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# Import from 3rd party libraries
import streamlit as st
import streamlit.components.v1 as components
# import streamlit_analytics
import pandas as pd
import numpy as np
import re
from sklearn.metrics.pairwise import cosine_similarity
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
nltk.download("stopwords")
nltk.download('wordnet')
from sentence_transformers import SentenceTransformer
import plotly.express as px
import pandas as pd
from sklearn.decomposition import PCA
import time
st.set_page_config(page_title="Mental disorder by description", page_icon="🤖")
def convert_string_to_numpy_array(s):
'''Function to convert a string to a NumPy array'''
numbers_list = re.findall(r'-?\d+\.\d+', s)
return np.array(numbers_list, dtype=np.float64)
#load the model
@st.cache_resource
def get_models():
st.write('*Loading the model...*')
name = "stsb-bert-large"
model = SentenceTransformer(name)
st.write("*The app is loaded and ready to use!*")
lemmatizer = WordNetLemmatizer()
return model, lemmatizer
model, lemmatizer = get_models()
stop_words = set(stopwords.words('english'))
#load the dataframe with disorder embeddings
@st.cache_data # 👈 Add the caching decorator
def load_data():
df_icd = pd.read_csv('icd_embedded.csv')
df_icd['numpy_array'] = df_icd['Embeddings'].apply(convert_string_to_numpy_array)
icd_embeddings = np.array(df_icd["numpy_array"].tolist())
return df_icd, icd_embeddings
df_icd, icd_embeddings = load_data()
#create a list of disease names
@st.cache_data # 👈 Add the caching decorator
def create_disease_list():
disease_names = []
for name in df_icd["Disease"]:
disease_names.append(name)
return disease_names
disease_names = create_disease_list()
if 'descriptions' not in st.session_state:
st.session_state.descriptions = []
def similarity_top(descr_emb, disorder_embs):
# reshaping the character_embedding to match the shape of mental_disorder_embeddings
descr_emb = descr_emb.reshape(1, -1)
# calculating the cosine similarity
similarity_scores = cosine_similarity(disorder_embs, descr_emb)
scores_names = []
for score, name in zip(similarity_scores, disease_names):
data = {"disease_name": name, "similarity_score": score}
scores_names.append(data)
scores_names = sorted(scores_names, key=lambda x: x['similarity_score'], reverse=True)
results = []
for item in scores_names:
disease_name = item['disease_name']
similarity_score = item['similarity_score'][0]
results.append((disease_name, similarity_score))
return results[:5]
def vis_results_2d(input_embed):
# performing dimensionality reduction using PCA
pca = PCA(n_components=2)
disease_embeddings_2d = pca.fit_transform(icd_embeddings)
# creating a DataFrame for disease embeddings plot
disease_data_df = pd.DataFrame(disease_embeddings_2d, columns=['PC1', 'PC2'])
disease_data_df['Type'] = 'Disease'
disease_data_df['Name'] = disease_names
input_embed_2d = input_embed.reshape(1, -1)
input_embed_2d = pca.transform(input_embed_2d)
# creating a DataFrame for character embedding plot
pca_2d = pd.DataFrame(input_embed_2d, columns=['PC1', 'PC2'])
pca_2d['Type'] = 'Character'
pca_2d['Your character'] = 'Your character'
# concatenating the two DataFrames
combined_2d = pd.concat([disease_data_df, pca_2d], ignore_index=True)
# creating an interactive 3D scatter plot
fig = px.scatter(combined_2d, x='PC1', y='PC2', text='Name', color='Type', symbol='Type', width=800, height=800)
fig.show()
def vis_results_3d(input_embed):
# performing dimensionality reduction using PCA
pca = PCA(n_components=3)
disease_embeddings_3d = pca.fit_transform(icd_embeddings)
# creating a DataFrame for disease embeddings plot
disease_data_df = pd.DataFrame(disease_embeddings_3d, columns=['PC1', 'PC2', 'PC3'])
disease_data_df['Type'] = 'Disease'
disease_data_df['Name'] = disease_names
input_embed_2d = input_embed.reshape(1, -1)
input_embed_3d = pca.transform(input_embed_2d)
# creating a DataFrame for character embedding plot
pca_3d = pd.DataFrame(input_embed_3d, columns=['PC1', 'PC2', 'PC3'])
pca_3d['Type'] = 'Character'
pca_3d['Your character'] = 'Your character'
# concatenating the two DataFrames
combined_3d = pd.concat([disease_data_df, pca_3d], ignore_index=True)
# creating an interactive 3D scatter plot
fig = px.scatter_3d(combined_3d, x='PC1', y='PC2', z='PC3', text='Name', color='Type', symbol='Type', width=800, height=800)
fig.show()
# Configure Streamlit page and state
st.title("Detect your character's mental disorder!")
st.markdown(
"This mini-app predicts top-5 most likely mental disorder based on your description. The more information you provide, the more informative the results will be."
)
st.caption("Note that this app can't be used for diagnostic purposes.")
input = st.text_input(label="Your description", placeholder="Insert a description of your character")
if input:
input_embed = model.encode(input)
sim_score = similarity_top(input_embed, icd_embeddings)
i = 1
for dis, value in sim_score:
st.write(f":green[Prediction number] {i}:")
st.write(f"{dis} (similarity score:", value, ")")
i+= 1
text_spinner_placeholder = st.empty()
with st.spinner("Please wait while your visualizations are being generated..."):
time.sleep(5)
vis_results_2d(input_embed)
vis_results_3d(input_embed)
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