Update app.py

app.py

@@ -7,14 +7,12 @@ import traceback
 import plotly.express as px
 import plotly.graph_objects as go
 
-# Updated NASA API client with proper endpoints and data parsing
-import requests
-import pandas as pd
-
 class NasaSsdCneosApi:
     def __init__(self):
         self.fireball_url = "https://ssd-api.jpl.nasa.gov/fireball.api"
         self.ca_url = "https://ssd-api.jpl.nasa.gov/cad.api"
+        self.nea_url = "https://ssd-api.jpl.nasa.gov/sbdb_query.api"
+        self.scout_url = "https://ssd-api.jpl.nasa.gov/scout.api"
 
     def get_fireballs(self, limit=10, date_min=None, energy_min=None):
         try:
@@ -33,8 +31,8 @@ class NasaSsdCneosApi:
             return None
 
     def get_close_approaches(self, dist_max=None, date_min=None, date_max=None,
-                              h_min=None, h_max=None, v_inf_min=None, v_inf_max=None,
-                              limit=10):
+                             h_min=None, h_max=None, v_inf_min=None, v_inf_max=None,
+                             limit=10):
         try:
             params = {'limit': limit, 'dist-max': dist_max, 'date-min': date_min,
                       'date-max': date_max, 'h-min': h_min, 'h-max': h_max,
@@ -49,6 +47,46 @@ class NasaSsdCneosApi:
             traceback.print_exc()
             return None
 
+    def get_nea_data(self, des=None, spk_id=None, h_max=None):
+        try:
+            # Build query parameter for NEAs - select asteroid with NEA flag
+            query_params = {
+                'sb-nea': 'true'  # Filter for Near-Earth Asteroids
+            }
+            
+            if des:
+                query_params['sb-spk'] = des
+            if spk_id:
+                query_params['sb-spkid'] = spk_id
+            if h_max:
+                query_params['sb-h-max'] = h_max
+                
+            # Add fields to return
+            query_params['fields'] = 'spkid,full_name,pdes,neo,H,G,diameter,extent,albedo,rot_per,GM,BV,UB,IR,spec_B,spec_T,H_sigma,diameter_sigma,orbit_id,epoch,epoch_mjd,epoch_cal,a,e,i,om,w,ma,ad,n,tp,tp_cal,per,per_y,q,moid,moid_ld,moid_jup'
+            query_params['limit'] = 100  # Set a reasonable limit
+
+            response = requests.get(self.nea_url, params=query_params)
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            print("NEA API Error:", e)
+            traceback.print_exc()
+            return None
+
+    def get_scout_data(self, limit=10, nea_comet="NEA"):
+        try:
+            params = {'limit': limit}
+            if nea_comet:
+                params['nea-comet'] = nea_comet.lower()
+
+            response = requests.get(self.scout_url, params=params)
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            print("Scout API Error:", e)
+            traceback.print_exc()
+            return None
+
     def format_response(self, data, format_type):
         try:
             if not data:
@@ -77,6 +115,29 @@ class NasaSsdCneosApi:
                     'dist_max': 'Maximum Distance (au)', 'v_rel': 'Velocity (km/s)',
                     'h': 'H (mag)'
                 })
+            
+            elif format_type == 'nea':
+                name_columns = {
+                    'full_name': 'Full Name', 'pdes': 'Designation',
+                    'H': 'Absolute Magnitude (mag)', 'diameter': 'Diameter (km)',
+                    'q': 'Perihelion (au)', 'ad': 'Aphelion (au)',
+                    'i': 'Inclination (deg)', 'e': 'Eccentricity',
+                    'moid': 'MOID (au)', 'moid_ld': 'MOID (LD)'
+                }
+                # Use only columns that exist in the dataframe
+                valid_columns = {k: v for k, v in name_columns.items() if k in df.columns}
+                return df.rename(columns=valid_columns)
+
+            elif format_type == 'scout':
+                # Handle Scout API response - column names may vary
+                # Adjust these column mappings based on actual response structure
+                if 'score' in df.columns:
+                    df = df.rename(columns={
+                        'object': 'Object', 'score': 'Rating',
+                        'diameter': 'Diameter (m)', 'ca_dist': 'Close Approach',
+                        'nobs': 'Observations'
+                    })
+                return df
 
             return df
         except Exception as e:
@@ -85,7 +146,6 @@ class NasaSsdCneosApi:
             return None
 
 
-
 # Gradio Interface Functions
 
 def fetch_fireballs(limit, date_min, energy_min):
@@ -176,13 +236,13 @@ def fetch_nea_data(des, spk_id, h_max):
         return "No data available", None
     
     # Create a scatter plot of perihelion vs aphelion colored by inclination
-    if not df.empty and 'Perihelion (au)' in df.columns:
+    if not df.empty and 'Perihelion (au)' in df.columns and 'Aphelion (au)' in df.columns:
         fig = px.scatter(df, 
                        x='Perihelion (au)', 
                        y='Aphelion (au)',
-                       hover_name='Designation',
-                       color='Inclination (deg)',
-                       size='Diameter (km)',
+                       hover_name='Designation' if 'Designation' in df.columns else None,
+                       color='Inclination (deg)' if 'Inclination (deg)' in df.columns else None,
+                       size='Diameter (km)' if 'Diameter (km)' in df.columns else None,
                        title='NEA Orbital Parameters')
         
         return df, fig
@@ -201,15 +261,22 @@ def fetch_scout_data(limit, nea_comet):
     if df is None or df.empty:
         return "No data available", None
     
-    # Create a scatter plot of diameter vs close approach distance
-    if not df.empty and 'Diameter (m)' in df.columns:
+    # Create a scatter plot based on available columns
+    if not df.empty:
+        # Use columns that are available in the dataframe
+        x_col = 'Diameter (m)' if 'Diameter (m)' in df.columns else df.columns[0]
+        y_col = 'Close Approach' if 'Close Approach' in df.columns else df.columns[1]
+        hover_col = 'Object' if 'Object' in df.columns else None
+        color_col = 'Rating' if 'Rating' in df.columns else None
+        size_col = 'Observations' if 'Observations' in df.columns else None
+        
         fig = px.scatter(df, 
-                       x='Diameter (m)', 
-                       y='Close Approach',
-                       hover_name='Object',
-                       color='Rating',
-                       size='Observations',
-                       title='Scout Objects - Size vs Close Approach Distance')
+                       x=x_col, 
+                       y=y_col,
+                       hover_name=hover_col,
+                       color=color_col,
+                       size=size_col,
+                       title='Scout Objects')
         
         return df, fig