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app.py

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+import streamlit as st
+import pickle
+from pymongo import MongoClient
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+import requests
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+from io import BytesIO
+import urllib.parse
+import math
+
+sbert_model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
+
+try:
+    client = MongoClient('mongodb://192.168.1.103:27017/')
+
+    print("---Connenction Successful---")
+
+    Recommendation_elderly = client['Recommendation_elderly']
+
+    healthcare_articles = Recommendation_elderly['token']
+
+except:
+    raise KeyError('Connection Fail')
+
+data = healthcare_articles.find()
+data = pd.DataFrame(list(data))
+data = data.drop_duplicates(subset=['url'])
+data = data[data['title'] != '']
+data = data.reset_index().drop(columns=['index'])
+data = data.reset_index().drop(columns=['_id','index'])
+
+with open('corpus_embeddings.pickle', 'rb') as file:
+    corpus_embeddings = pickle.load(file)
+
+def personal_check(age,weight,height,gender):
+
+    #age check
+    if age >= 60:
+        age = 'ผู้สูงอายุ'
+    else:
+        age = 'วัยทำงาน'
+
+    #gender check
+    if gender == 'Female':
+        gender = 'ผู้หญิง สตรี'
+    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):
+
+    global sbert_model,corpus_embeddings,data
+
+    index_lst = []
+    score_lst = []
+
+    for query in queries:
+        query_embedding = sbert_model.encode(query, convert_to_tensor=True)
+        hits = util.semantic_search(query_embedding, corpus_embeddings, 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
+    sbert_searched = sbert_searched[['url','title','score','banner']]
+
+    return sbert_searched
+
+def visualize_articles_images(title,banner):
+    # Calculate the number of rows and columns for the grid
+    num_images = len(banner)
+    num_rows = math.ceil(num_images / 3)
+    num_cols = min(num_images, 3)
+    fp = 'angsana.ttc'
+
+    # Create a grid of subplots
+    fig, axs = plt.subplots(num_rows, num_cols, figsize=(20, 20))
+
+    # Iterate over the image URLs
+    for i, url in enumerate(banner):
+        # Calculate the subplot position
+        row = i // num_cols
+        col = i % num_cols
+        axs[row, col].set_title(title.iloc[i],fontname='Tahoma',fontsize=16)
+
+        if str(url) == 'nan':
+            continue
+
+        else:
+            try:
+                # Encode the URL using UTF-8
+                encoded_url = urllib.parse.quote(url, safe=':/')
+
+                # Download the image
+                response = requests.get(encoded_url)
+                img = mpimg.imread(BytesIO(response.content), format='jpg')
+
+                # Calculate the subplot position
+                row = i // num_cols
+                col = i % num_cols
+
+                # Plot the image
+                axs[row, col].imshow(img)
+                axs[row, col].axis('off')
+            except:
+                continue
+            finally:
+                pass
+
+    return fig
+
+def main():
+    #header
+    st.title("---ระบบแนะนำบทความสุขภาพ---")
+    st.subheader("ให้คะแนนบทความหน่อยนะครับ:smile:")
+
+    #personal information input
+    age = st.slider("อายุ", 0, 100, 25)
+    weight = st.number_input("น้ำหนัก (Kg.): ",30,120,step=1,value=30)
+    height = st.number_input("ส่วนสูง (cm.): ",100,250,step=1,value=120)
+    gender = st.selectbox('เพศ',('ชาย', 'หญิง'))
+    food_allergy = st.selectbox('แพ้อาหาร?',('ไม่แพ้', 'แพ้อาหาร'))
+    drug_allergy = st.selectbox('แพ้ยา?',('ไม่แพ้', 'แพ้ยา'))
+    congentital_disease = st.text_input('โรคประจำตัวของคุณ')
+
+    # Add a button
+    if st.button("Click me"):
+        age,gender,bmi = personal_check(age,weight,height,gender)
+
+        if food_allergy == 'ไม่แพ้':
+            food_allergy = ''
+        if drug_allergy == 'ไม่แพ้':
+            drug_allergy = ''
+
+        queries = [gender+age+bmi+food_allergy+drug_allergy+congentital_disease]
+
+        sbert_searched = sbert_search(queries)
+
+        st.write(f"{queries}")
+        st.pyplot(visualize_articles_images(sbert_searched['title'],sbert_searched['banner']))
+    
+if __name__ == "__main__":
+    main()

corpus_embeddings.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df1b3b8a5bbcb56feefb6fc9f10377ca717a161fa4a45358f66c3266f9c05e93
+size 14656907