                'dt': date.strftime('%Y-%m-%d')
+            }
+        else:
+            endpoint = f"{BASE_URL}/forecast.json"
+            params = {
+                'key': API_KEY,
+                'q': location,
+                'days': 1,
+                'aqi': 'no'
+            }
+
+        response = requests.get(endpoint, params=params)
+        response.raise_for_status()
+        return response.json()
+
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error fetching weather data: {e}")
+        return None
+
+def display_weather_data(weather_data, selected_datetime, container):
+    """Display weather data in the specified container"""
+    if not weather_data:
+        container.error("No weather data available")
+        return
+
+    container.subheader(f"Date: {selected_datetime.strftime('%Y-%m-%d %H:%M')}")
+    
+    # Handle both historical and forecast data structure
+    if 'current' in weather_data:
+        current_data = weather_data['current']
+    else:
+        historical_hour = min(
+            weather_data['forecast']['forecastday'][0]['hour'],
+            key=lambda x: abs(datetime.strptime(x['time'], '%Y-%m-%d %H:%M').time().hour - 
+                           selected_datetime.time().hour)
+        )
+        current_data = historical_hour
+
+    # Display current weather icon and condition
+    icon_url = f"https:{current_data['condition']['icon']}"
+    weather_icon = load_weather_icon(icon_url)
+    if weather_icon:
+        col1, col2, col3 = container.columns([1, 2, 1])
+        with col2:
+            st.image(weather_icon, caption=current_data['condition']['text'], use_container_width=True)
+    
+    # Create metrics in a grid
+    metric_col1, metric_col2 = container.columns(2)
+    
+    with metric_col1:
+        st.metric("Temperature", f"{current_data['temp_c']}°C",
+                 f"Feels like {current_data['feelslike_c']}°C")
+        st.metric("Wind", f"{current_data['wind_kph']} km/h",
+                 f"Direction: {current_data['wind_dir']}")
+    
+    with metric_col2:
+        st.metric("Humidity", f"{current_data['humidity']}%")
+        st.metric("Pressure", f"{current_data['pressure_mb']} mb")
+    
+    # Display hourly forecast
+    container.subheader("Hourly Forecast")
+    hourly_data = weather_data['forecast']['forecastday'][0]['hour']
+    
+    # Create a more visual hourly forecast
+    hours_to_show = 6
+    current_hour = selected_datetime.hour
+    relevant_hours = [h for h in hourly_data if datetime.strptime(h['time'], '%Y-%m-%d %H:%M').hour >= current_hour][:hours_to_show]
+    
+    hour_cols = container.columns(min(hours_to_show, len(relevant_hours)))
+    
+    for hour, col in zip(relevant_hours, hour_cols):
+        with col:
+            hour_time = datetime.strptime(hour['time'], '%Y-%m-%d %H:%M').strftime('%I %p')
+            st.markdown(f"**{hour_time}**")
+            
+            hour_icon_url = f"https:{hour['condition']['icon']}"
+            hour_icon = load_weather_icon(hour_icon_url)
+            if hour_icon:
+                st.image(hour_icon, use_container_width=True)
+            
+            st.markdown(f"🌡️ {hour['temp_c']}°C")
+            st.markdown(f"💧 {hour['chance_of_rain']}%")
+            st.markdown(f"💨 {hour['wind_kph']} km/h")
+    
+    # Display detailed hourly data
+    with container.expander("Detailed Hourly Forecast"):
+        hourly_df = pd.DataFrame([
+            {
+                'Time': datetime.strptime(hour['time'], '%Y-%m-%d %H:%M').strftime('%I:%M %p'),
+                'Temperature': f"{hour['temp_c']}°C",
+                'Condition': hour['condition']['text'],
+                'Chance of Rain': f"{hour['chance_of_rain']}%",
+                'Wind Speed': f"{hour['wind_kph']} km/h",
+                'Humidity': f"{hour['humidity']}%"
+            }
+            for hour in hourly_data
+        ])
+        
+        st.dataframe(
+            hourly_df,
+            hide_index=True,
+            use_container_width=True
+        )
+
+def main():
+    """Main function to run the Streamlit app"""
+    st.markdown("<h1 class='main-header'>🌤️ Weather Forecast Dashboard</h1>", unsafe_allow_html=True)
+
+    # Sidebar inputs
+    with st.sidebar:
+        st.header("Settings")
+        location = st.text_input("Enter Location", "London")
+        
+        # Date selection
+        view_type = st.radio("Select View", ["Current & Forecast", "Historical"])
+        
+        if view_type == "Historical":
+            max_date = datetime.now() - timedelta(days=1)
+            min_date = max_date - timedelta(days=7)
+            selected_date = st.date_input(
+                "Select Date",
+                value=max_date,
+                min_value=min_date,
+                max_value=max_date
+            )
+            selected_time = st.time_input("Select Time", datetime.now().time())
+            selected_datetime = datetime.combine(selected_date, selected_time)
+            is_historical = True
+        else:
+            selected_datetime = datetime.now()
+            is_historical = False
+
+        if st.button("Get Weather Data"):
+            with st.spinner("Fetching weather data..."):
+                weather_data = get_weather_data(
+                    location,
+                    is_historical=is_historical,
+                    date=selected_datetime if is_historical else None
+                )
+                
+                if weather_data:
+                    st.session_state.weather_data = weather_data
+                    st.session_state.selected_datetime = selected_datetime
+
+    # Main content area
+    if 'weather_data' in st.session_state:
+        display_weather_data(
+            st.session_state.weather_data,
+            st.session_state.selected_datetime,
+            st
+        )
+    else:
+        st.info("Enter a location and click 'Get Weather Data' to start")
+
+if __name__ == "__main__":
+    main()