Delete app.py

app.py

@@ -1,346 +0,0 @@
-import streamlit as st
-import pickle
-import pandas as pd
-from sentence_transformers import SentenceTransformer, util
-import numpy as np
-from pymongo import MongoClient
-import urllib.parse
-import requests
-from bertopic import BERTopic
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-import string
-import deepcut
-import unicodedata
-from pythainlp.util import normalize
-import torch
-import csv
-
-#initial state
-if 'state' not in st.session_state:
-    st.session_state.state = 0
-if 'age' not in st.session_state:
-    st.session_state.age = 0
-if 'weight' not in st.session_state:
-    st.session_state.weight = 0
-if 'height' not in st.session_state:
-    st.session_state.height = 0
-if 'gender' not in st.session_state:
-    st.session_state.gender = 0
-if 'food_allergy' not in st.session_state:
-    st.session_state.food_allergy = 0
-if 'drug_allergy' not in st.session_state:
-    st.session_state.drug_allergy = 0
-if 'congentital_disease' not in st.session_state:
-    st.session_state.congentital_disease = 0
-if 'optional_keyword' not in st.session_state:
-    st.session_state.optional_keyword = 0
-if 'all_recommend' not in st.session_state:
-    st.session_state.all_recommend = None
-if 'true_check' not in st.session_state:
-    st.session_state.true_check = None
-if 'queries' not in st.session_state:
-    st.session_state.queries = None
-if 'string_contain' not in st.session_state:
-    st.session_state.string_contain = False
-if 'sbert_searched_df' not in st.session_state:
-    st.session_state.sbert_searched_df = None
-if 'string_contain_df' not in st.session_state:
-    st.session_state.string_contain_df = None
-for i in range(10):
-    if 'score_'+str(i+1) not in st.session_state:
-        st.session_state['score_'+str(i+1)] = 'NA'
-
-def set_state(state):
-    st.session_state.state = state
-
-def split_text(text):
-    return text.split(',')
-
-#import data
-sbert_model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
-
-with open('corpus_embeddings.pickle', 'rb') as file:
-    corpus_embeddings = pickle.load(file)
-corpus_embeddings = pd.DataFrame(corpus_embeddings)
-
-topic_model = BERTopic.load("Jiranuwat/topic_model")
-data = pd.read_csv('articles_data.csv')
-data['child_topic'] = topic_model.topics_[:]
-
-with open('sensitive_words.txt', 'r',encoding='utf-8') as file:
-    sensitive_words = file.read()
-sensitive_words = sensitive_words.lower().replace('\n','').split(' ')
-sensitive_words = list(set(sensitive_words))
-
-#local function
-def save_session_state_data(session_state_data, filename):
-            with open(filename, 'a', newline='', encoding='utf-8') as file:
-                writer = csv.DictWriter(file, fieldnames=session_state_data.keys())
-                if file.tell() == 0:
-                    writer.writeheader()
-                writer.writerow(session_state_data)
-
-def deepcut_tokenizer(text,sensitive_words=sensitive_words):
-    cleanedText = "".join([i for i in text if i not in string.punctuation]).lower()
-    cleanedText = normalize(unicodedata.normalize('NFKD', cleanedText).replace('\n','').replace('\r','').replace('\t','').replace('“','').replace('”','').replace('.','').replace('–','').replace('‘','').replace('’','').replace('ํา','ำ').replace('...','').replace(',','').replace( 'ี','ี'))
-    #cleanedText = re.sub(r'\d+', '', cleanedText)
-    cleanedText = deepcut.tokenize(cleanedText,custom_dict=sensitive_words)
-    #stopwords = list(thai_stopwords())+'EMagazine GJ international bangkok hospital'.lower().split(' ')
-    stopwords = 'EMagazine GJ international bangkok hospital'.lower().split(' ')
-    cleanedText = [i for i in cleanedText if i not in stopwords]
-    cleanedText = [i.replace(' ','') for i in cleanedText if len(i) != 1 and len(i) !=0]
-    cleanedText = ','.join(cleanedText)
-    return cleanedText
-
-def personal_check(age,weight,height,gender):
-
-    #age check
-    if age >= 60:
-        age = 'ผู้สูงอายุ'
-    else:
-        age = 'ทำงาน'
-
-    #gender check
-    if gender == 'หญิง':
-        gender = 'ผู้หญิง'
-    else:
-        gender = 'ผู้ชาย'
-
-    #bmi check
-    height_meters = height / 100  
-
-    bmi = weight / (height_meters ** 2)
-
-    if bmi >= 30:
-        bmi = 'อ้วนมาก'
-    elif bmi >= 23 and bmi <30:
-        bmi = 'อ้วน'
-    elif bmi >= 18.5 and bmi <23:
-        bmi = ''
-    else:
-        bmi = 'ผอม'
-    
-    return age,gender,bmi
-
-def sbert_search(queries,data,embeddiing,sbert_model=sbert_model):
-
-    index_lst = []
-    score_lst = []
-
-    query_embedding = sbert_model.encode(queries, convert_to_tensor=True)
-    hits = util.semantic_search(query_embedding, embeddiing, top_k=15)
-    hits = hits[0]
-    for hit in hits:
-        index_lst.append(hit['corpus_id'])
-        score_lst.append(hit['score'])
-
-    sbert_searched = data.iloc[index_lst]
-    sbert_searched['score'] = score_lst
-
-    return sbert_searched
-
-def sbert_tfidf_search(queries,head,topic_model=topic_model,data=data,corpus_embeddings=corpus_embeddings):
-    
-    similar_df = None
-    text_to_predict_token = deepcut_tokenizer(queries)
-
-    # Find topics
-    try:
-        similar_topics, similarity = topic_model.find_topics(text_to_predict_token, top_n=1)
-    except:
-        similar_topics, similarity = topic_model.find_topics(queries, top_n=1)
-
-    # Example DataFrame
-    similar_df = data[data['child_topic'] == similar_topics[0]]
-
-    # TF-IDF vectorizer
-    vectorizer = TfidfVectorizer(tokenizer=lambda x: x, lowercase=False)
-    tfidf_matrix = vectorizer.fit_transform(similar_df['text_token'])
-
-    # TF-IDF vector for input text
-    text_tfidf = vectorizer.transform([text_to_predict_token])
-
-    # Compute cosine similarity
-    similarity_scores = cosine_similarity(tfidf_matrix, text_tfidf)
-
-    # Add similarity scores to DataFrame
-    similar_df['score'] = similarity_scores
-
-    similar_df = similar_df.sort_values('score', ascending=False).head(15)
-
-    select_corpus = corpus_embeddings.iloc[similar_df.index.sort_values()]
-    similar_embedding = torch.tensor(select_corpus.values)
-    similar_searched = sbert_search(queries,similar_df,similar_embedding)
-    sbert_searched = sbert_search(queries,data,torch.tensor(corpus_embeddings.values))
-    combined_searched = pd.concat([similar_searched,sbert_searched])
-    output = combined_searched.sort_values('score', ascending=False).head(head)
-
-    return output
-
-def string_contain_search(queries,sample,data=data):
-    data['all_content'] = data['title']+data['content']
-    return data[data['all_content'].str.contains(queries,na=False)].sample(sample)
-
-#main
-def main():
-    #header
-    st.markdown("<h1 style='text-align: center; color: black;'>---ระบบแนะนำบทความสุขภาพ---</h1>", unsafe_allow_html=True)
-    st.subheader("ให้คะแนนบทความหน่อยนะครับ😄")
-
-    with st.form('user_info'):
-
-        #personal information input
-        age = st.slider("อายุ", 10, 100, 25)
-
-        col1, col2 = st.columns(2)
-        with col1:
-            weight = st.number_input("น้ำหนัก (Kg.): ",30.0,120.0,step=1.0,value=50.0)
-        with col2:
-            height = st.number_input("ส่วนสูง (cm.): ",100.0,250.0,step=1.0,value=150.0)
-        
-        col3, col4, col5 = st.columns(3)
-        with col3:
-            gender = st.selectbox('เพศ',('ชาย', 'หญิง'))
-        with col4:
-            food_allergy = st.selectbox('แพ้อาหาร?',('ไม่แพ้', 'แพ้อาหาร'))
-        with col5:
-            drug_allergy = st.selectbox('แพ้ยา?',('ไม่แพ้', 'แพ้ยา'))
-        congentital_disease = st.text_input('โรคประจำตัวของคุณ (ถ้าหากไม่มี ไม่ต้องกรอก หรือใส่ "ไม่มี")')
-        optional_keyword = st.text_input('คำค้นหาเพิ่มเติม (ถ้ามี)')
-
-        st.form_submit_button(on_click=set_state,args=(1,))
-
-    if st.session_state.state == 1:
-
-        #asign state
-        st.session_state.age = age
-        st.session_state.weight = weight
-        st.session_state.height = height
-        st.session_state.gender = gender
-        st.session_state.food_allergy = food_allergy
-        st.session_state.drug_allergy = drug_allergy
-        st.session_state.congentital_disease = congentital_disease
-        st.session_state.optional_keyword = optional_keyword
-
-        #algorithm
-        age,gender,bmi = personal_check(age,weight,height,gender)
-
-        if food_allergy == 'ไม่แพ้':
-            food_allergy = ''
-        if drug_allergy == 'ไม่แพ้':
-            drug_allergy = ''
-        if congentital_disease == 'ไม่มี':
-            congentital_disease = ''
-        if congentital_disease != '' or optional_keyword != '':
-            queries = optional_keyword+congentital_disease
-        else:
-            queries = gender+age+bmi+food_allergy+drug_allergy+congentital_disease+optional_keyword
-        
-        #Bertopic search
-        try:
-            sbert_searched = sbert_tfidf_search(queries,5)
-            string_contain = string_contain_search(queries,5)
-            all_recommend = pd.concat([sbert_searched,string_contain])
-            all_recommend = all_recommend.drop_duplicates(subset=['url'])
-
-            if len(all_recommend) != 10:
-                for i in range(3):
-                    if len(all_recommend) < 10:
-                        all_recommend = None
-                        sbert_searched = sbert_tfidf_search(queries,5+i+1)
-                        sbert_searched = sbert_searched.head(5)
-                        string_contain = string_contain_search(queries,5+i+1)
-                        string_contain = string_contain.head(5)
-                        all_recommend = pd.concat([sbert_searched,string_contain])
-                        all_recommend = all_recommend.drop_duplicates(subset=['url'])
-
-            st.session_state.sbert_searched_df = sbert_searched
-            st.session_state.string_contain_df = string_contain
-            st.session_state.string_contain = True
-
-        except:
-            sbert_searched = sbert_tfidf_search(queries,10)
-            st.session_state.sbert_searched_df = sbert_searched
-            all_recommend = sbert_searched
-
-        st.session_state.all_recommend = all_recommend
-        st.session_state.queries = queries
-        st.session_state.state = 2
-
-    if st.session_state.state == 2:
-        placeholder = st.empty()
-
-        #satisfaction
-        with placeholder.form('Satisfaction Survey'):
-            st.markdown("<h1 style='text-align: center; color: black;'>📰บทความสำหรับคุณ😆</h1>", unsafe_allow_html=True)
-            st.header("ระดับความเกี่ยวข้อง")
-            st.write("😞 หมายถึง ไม่เกี่ยวข้องเลย")
-            st.write("🙁 หมายถึง เกี่ยวข้องเล็กน้อย")
-            st.write("😐 หมายถึง เฉยๆ")
-            st.write("🙂 หมายถึง ค่อนข้างเกี่ยวข้อง")
-            st.write("😀 หมายถึง เกี่ยวข้องมากที่สุด")
-            st.write("---------------------------------------------------------------------------------------")
-
-            for i in range(len(st.session_state.all_recommend)):
-                st.header(str(i+1)+'. '+st.session_state.all_recommend.iloc[i]['title'])
-                st.markdown(f"[Page source (Click here.)]({st.session_state.all_recommend.iloc[i].url})")
-
-                try:
-                    banner_url = urllib.parse.quote(st.session_state.all_recommend.iloc[i]['banner'], safe=':/')
-                    response = requests.get(banner_url)
-                    st.image(response.content)
-                except:
-                    st.image('https://icon-library.com/images/no-photo-icon/no-photo-icon-1.jpg')
-
-                #satisfaction survey
-                st.subheader("Satisfaction Survey")
-                st.write("บทความที่แนะนำเกี่ยวข้องกับคุณมากเพียงใด")
-                st.radio('ระดับความพึงพอใจ',['NA','😞','🙁','😐','🙂','😀'],horizontal=True,key='score_'+str(i+1))
-                st.write("---------------------------------------------------------------------------------------")
-
-            if st.form_submit_button("ยืนยันการส่งคำตอบ"):
-                # Check if all articles have satisfaction levels selected
-                st.session_state.true_check = []
-                for satis_val in [st.session_state[i] for i in ['score_' + str(i+1) for i in range(10)]]:
-                    if satis_val != 'NA': 
-                        st.session_state.true_check.append(True)
-                    else:
-                        st.session_state.true_check.append(False)
-
-                if np.all(st.session_state.true_check):
-                    st.session_state.state = 3
-                    placeholder.empty()
-                
-                else:
-                    idx = []
-                    for i in range(len(st.session_state.true_check)):
-                        if st.session_state.true_check[i] == False:
-                            idx.append(i+1)
-                    article_indexes = ', '.join(map(str, idx))
-                    st.warning(f":red[กรุณาให้คะแนนบทความที่ {article_indexes} ด้วยครับ]")
-
-    if st.session_state.state == 3:
-        st.success('บันทึกคำตอบแล้ว')
-
-        st.session_state.all_recommend = st.session_state.all_recommend.to_dict(orient='records')
-        if st.session_state.sbert_searched_df is not None:
-            st.session_state.sbert_searched_df = st.session_state.sbert_searched_df.to_dict(orient='records')
-        if st.session_state.string_contain_df is not None:
-            st.session_state.string_contain_df = st.session_state.string_contain_df.to_dict(orient='records')
-
-        try:
-            save_session_state_data(st.session_state.to_dict(), 'satisfaction.csv')
-
-        except:
-            #database insertion
-            client = MongoClient('mongodb://192.168.1.103:27017/')
-            database = client['test']
-            collection = database['satisfy_articles']
-            collection.insert_one(st.session_state.to_dict())
-
-        finally:
-            st.session_state.state = 0
-
-if __name__ == "__main__":
-    main()