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import pyvo as vo
import pandas as pd
def fetch_exoplanet_data():
# Connect to NASA Exoplanet Archive TAP Service
tap_service = vo.dal.TAPService("https://exoplanetarchive.ipac.caltech.edu/TAP")
# Query to fetch all columns from the pscomppars table
ex_query = """
SELECT TOP 10 pl_name, hostname, sy_snum, sy_pnum, discoverymethod, disc_year, disc_facility, pl_controv_flag, pl_orbper, pl_orbsmax, pl_rade, pl_bmasse, pl_orbeccen, pl_eqt, st_spectype, st_teff, st_rad, st_mass, ra, dec, sy_vmag
FROM pscomppars
"""
# Execute the query
qresult = tap_service.search(ex_query)
# Convert to a Pandas DataFrame
ptable = qresult.to_table()
exoplanet_data = ptable.to_pandas()
return exoplanet_data
def generate_data_insights(user_input, client, exoplanet_data, max_tokens=500, temperature=0.3):
"""
Generate insights by passing the user's input along with the exoplanet data to GPT-4.
"""
# Convert the dataframe to a readable format for GPT (e.g., CSV-style text)
data_as_text = exoplanet_data.to_csv(index=False) # CSV-style for better readability
# Create a prompt with the user query and the data sample
insights_prompt = (
f"Analyze the following user query and provide relevant insights based on the provided exoplanet data.\n\n"
f"User Query: {user_input}\n\n"
f"Exoplanet Data:\n{data_as_text}\n\n"
f"Please provide insights that are relevant to the user's query."
)
# Call GPT-4 to generate insights based on the data and user input
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are an expert in analyzing astronomical data and generating insights."},
{"role": "user", "content": insights_prompt}
],
max_tokens=max_tokens,
temperature=temperature
)
# Extract and return GPT-4's insights
insights_from_data = response.choices[0].message.content.strip()
return insights_from_data |