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