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app.py

@@ -0,0 +1,118 @@
+# 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
+
+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]
+
+
+# with text_spinner_placeholder:
+#     with st.spinner("Please wait while your Tweet is being generated..."):
+#         mood_prompt = f"{mood} " if mood else ""
+#         if style:
+#             twitter = twe.Tweets(account=style)
+#             tweets = twitter.fetch_tweets()
+#             tweets_prompt = "\n\n".join(tweets)
+#             prompt = (
+#                 f"Write a {mood_prompt}Tweet about {topic} in less than 120 characters "
+#                 f"and in the style of the following Tweets:\n\n{tweets_prompt}\n\n"
+                    
+# Configure Streamlit page and state
+st.title("Detect the disorder")
+st.markdown(
+    "This mini-app predicts a mental disorder based on your description."
+)
+
+input = st.text_input(label="Your description)", placeholder="Insert a description of a character")
+if input:
+    input_embed = model.encode(input)
+    sim_score = similarity_top(input_embed, icd_embeddings)
+    st.write(sim_score)
+    
+# mood = st.text_input(
+#     label="Mood (e.g. inspirational, funny, serious) (optional)",
+#     placeholder="inspirational",
+# )
+# style = st.text_input(
+#     label="Twitter account handle to style-copy recent Tweets (optional, limited by Twitter's API)",
+#     placeholder="elonmusk",
+# )
+
+text_spinner_placeholder = st.empty()

apps.py

@@ -1,36 +0,0 @@
-# Import from 3rd party libraries
-import streamlit as st
-import streamlit.components.v1 as components
-# import streamlit_analytics
-
-
-# with text_spinner_placeholder:
-#     with st.spinner("Please wait while your Tweet is being generated..."):
-#         mood_prompt = f"{mood} " if mood else ""
-#         if style:
-#             twitter = twe.Tweets(account=style)
-#             tweets = twitter.fetch_tweets()
-#             tweets_prompt = "\n\n".join(tweets)
-#             prompt = (
-#                 f"Write a {mood_prompt}Tweet about {topic} in less than 120 characters "
-#                 f"and in the style of the following Tweets:\n\n{tweets_prompt}\n\n"
-                    
-# Configure Streamlit page and state
-st.set_page_config(page_title="Tweet", page_icon="🤖")
-
-st.title("Generate Tweets")
-st.markdown(
-    "This mini-app predicts a mental disorder based on your description."
-)
-
-topic = st.text_input(label="Topic (or hashtag)", placeholder="AI")
-mood = st.text_input(
-    label="Mood (e.g. inspirational, funny, serious) (optional)",
-    placeholder="inspirational",
-)
-style = st.text_input(
-    label="Twitter account handle to style-copy recent Tweets (optional, limited by Twitter's API)",
-    placeholder="elonmusk",
-)
-
-text_spinner_placeholder = st.empty()