app.py

import requests
import json
from datetime import datetime, timedelta
import gradio as gr
import pandas as pd
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"

    def get_fireballs(self, limit=10, date_min=None, energy_min=None):
        try:
            params = {'limit': limit}
            if date_min:
                params['date-min'] = date_min
            if energy_min:
                params['energy-min'] = energy_min

            response = requests.get(self.fireball_url, params=params)
            response.raise_for_status()
            return response.json()
        except Exception as e:
            print("Fireball API Error:", e)
            traceback.print_exc()
            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):
        try:
            params = {'limit': limit, 'dist-max': dist_max, 'date-min': date_min,
                      'date-max': date_max, 'h-min': h_min, 'h-max': h_max,
                      'v-inf-min': v_inf_min, 'v-inf-max': v_inf_max, 'sort': 'date'}
            params = {k: v for k, v in params.items() if v is not None}

            response = requests.get(self.ca_url, params=params)
            response.raise_for_status()
            return response.json()
        except Exception as e:
            print("Close Approaches API Error:", e)
            traceback.print_exc()
            return None

    def format_response(self, data, format_type):
        try:
            if not data:
                return None

            fields = data.get('fields')
            rows = data.get('data')

            if not fields or not rows:
                return None

            df = pd.DataFrame([dict(zip(fields, row)) for row in rows])

            if format_type == 'fireballs':
                return df.rename(columns={
                    'date': 'Date/Time', 'energy': 'Energy (kt)',
                    'impact-e': 'Impact Energy (10^10 J)', 'lat': 'Latitude',
                    'lon': 'Longitude', 'alt': 'Altitude (km)',
                    'vel': 'Velocity (km/s)'
                })

            elif format_type == 'close_approaches':
                return df.rename(columns={
                    'des': 'Object', 'orbit_id': 'Orbit ID', 'cd': 'Time (TDB)',
                    'dist': 'Nominal Distance (au)', 'dist_min': 'Minimum Distance (au)',
                    'dist_max': 'Maximum Distance (au)', 'v_rel': 'Velocity (km/s)',
                    'h': 'H (mag)'
                })

            return df
        except Exception as e:
            print("Data formatting error:", e)
            traceback.print_exc()
            return None



# Gradio Interface Functions

def fetch_fireballs(limit, date_min, energy_min):
    api = NasaSsdCneosApi()
    
    # Convert empty strings to None
    date_min = date_min if date_min else None
    energy_min = float(energy_min) if energy_min else None
    
    data = api.get_fireballs(
        limit=int(limit),
        date_min=date_min,
        energy_min=energy_min
    )
    
    df = api.format_response(data, 'fireballs')
    if df is None or df.empty:
        return "No data available", None
    
    # Create world map of fireballs
    if 'Latitude' in df.columns and 'Longitude' in df.columns:
        fig = px.scatter_geo(df, 
                           lat='Latitude', 
                           lon='Longitude',
                           size='Energy (kt)',
                           hover_name='Date/Time',
                           projection='natural earth',
                           title='Fireball Events')
        
        return df, fig
    
    return df, None

def fetch_close_approaches(limit, dist_max, date_min, date_max, h_min, h_max, v_inf_min, v_inf_max):
    api = NasaSsdCneosApi()
    
    # Convert empty strings to None
    dist_max = float(dist_max) if dist_max else None
    date_min = date_min if date_min else None
    date_max = date_max if date_max else None
    h_min = float(h_min) if h_min else None
    h_max = float(h_max) if h_max else None
    v_inf_min = float(v_inf_min) if v_inf_min else None
    v_inf_max = float(v_inf_max) if v_inf_max else None
    
    data = api.get_close_approaches(
        limit=int(limit),
        dist_max=dist_max,
        date_min=date_min,
        date_max=date_max,
        h_min=h_min,
        h_max=h_max,
        v_inf_min=v_inf_min,
        v_inf_max=v_inf_max
    )
    
    df = api.format_response(data, 'close_approaches')
    if df is None or df.empty:
        return "No data available", None
    
    # Create scatter plot of distance vs velocity
    fig = px.scatter(df, 
                   x='Nominal Distance (au)', 
                   y='Velocity (km/s)',
                   hover_name='Object',
                   size='H (mag)',
                   color='H (mag)',
                   title='Close Approaches - Distance vs Velocity')
    
    return df, fig

def fetch_nea_data(des, spk_id, h_max):
    api = NasaSsdCneosApi()
    
    # Convert empty strings to None
    des = des if des else None
    spk_id = spk_id if spk_id else None
    h_max = float(h_max) if h_max else None
    
    data = api.get_nea_data(
        des=des,
        spk_id=spk_id,
        h_max=h_max
    )
    
    df = api.format_response(data, 'nea')
    if df is None or df.empty:
        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:
        fig = px.scatter(df, 
                       x='Perihelion (au)', 
                       y='Aphelion (au)',
                       hover_name='Designation',
                       color='Inclination (deg)',
                       size='Diameter (km)',
                       title='NEA Orbital Parameters')
        
        return df, fig
    
    return df, None

def fetch_scout_data(limit, nea_comet):
    api = NasaSsdCneosApi()
    
    data = api.get_scout_data(
        limit=int(limit),
        nea_comet=nea_comet
    )
    
    df = api.format_response(data, 'scout')
    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:
        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')
        
        return df, fig
    
    return df, None

# Create Gradio interface
with gr.Blocks(title="NASA SSD/CNEOS API Explorer") as demo:
    gr.Markdown("# NASA SSD/CNEOS API Explorer")
    gr.Markdown("Access data from NASA's Center for Near Earth Object Studies")
    
    with gr.Tab("Fireballs"):
        gr.Markdown("### Fireball Events")
        gr.Markdown("Get information about recent fireball events detected by sensors.")
        with gr.Row():
            with gr.Column():
                fireball_limit = gr.Slider(minimum=1, maximum=100, value=10, step=1, label="Limit")
                fireball_date = gr.Textbox(label="Minimum Date (YYYY-MM-DD)", placeholder="e.g. 2023-01-01")
                fireball_energy = gr.Textbox(label="Minimum Energy (kt)", placeholder="e.g. 0.5")
                fireball_submit = gr.Button("Fetch Fireballs")
            with gr.Column():
                fireball_results = gr.DataFrame(label="Fireball Results")
                fireball_map = gr.Plot(label="Fireball Map")
        
        fireball_submit.click(fetch_fireballs, inputs=[fireball_limit, fireball_date, fireball_energy], outputs=[fireball_results, fireball_map])
    
    with gr.Tab("Close Approaches"):
        gr.Markdown("### Close Approaches")
        gr.Markdown("Get information about close approaches of near-Earth objects.")
        with gr.Row():
            with gr.Column():
                ca_limit = gr.Slider(minimum=1, maximum=100, value=10, step=1, label="Limit")
                ca_dist_max = gr.Textbox(label="Maximum Distance (AU)", placeholder="e.g. 0.05")
                ca_date_min = gr.Textbox(label="Minimum Date (YYYY-MM-DD)", placeholder="e.g. 2023-01-01")
                ca_date_max = gr.Textbox(label="Maximum Date (YYYY-MM-DD)", placeholder="e.g. 2023-12-31")
                ca_h_min = gr.Textbox(label="Minimum H (mag)", placeholder="e.g. 20")
                ca_h_max = gr.Textbox(label="Maximum H (mag)", placeholder="e.g. 30")
                ca_v_min = gr.Textbox(label="Minimum Velocity (km/s)", placeholder="e.g. 10")
                ca_v_max = gr.Textbox(label="Maximum Velocity (km/s)", placeholder="e.g. 30")
                ca_submit = gr.Button("Fetch Close Approaches")
            with gr.Column():
                ca_results = gr.DataFrame(label="Close Approach Results")
                ca_plot = gr.Plot(label="Close Approach Plot")
        
        ca_submit.click(fetch_close_approaches, 
                      inputs=[ca_limit, ca_dist_max, ca_date_min, ca_date_max, ca_h_min, ca_h_max, ca_v_min, ca_v_max], 
                      outputs=[ca_results, ca_plot])
    
    with gr.Tab("NEA Data"):
        gr.Markdown("### Near-Earth Asteroid Data")
        gr.Markdown("Get information about specific near-Earth asteroids.")
        with gr.Row():
            with gr.Column():
                nea_des = gr.Textbox(label="Designation", placeholder="e.g. 2020 SW")
                nea_spk = gr.Textbox(label="SPK-ID", placeholder="e.g. 54101815")
                nea_h_max = gr.Textbox(label="Maximum H (mag)", placeholder="e.g. 25")
                nea_submit = gr.Button("Fetch NEA Data")
            with gr.Column():
                nea_results = gr.DataFrame(label="NEA Results")
                nea_plot = gr.Plot(label="NEA Orbital Parameters")
        
        nea_submit.click(fetch_nea_data, inputs=[nea_des, nea_spk, nea_h_max], outputs=[nea_results, nea_plot])
    
    with gr.Tab("Scout Data"):
        gr.Markdown("### Scout System Data")
        gr.Markdown("Get information about newly discovered objects from NASA's Scout system.")
        with gr.Row():
            with gr.Column():
                scout_limit = gr.Slider(minimum=1, maximum=100, value=10, step=1, label="Limit")
                scout_type = gr.Radio(["NEA", "comet"], label="Object Type", value="NEA")
                scout_submit = gr.Button("Fetch Scout Data")
            with gr.Column():
                scout_results = gr.DataFrame(label="Scout Results")
                scout_plot = gr.Plot(label="Scout Objects Plot")
        
        scout_submit.click(fetch_scout_data, inputs=[scout_limit, scout_type], outputs=[scout_results, scout_plot])
    
    gr.Markdown("### About")
    gr.Markdown("""
    This application provides access to NASA's Solar System Dynamics (SSD) and Center for Near Earth Object Studies (CNEOS) API.
    
    Data is retrieved in real-time from NASA's servers. All data is courtesy of NASA/JPL-Caltech.
    
    Created by [Your Name] using Gradio and Hugging Face Spaces.
    """)

# Create requirements.txt file
requirements = """
gradio>=3.50.0
pandas>=1.5.0
plotly>=5.14.0
requests>=2.28.0
"""

with open("requirements.txt", "w") as f:
    f.write(requirements)

if __name__ == "__main__":
    demo.launch()