Upload app.py

app.py

@@ -0,0 +1,61 @@
+import streamlit as st
+import pandas as pd
+import json
+from utils import (
+    get_prob,
+    device,
+    get_prob_lists,
+    compare_sentence,
+    query_jds,
+    query,
+    tfidf_score,
+    tfidf_matrix,
+)
+from ast import literal_eval
+from stqdm import stqdm
+
+with open("data/joblists.txt") as file:
+    lines = file.readlines()
+    jobs = [line.rstrip() for line in lines]
+
+DB = pd.read_csv("data/JDs_final.csv").dropna()
+data = pd.read_csv("data/processed_courses_data.csv")
+
+
+def get_recommendation(DB, data, jobname, by="course_info"):
+    JD_sentences = query_jds(DB, jobname).description.values
+    DB["Query_Score"] = DB.description.progress_apply(
+        lambda x: compare_sentence(x, jobs[0])
+    )
+    target = DB.sort_values(by="Query_Score", ascending=False)[:10]
+    JD_sentences = target.description.values
+    data["Recommendation_score"] = data[by].progress_apply(
+        lambda x: compare_sentence(x, JD_sentences)
+    )
+    return data.sort_values(by="Recommendation_score", ascending=False)[:26][
+        ["Course_Name", "course_info", "syllabus", "div", "Recommendation_score"]
+    ]
+
+
+st.title("Course Recommender🤔")
+
+if device == "cpu":
+    processor = "🖥️"
+else:
+    processor = "💽"
+
+option = st.checkbox("💻From referece IT jobs?")
+if option:
+    job = st.selectbox("Choose your job", jobs)
+
+else:
+    job = st.text_input("Put job you are interested")
+
+btn = st.button("Run recommendation!")
+stqdm.pandas()
+
+if btn:
+    with st.spinner("⌛Generating Recommendation!"):
+        recommendation = get_recommendation(DB, data, job)
+    st.success("Recommended course for {} ".format(job))
+    st.write(recommendation)