delete radius

app.py

@@ -73,7 +73,7 @@ def normalize_distance(distances):
     return normalized
 
 # Menambahkan radius sebagai parameter
-def prepare_and_recommend(df, user_skills, user_location, radius_km):
+def prepare_and_recommend(df, user_skills, user_location):
     # 1. Memastikan dataset memiliki koordinat
     if 'latitude' not in df or 'longitude' not in df:
         raise ValueError("Dataset harus memiliki kolom latitude dan longitude")
@@ -100,7 +100,7 @@ def prepare_and_recommend(df, user_skills, user_location, radius_km):
     df['distance (km)'] = distances
 
     # 5. Filter pekerjaan berdasarkan radius
-    df = df[df['distance (km)'] <= radius_km]
+    df = df[df['distance (km)']]
 
     # 6. Normalisasi jarak
     if not df.empty:
@@ -118,16 +118,15 @@ def prepare_and_recommend(df, user_skills, user_location, radius_km):
     return top_jobs[['job_link', 'title', 'company', 'location', 'distance (km)', 'final score']]
 
 # Streamlit app
-st.title('Job Recommendation System')
-st.write('Enter your skills and location to get job recommendations.')
+st.title('Job Findr')
+st.write('Find your job with ease.')
 
 user_skills = st.text_input('Enter your skills (comma-separated):')
-user_location = st.text_input('Enter your location:')
-radius_km = st.number_input('Enter your preferred radius (in km):', min_value=1, value=10)
+user_location = st.text_input('Job location:')
 
 if st.button('Get Recommendations'):
     if user_skills and user_location:
-        recommended_jobs = prepare_and_recommend(sample_data, user_skills, user_location, radius_km)
+        recommended_jobs = prepare_and_recommend(sample_data, user_skills, user_location)
         if recommended_jobs.empty:
             st.warning('Tidak ditemukan pekerjaan yang sesuai dengan keterampilan dan lokasi Anda.')
         elif recommended_jobs['final score'].max() < 0.02: