Update app.py

app.py

@@ -1,28 +1,19 @@
-import streamlit as st
+# app.py
 import os
-import requests
+from datetime import datetime, timedelta
+
 import pandas as pd
 import plotly.express as px
-from dotenv import load_dotenv
-from datetime import datetime, timedelta
+import streamlit as st
+from fetch import fetch_asteroid_data
 
 # App title and description
-st.title("🌠 NASA Near-Earth Objects Tracker")
+st.title("\U0001F320 NASA Near-Earth Objects Tracker")
 st.markdown("""
 This application uses NASA's NeoWs (Near Earth Object Web Service) API to retrieve and visualize 
 information about asteroids and other near-Earth objects.
 """)
 
-# API Configuration
-NASA_API_URL = "https://api.nasa.gov/neo/rest/v1/feed"
-import os  # Ensure this is at the top
-
-API_KEY = os.getenv("NASA_API_KEY")
-if not API_KEY:
-    st.error("🚨 NASA_API_KEY environment variable is not set.")
-    st.stop()
-
-
 # Date selection
 st.sidebar.header("Search Parameters")
 today = datetime.now()
@@ -35,52 +26,33 @@ end_date = st.sidebar.date_input("End Date", default_end_date)
 # Validate date range
 date_diff = (end_date - start_date).days
 if date_diff > 7:
-    st.warning("⚠️ NASA API limits date range to 7 days or less. Adjusting to a 7-day period.")
+    st.warning("\u26a0\ufe0f NASA API limits date range to 7 days or less. Adjusting to a 7-day period.")
     end_date = start_date + timedelta(days=7)
 
-# Function to fetch data from NASA API
-def fetch_asteroid_data(start_date, end_date, api_key):
-    params = {
-        "start_date": start_date.strftime("%Y-%m-%d"),
-        "end_date": end_date.strftime("%Y-%m-%d"),
-        "api_key": api_key
-    }
-    
-    with st.spinner("Fetching asteroid data from NASA..."):
-        try:
-            response = requests.get(NASA_API_URL, params=params)
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            return response.json()
-        except requests.exceptions.RequestException as e:
-            st.error(f"Error accessing NASA API: {e}")
-            return None
-
 # Search button
 if st.sidebar.button("Search Asteroids"):
-    # Fetch data
-    data = fetch_asteroid_data(start_date, end_date, API_KEY)
-    
+    data = fetch_asteroid_data(start_date, end_date)
+
     if data:
-        # Store data in session state
         st.session_state.asteroid_data = data
         st.session_state.searched = True
     else:
-        st.error("Failed to fetch asteroid data. Please check your API key and try again.")
+        st.error("Failed to fetch asteroid data. Please check your environment setup.")
 
 # Display results if search was performed
 if 'searched' in st.session_state and st.session_state.searched:
     data = st.session_state.asteroid_data
-    
-    # Extract asteroid count
     element_count = data.get('element_count', 0)
     st.success(f"Found {element_count} near-Earth objects between {start_date} and {end_date}")
-    
-    # Process and organize data
+
     neo_data = data.get('near_earth_objects', {})
-    
     all_asteroids = []
+
     for date, asteroids in neo_data.items():
         for asteroid in asteroids:
+            if not asteroid['close_approach_data']:
+                continue
+
             asteroid_info = {
                 'id': asteroid['id'],
                 'name': asteroid['name'],
@@ -93,53 +65,34 @@ if 'searched' in st.session_state and st.session_state.searched:
                 'relative_velocity_kph': float(asteroid['close_approach_data'][0]['relative_velocity']['kilometers_per_hour'])
             }
             all_asteroids.append(asteroid_info)
-    
-    # Convert to DataFrame for easier manipulation
+
     df = pd.DataFrame(all_asteroids)
-    
-    # Add average diameter column
     df['avg_diameter_km'] = (df['diameter_min_km'] + df['diameter_max_km']) / 2
-    
-    # Display summary statistics
+
     st.header("Summary Statistics")
     col1, col2, col3 = st.columns(3)
-    
     with col1:
         st.metric("Total Asteroids", len(df))
-    
     with col2:
         hazardous_count = df['is_hazardous'].sum()
         st.metric("Potentially Hazardous", f"{hazardous_count} ({hazardous_count/len(df)*100:.1f}%)")
-    
     with col3:
         st.metric("Avg. Size", f"{df['avg_diameter_km'].mean():.2f} km")
-    
-    # Visualizations
+
     st.header("Visualizations")
-    
     viz_tab1, viz_tab2 = st.tabs(["Size Distribution", "Miss Distance"])
-    
     with viz_tab1:
-        # Size distribution chart
         fig1 = px.histogram(
-            df, 
-            x="avg_diameter_km", 
-            color="is_hazardous",
+            df, x="avg_diameter_km", color="is_hazardous",
             title="Size Distribution of Near-Earth Objects",
             labels={"avg_diameter_km": "Average Diameter (km)", "is_hazardous": "Potentially Hazardous"},
             color_discrete_map={True: "red", False: "green"}
         )
         st.plotly_chart(fig1, use_container_width=True)
-    
     with viz_tab2:
-        # Miss distance scatter plot
         fig2 = px.scatter(
-            df, 
-            x="miss_distance_km", 
-            y="avg_diameter_km",
-            color="is_hazardous",
-            size="relative_velocity_kph",
-            hover_name="name",
+            df, x="miss_distance_km", y="avg_diameter_km", color="is_hazardous",
+            size="relative_velocity_kph", hover_name="name",
             title="Miss Distance vs. Size (with velocity)",
             labels={
                 "miss_distance_km": "Miss Distance (km)",
@@ -151,40 +104,26 @@ if 'searched' in st.session_state and st.session_state.searched:
         )
         fig2.update_layout(xaxis_type="log")
         st.plotly_chart(fig2, use_container_width=True)
-    
-    # Detailed asteroid data
+
     st.header("Detailed Asteroid Data")
-    
-    # Filter options
     st.subheader("Filters")
     col1, col2 = st.columns(2)
-    
     with col1:
         show_hazardous = st.checkbox("Show only hazardous asteroids", False)
-    
     with col2:
         size_threshold = st.slider("Minimum size (km)", 0.0, max(df['avg_diameter_km']), 0.0, 0.01)
-    
-    # Apply filters
+
     filtered_df = df.copy()
     if show_hazardous:
         filtered_df = filtered_df[filtered_df['is_hazardous'] == True]
-    
     filtered_df = filtered_df[filtered_df['avg_diameter_km'] >= size_threshold]
-    
-    # Sort options
-    sort_by = st.selectbox(
-        "Sort by", 
-        ["close_approach_date", "name", "avg_diameter_km", "miss_distance_km", "relative_velocity_kph"]
-    )
-    
+
+    sort_by = st.selectbox("Sort by", [
+        "close_approach_date", "name", "avg_diameter_km", "miss_distance_km", "relative_velocity_kph"])
     sort_order = st.radio("Sort order", ["Ascending", "Descending"], horizontal=True)
-    
-    # Apply sorting
     ascending = sort_order == "Ascending"
     filtered_df = filtered_df.sort_values(by=sort_by, ascending=ascending)
-    
-    # Display dataframe with key information
+
     display_df = filtered_df[[
         'name', 'close_approach_date', 'avg_diameter_km', 
         'miss_distance_km', 'relative_velocity_kph', 'is_hazardous'
@@ -196,43 +135,30 @@ if 'searched' in st.session_state and st.session_state.searched:
         'relative_velocity_kph': 'Velocity (km/h)',
         'is_hazardous': 'Hazardous'
     })
-    
     st.dataframe(display_df, use_container_width=True)
-    
-    # Asteroid details expander
+
     st.subheader("Individual Asteroid Details")
-    
-    # Allow user to select an asteroid for detailed view
     selected_asteroid = st.selectbox("Select an asteroid", filtered_df['name'].tolist())
-    
     if selected_asteroid:
         asteroid_details = filtered_df[filtered_df['name'] == selected_asteroid].iloc[0]
-        
-        st.subheader(f"🌑 {selected_asteroid}")
-        
+        st.subheader(f"\U0001F311 {selected_asteroid}")
         col1, col2 = st.columns(2)
-        
         with col1:
             st.write("**ID:**", asteroid_details['id'])
             st.write("**Approach Date:**", asteroid_details['close_approach_date'])
-            st.write("**Hazardous:**", "Yes ⚠️" if asteroid_details['is_hazardous'] else "No ✓")
-        
+            st.write("**Hazardous:**", "Yes \u26a0\ufe0f" if asteroid_details['is_hazardous'] else "No \u2713")
         with col2:
             st.write("**Diameter Range:**", f"{asteroid_details['diameter_min_km']:.3f} - {asteroid_details['diameter_max_km']:.3f} km")
             st.write("**Miss Distance:**", f"{asteroid_details['miss_distance_km']:,.0f} km")
             st.write("**Relative Velocity:**", f"{asteroid_details['relative_velocity_kph']:,.0f} km/h")
-        
-        # Create a gauge-like visualization for the hazard level
+
         hazard_level = 0
         if asteroid_details['is_hazardous']:
-            # Calculate hazard level based on size and miss distance
-            size_factor = min(asteroid_details['avg_diameter_km'] / 0.5, 1)  # Normalize by 0.5km
-            distance_factor = min(1000000 / asteroid_details['miss_distance_km'], 1)  # Normalize by 1M km
+            size_factor = min(asteroid_details['avg_diameter_km'] / 0.5, 1)
+            distance_factor = min(1000000 / asteroid_details['miss_distance_km'], 1)
             hazard_level = (size_factor * 0.7 + distance_factor * 0.3) * 100
-        
         st.progress(int(hazard_level), text=f"Relative Hazard Level: {hazard_level:.1f}%")
-        
-        # Add some context about the asteroid
+
         st.write("### Context")
         if hazard_level > 70:
             st.warning("This asteroid is classified as potentially hazardous and is relatively large and close.")
@@ -241,17 +167,14 @@ if 'searched' in st.session_state and st.session_state.searched:
         else:
             st.success("This asteroid is not considered hazardous and poses no risk to Earth.")
 
-# Add information about the NASA API
+# Sidebar info
 st.sidebar.markdown("---")
 st.sidebar.markdown("""
 ### About NASA NeoWs API
-The [Near Earth Object Web Service](https://api.nasa.gov) is a RESTful web service for near earth asteroid information. 
-This API provides data on asteroids based on their closest approach date to Earth.
-
-To get your own API key, visit [api.nasa.gov](https://api.nasa.gov).
+The [Near Earth Object Web Service](https://api.nasa.gov) provides asteroid data based on closest approach to Earth.
+To get an API key, visit [api.nasa.gov](https://api.nasa.gov).
 """)
 
-# Add deployment instructions
 st.sidebar.markdown("---")
 st.sidebar.markdown("""
 ### Deployment Instructions
@@ -262,6 +185,11 @@ st.sidebar.markdown("""
    requests
    pandas
    plotly
+   python-dotenv
    ```
-3. Upload to Hugging Face Spaces
-""")
+3. Add `.env` file with:
+   ```
+   NASA_API_KEY=your_key_here
+   ```
+4. Deploy to Streamlit Cloud or similar
+""")