app.py

import pandas as pd
import random
import streamlit as st

df = pd.read_csv('profiles.csv')


def match_therapist(new_user_interests, new_user_therapy_goals):
  """
  This function takes a new user's interests and therapy goals and finds a matching therapist from the DataFrame.

  Args:
      Take user input of new user's interests (e.g., ['Anxiety', 'Stress']).
       Take user input of new user's therapy goals (e.g., ['Mindfulness', 'Stress management']).

  Returns:
      DataFrame: A DataFrame containing the matched therapist's information (including User_ID, Name, etc.).
  """

  # Find therapists with matching interests
  matching_interests = df['Interests'].apply(lambda interests: any(interest in new_user_interests for interest in interests.split(',')))
  therapists_by_interests = df[matching_interests]

  # Further filter based on matching therapy goals (consider weights for stronger matches)
  matching_goals = therapists_by_interests['Therapy_Goals'].apply(lambda goals: any(goal in new_user_therapy_goals for goal in goals.split(',')))
  matched_therapist = therapists_by_interests[matching_goals].iloc[0]  # Select first matched therapist (can be enhanced)

  return matched_therapist

# Example usage:
# new_user_interests = input('Enter the interest: ')
# new_user_therapy_goals = input('Enter the therapy: ')

new_user_interests = st.text_area("Enter the interest: ")
new_user_therapy_goals = st.text_area("Enter the therapy: ")

matched_therapist_df = match_therapist(new_user_interests, new_user_therapy_goals)

print(f"Matched user information:\n{matched_therapist_df}")