Update app.py

app.py

@@ -1,4 +1,207 @@
 
+# import gradio as gr
+# import pandas as pd 
+# import numpy as np 
+# import pickle
+# import sklearn
+# from datasets import load_dataset
+
+# data = pd.read_csv("mldata.csv")
+
+
+# #load prediction model from notebook
+# # pickleFile = open('rfweights (1).pkl','rb')
+# pickelFile=open('dtreeweights.pkl','rb')
+# rfmodel = pickle.load(pickelFile)
+
+# #Obtain the categorical/nominal data because it is not coded according (but based on the first occurence, first come first assign number)
+# #Therefore, need to read from the file to obtain the number.
+# categorical_cols = data[[
+#     'certifications',
+#     'workshops',
+#     'Interested subjects',
+#     'interested career area ',
+#     'Type of company want to settle in?',
+#     'Interested Type of Books'
+#                         ]]
+# #assign the datatype and automated assigned code
+# for i in categorical_cols:
+#     data[i] = data[i].astype('category')
+#     data[i] = data[i].cat.codes
+
+# #embedded nominal/ categorical values for certicates
+# certificates_name = list(categorical_cols['certifications'].unique())
+# certificates_code = list(data['certifications'].unique())
+# certificates_references = dict(zip(certificates_name,certificates_code))
+
+# #embedding for workshops
+# workshop_name = list(categorical_cols['workshops'].unique())
+# workshop_code = list(data['workshops'].unique())
+# workshop_references = dict(zip(workshop_name, workshop_code))
+
+# #embedding for subjects_interests
+# subjects_interest_name = list(categorical_cols['Interested subjects'].unique())
+# subjects_interest_code = list(data['Interested subjects'].unique())
+# subjects_interest_references = dict(zip(subjects_interest_name, subjects_interest_code))
+
+# #embedding for career_interests
+# career_interest_name = list(categorical_cols['interested career area '].unique())
+# career_interest_code = list(data['interested career area '].unique())
+# career_interest_references = dict(zip(career_interest_name, career_interest_code))
+
+# #embedding for company_intends
+# company_intends_name = list(categorical_cols['Type of company want to settle in?'].unique())
+# company_intends_code = list(data['Type of company want to settle in?'].unique())
+# company_intends_references = dict(zip(company_intends_name, company_intends_code))
+
+# #embedding for book_interests
+# book_interest_name = list(categorical_cols['Interested Type of Books'].unique())
+# book_interest_code = list(data['Interested Type of Books'].unique())
+# book_interest_references = dict(zip(book_interest_name, book_interest_code))
+
+
+# def greet(name):
+#     return f"Hello, {name}!"
+
+# '''#dummy encode
+# def dummy_encode(df):
+#     if input == "Management":
+#         return [1, 0]
+#     elif input == "Technical":
+#         return [0, 1]
+#     elif input == "smart worker":
+#         return [1, 0]
+#     elif input == "hard worker":
+#         return [0, 1]
+#     else:
+#         return "Invalid choice"'''
+
+# def rfprediction(name, logical_thinking, hackathon_attend, coding_skills, public_speaking_skills,
+#                  self_learning, extra_course, certificate_code, worskhop_code, read_writing_skill, memory_capability
+#                  ,subject_interest, career_interest, company_intend, senior_elder_advise, book_interest, introvert_extro,
+#                  team_player, management_technical, smart_hardworker):
+#     df = pd.DataFrame.from_dict(
+#         {
+#             "logical_thinking": [logical_thinking],
+#             "hackathon_attend": [hackathon_attend],
+#             "coding_skills": [coding_skills],
+#             "public_speaking_skills": [public_speaking_skills],
+#             "self_learning": [self_learning],
+#             "extra_course": [extra_course],
+#             "certificate": [certificate_code],
+#             "workshop": [worskhop_code],
+#             "read_writing_skills": [
+#                     (0 if "poor" in read_writing_skill else 1 if "medium" in read_writing_skill else 2)
+#                 ],
+#             "memory_capability": [
+#                     (0 if "poor" in memory_capability else 1 if "medium" in memory_capability else 2)   
+#                 ],
+#             "subject_interest": [subject_interest],
+#             "career_interest": [career_interest],
+#             "company_intend": [company_intend],
+#             "senior_elder_advise": [senior_elder_advise],
+#             "book_interest": [book_interest],
+#             "introvert_extro": [introvert_extro],
+#             "team_player": [team_player],
+#             "management_technical":[management_technical],
+#             "smart_hardworker": [smart_hardworker]
+#         }
+#     )
+    
+#     #replace str to numeric representation, dtype chged to int8
+#     df = df.replace({"certificate": certificates_references, 
+#                                        "workshop":workshop_references,
+#                                        "subject_interest":subjects_interest_references,
+#                                        "career_interest": career_interest_references,
+#                                        "company_intend":company_intends_references,
+#                                        "book_interest":book_interest_references})
+    
+#     #dummy encoding
+#     #first we convert into list from df
+#     userdata_list = df.values.tolist()
+#     #now we append boolean based conditions
+#     if(df["management_technical"].values == "Management"):
+#         userdata_list[0].extend([1])
+#         userdata_list[0].extend([0])
+#         userdata_list[0].remove('Management')
+#     elif(df["management_technical"].values == "Technical"):
+#         userdata_list[0].extend([0])
+#         userdata_list[0].extend([1])
+#         userdata_list[0].remove('Technical')
+#     else: return "Err"
+
+#     if(df["smart_hardworker"].values == "smart worker"):
+#         userdata_list[0].extend([1])
+#         userdata_list[0].extend([0])
+#         userdata_list[0].remove('smart worker')
+#     elif(df["smart_hardworker"].values == "hard worker"):
+#         userdata_list[0].extend([0])
+#         userdata_list[0].extend([1])
+#         userdata_list[0].remove('hard worker')
+#     else: return "Err"
+
+#     prediction_result = rfmodel.predict(userdata_list)
+#     prediction_result_all = rfmodel.predict_proba(userdata_list)
+#     print(prediction_result_all)
+#     #create a list for output 
+#     result_list = {"Applications Developer": float(prediction_result_all[0][0]),
+#                    "CRM Technical Developer": float(prediction_result_all[0][1]),
+#                    "Database Developer": float(prediction_result_all[0][2]),
+#                    "Mobile Applications Developer": float(prediction_result_all[0][3]),
+#                    "Network Security Engineer": float(prediction_result_all[0][4]),
+#                    "Software Developer": float(prediction_result_all[0][5]),
+#                    "Software Engineer": float(prediction_result_all[0][6]),
+#                    "Software Quality Assurance (QA)/ Testing": float(prediction_result_all[0][7]),
+#                    "Systems Security Administrator": float(prediction_result_all[0][8]),
+#                    "Technical Support": float(prediction_result_all[0][9]),
+#                    "UX Designer": float(prediction_result_all[0][10]),
+#                    "Web Developer": float(prediction_result_all[0][11]),
+#                    }
+#     return result_list
+
+# cert_list = ["app development", "distro making", "full stack", "hadoop", "information security", "machine learning", "python", "r programming", "shell programming"]
+# workshop_list = ["cloud computing", "data science", "database security", "game development", "hacking", "system designing", "testing", "web technologies"]
+# skill = ["excellent", "medium", "poor"] #can be used in this section and memory capability section
+# subject_list = ["cloud computing", "Computer Architecture", "data engineering", "hacking", "IOT", "Management", "networks", "parallel computing", "programming", "Software Engineering"]
+# career_list = ["Business process analyst", "cloud computing", "developer", "security", "system developer", "testing"]
+# company_list = ["BPA", "Cloud Services", "Finance", "Product based", "product development", "SAaS services", "Sales and Marketing", "Service Based", "Testing and Maintainance Services", "Web Services"]
+# book_list = ["Action and Adventure", "Anthology", "Art", "Autobiographies", "Biographies", "Childrens", "Comics","Cookbooks","Diaries","Dictionaries","Drama","Encyclopedias","Fantasy","Guide","Health","History","Horror","Journals","Math","Mystery","Poetry","Prayer books","Religion-Spirituality","Romance","Satire","Science","Science fiction","Self help","Series","Travel","Trilogy"]
+# Choice_list = ["Management", "Technical"]
+# worker_list = ["hard worker", "smart worker"]
+
+# demo =gr.Interface(fn = rfprediction, inputs=[
+#                 gr.Textbox(placeholder="What is your name?", label="Name"),
+#                 gr.Slider(minimum=1,maximum=9,value=3,step=1,label="Are you a logical thinking person?", info="Scale: 1 - 9"),
+#                 gr.Slider(minimum=0,maximum=6,value=0,step=1,label="Do you attend any Hackathons?", info="Scale: 0 - 6 | 0 - if not attended any"),
+#                 gr.Slider(minimum=1,maximum=9,value=5,step=1,label="How do you rate your coding skills?", info="Scale: 1 - 9"),
+#                 gr.Slider(minimum=1,maximum=9,value=3,step=1,label="How do you rate your public speaking skills/confidency?", info="Scale: 1 - 9"),
+#                 gr.Radio({"Yes", "No"}, type="index", label="Are you a self-learning person? *"),
+#                 gr.Radio({"Yes", "No"}, type="index", label="Do you take extra courses in uni (other than IT)? *"),
+#                 gr.Dropdown(cert_list, label="Select a certificate you took!"),
+#                 gr.Dropdown(workshop_list, label="Select a workshop you attended!"),
+#                 gr.Dropdown(skill, label="Select your read and writing skill"),
+#                 gr.Dropdown(skill, label="Is your memory capability good?"),
+#                 gr.Dropdown(subject_list, label="What subject you are interested in?"),
+#                 gr.Dropdown(career_list, label="Which IT-Career do you have interests in?"),
+#                 gr.Dropdown(company_list, label="Do you have any interested company that you intend to settle in?"),
+#                 gr.Radio({"Yes", "No"}, type="index", label="Do you ever seek any advices from senior or elders? *"),
+#                 gr.Dropdown(book_list, label="Select your interested genre of book!"),
+#                 gr.Radio({"Yes", "No"}, type="index", label="Are you an Introvert?| No - extrovert *"),
+#                 gr.Radio({"Yes", "No"}, type="index", label="Ever worked in a team? *"),
+#                 gr.Dropdown(Choice_list, label="Which area do you prefer: Management or Technical?"),
+#                 gr.Dropdown(worker_list, label="Are you a Smart worker or Hard worker?")
+#                 ],
+#                 outputs=gr.Label(num_top_classes=5), 
+#                 title="IT-Career Recommendation System: TMI4033 Colletive Intelligence, Group 12",
+#                 description="Members: Derrick Lim Kin Yeap 74597, Jason Jong Sheng Tat 75125, Jason Ng Yong Xing 75127, Muhamad Hazrie Bin Suhkery 73555 "
+#                 )
+
+
+# #main
+# if __name__ == "__main__":
+#     demo.launch(share=True)
+
+
 import gradio as gr
 import pandas as pd 
 import numpy as np 
@@ -6,16 +209,21 @@ import pickle
 import sklearn
 from datasets import load_dataset
 
+# Read the data
 data = pd.read_csv("mldata.csv")
 
+# Function to load model based on selection
+def load_model(model_choice):
+    if model_choice == "Random Forest":
+        with open('rfweights (1).pkl', 'rb') as pickleFile:
+            return pickle.load(pickleFile)
+    elif model_choice == "Decision Tree":
+        with open('dtreeweights.pkl', 'rb') as pickleFile:
+            return pickle.load(pickleFile)
+    else:
+        raise ValueError("Invalid model selection")
 
-#load prediction model from notebook
-# pickleFile = open('rfweights (1).pkl','rb')
-pickelFile=open('dtreeweights.pkl','rb')
-rfmodel = pickle.load(pickelFile)
-
-#Obtain the categorical/nominal data because it is not coded according (but based on the first occurence, first come first assign number)
-#Therefore, need to read from the file to obtain the number.
+# Prepare categorical data (same as original code)
 categorical_cols = data[[
     'certifications',
     'workshops',
@@ -23,63 +231,35 @@ categorical_cols = data[[
     'interested career area ',
     'Type of company want to settle in?',
     'Interested Type of Books'
-                        ]]
-#assign the datatype and automated assigned code
+]]
+
+# Assign category codes
 for i in categorical_cols:
     data[i] = data[i].astype('category')
     data[i] = data[i].cat.codes
 
-#embedded nominal/ categorical values for certicates
-certificates_name = list(categorical_cols['certifications'].unique())
-certificates_code = list(data['certifications'].unique())
-certificates_references = dict(zip(certificates_name,certificates_code))
-
-#embedding for workshops
-workshop_name = list(categorical_cols['workshops'].unique())
-workshop_code = list(data['workshops'].unique())
-workshop_references = dict(zip(workshop_name, workshop_code))
-
-#embedding for subjects_interests
-subjects_interest_name = list(categorical_cols['Interested subjects'].unique())
-subjects_interest_code = list(data['Interested subjects'].unique())
-subjects_interest_references = dict(zip(subjects_interest_name, subjects_interest_code))
-
-#embedding for career_interests
-career_interest_name = list(categorical_cols['interested career area '].unique())
-career_interest_code = list(data['interested career area '].unique())
-career_interest_references = dict(zip(career_interest_name, career_interest_code))
-
-#embedding for company_intends
-company_intends_name = list(categorical_cols['Type of company want to settle in?'].unique())
-company_intends_code = list(data['Type of company want to settle in?'].unique())
-company_intends_references = dict(zip(company_intends_name, company_intends_code))
-
-#embedding for book_interests
-book_interest_name = list(categorical_cols['Interested Type of Books'].unique())
-book_interest_code = list(data['Interested Type of Books'].unique())
-book_interest_references = dict(zip(book_interest_name, book_interest_code))
-
-
-def greet(name):
-    return f"Hello, {name}!"
-
-'''#dummy encode
-def dummy_encode(df):
-    if input == "Management":
-        return [1, 0]
-    elif input == "Technical":
-        return [0, 1]
-    elif input == "smart worker":
-        return [1, 0]
-    elif input == "hard worker":
-        return [0, 1]
-    else:
-        return "Invalid choice"'''
+# Create reference dictionaries for embeddings (same as original code)
+def create_embedding_dict(column):
+    unique_names = list(categorical_cols[column].unique())
+    unique_codes = list(data[column].unique())
+    return dict(zip(unique_names, unique_codes))
 
-def rfprediction(name, logical_thinking, hackathon_attend, coding_skills, public_speaking_skills,
-                 self_learning, extra_course, certificate_code, worskhop_code, read_writing_skill, memory_capability
-                 ,subject_interest, career_interest, company_intend, senior_elder_advise, book_interest, introvert_extro,
+certificates_references = create_embedding_dict('certifications')
+workshop_references = create_embedding_dict('workshops')
+subjects_interest_references = create_embedding_dict('Interested subjects')
+career_interest_references = create_embedding_dict('interested career area ')
+company_intends_references = create_embedding_dict('Type of company want to settle in?')
+book_interest_references = create_embedding_dict('Interested Type of Books')
+
+# Prediction function (modified to accept model choice)
+def rfprediction(model_choice, name, logical_thinking, hackathon_attend, coding_skills, public_speaking_skills,
+                 self_learning, extra_course, certificate_code, worskhop_code, read_writing_skill, memory_capability,
+                 subject_interest, career_interest, company_intend, senior_elder_advise, book_interest, introvert_extro,
                  team_player, management_technical, smart_hardworker):
+    # Load the selected model
+    rfmodel = load_model(model_choice)
+    
+    # Create DataFrame (same as original code)
     df = pd.DataFrame.from_dict(
         {
             "logical_thinking": [logical_thinking],
@@ -108,18 +288,20 @@ def rfprediction(name, logical_thinking, hackathon_attend, coding_skills, public
         }
     )
     
-    #replace str to numeric representation, dtype chged to int8
-    df = df.replace({"certificate": certificates_references, 
-                                       "workshop":workshop_references,
-                                       "subject_interest":subjects_interest_references,
-                                       "career_interest": career_interest_references,
-                                       "company_intend":company_intends_references,
-                                       "book_interest":book_interest_references})
+    # Replace string values with numeric representations
+    df = df.replace({
+        "certificate": certificates_references, 
+        "workshop": workshop_references,
+        "subject_interest": subjects_interest_references,
+        "career_interest": career_interest_references,
+        "company_intend": company_intends_references,
+        "book_interest": book_interest_references
+    })
     
-    #dummy encoding
-    #first we convert into list from df
+    # Dummy encoding (same as original code)
     userdata_list = df.values.tolist()
-    #now we append boolean based conditions
+    
+    # Management-Technical dummy encoding
     if(df["management_technical"].values == "Management"):
         userdata_list[0].extend([1])
         userdata_list[0].extend([0])
@@ -128,8 +310,10 @@ def rfprediction(name, logical_thinking, hackathon_attend, coding_skills, public
         userdata_list[0].extend([0])
         userdata_list[0].extend([1])
         userdata_list[0].remove('Technical')
-    else: return "Err"
+    else: 
+        return "Error in Management-Technical encoding"
 
+    # Smart-Hard worker dummy encoding
     if(df["smart_hardworker"].values == "smart worker"):
         userdata_list[0].extend([1])
         userdata_list[0].extend([0])
@@ -138,30 +322,33 @@ def rfprediction(name, logical_thinking, hackathon_attend, coding_skills, public
         userdata_list[0].extend([0])
         userdata_list[0].extend([1])
         userdata_list[0].remove('hard worker')
-    else: return "Err"
+    else: 
+        return "Error in Smart-Hard worker encoding"
 
-    prediction_result = rfmodel.predict(userdata_list)
+    # Prediction
     prediction_result_all = rfmodel.predict_proba(userdata_list)
-    print(prediction_result_all)
-    #create a list for output 
-    result_list = {"Applications Developer": float(prediction_result_all[0][0]),
-                   "CRM Technical Developer": float(prediction_result_all[0][1]),
-                   "Database Developer": float(prediction_result_all[0][2]),
-                   "Mobile Applications Developer": float(prediction_result_all[0][3]),
-                   "Network Security Engineer": float(prediction_result_all[0][4]),
-                   "Software Developer": float(prediction_result_all[0][5]),
-                   "Software Engineer": float(prediction_result_all[0][6]),
-                   "Software Quality Assurance (QA)/ Testing": float(prediction_result_all[0][7]),
-                   "Systems Security Administrator": float(prediction_result_all[0][8]),
-                   "Technical Support": float(prediction_result_all[0][9]),
-                   "UX Designer": float(prediction_result_all[0][10]),
-                   "Web Developer": float(prediction_result_all[0][11]),
-                   }
+    
+    # Create result dictionary
+    result_list = {
+        "Applications Developer": float(prediction_result_all[0][0]),
+        "CRM Technical Developer": float(prediction_result_all[0][1]),
+        "Database Developer": float(prediction_result_all[0][2]),
+        "Mobile Applications Developer": float(prediction_result_all[0][3]),
+        "Network Security Engineer": float(prediction_result_all[0][4]),
+        "Software Developer": float(prediction_result_all[0][5]),
+        "Software Engineer": float(prediction_result_all[0][6]),
+        "Software Quality Assurance (QA)/ Testing": float(prediction_result_all[0][7]),
+        "Systems Security Administrator": float(prediction_result_all[0][8]),
+        "Technical Support": float(prediction_result_all[0][9]),
+        "UX Designer": float(prediction_result_all[0][10]),
+        "Web Developer": float(prediction_result_all[0][11]),
+    }
     return result_list
 
+# Lists for dropdown menus (same as original code)
 cert_list = ["app development", "distro making", "full stack", "hadoop", "information security", "machine learning", "python", "r programming", "shell programming"]
 workshop_list = ["cloud computing", "data science", "database security", "game development", "hacking", "system designing", "testing", "web technologies"]
-skill = ["excellent", "medium", "poor"] #can be used in this section and memory capability section
+skill = ["excellent", "medium", "poor"]
 subject_list = ["cloud computing", "Computer Architecture", "data engineering", "hacking", "IOT", "Management", "networks", "parallel computing", "programming", "Software Engineering"]
 career_list = ["Business process analyst", "cloud computing", "developer", "security", "system developer", "testing"]
 company_list = ["BPA", "Cloud Services", "Finance", "Product based", "product development", "SAaS services", "Sales and Marketing", "Service Based", "Testing and Maintainance Services", "Web Services"]
@@ -169,34 +356,37 @@ book_list = ["Action and Adventure", "Anthology", "Art", "Autobiographies", "Bio
 Choice_list = ["Management", "Technical"]
 worker_list = ["hard worker", "smart worker"]
 
-demo =gr.Interface(fn = rfprediction, inputs=[
-                gr.Textbox(placeholder="What is your name?", label="Name"),
-                gr.Slider(minimum=1,maximum=9,value=3,step=1,label="Are you a logical thinking person?", info="Scale: 1 - 9"),
-                gr.Slider(minimum=0,maximum=6,value=0,step=1,label="Do you attend any Hackathons?", info="Scale: 0 - 6 | 0 - if not attended any"),
-                gr.Slider(minimum=1,maximum=9,value=5,step=1,label="How do you rate your coding skills?", info="Scale: 1 - 9"),
-                gr.Slider(minimum=1,maximum=9,value=3,step=1,label="How do you rate your public speaking skills/confidency?", info="Scale: 1 - 9"),
-                gr.Radio({"Yes", "No"}, type="index", label="Are you a self-learning person? *"),
-                gr.Radio({"Yes", "No"}, type="index", label="Do you take extra courses in uni (other than IT)? *"),
-                gr.Dropdown(cert_list, label="Select a certificate you took!"),
-                gr.Dropdown(workshop_list, label="Select a workshop you attended!"),
-                gr.Dropdown(skill, label="Select your read and writing skill"),
-                gr.Dropdown(skill, label="Is your memory capability good?"),
-                gr.Dropdown(subject_list, label="What subject you are interested in?"),
-                gr.Dropdown(career_list, label="Which IT-Career do you have interests in?"),
-                gr.Dropdown(company_list, label="Do you have any interested company that you intend to settle in?"),
-                gr.Radio({"Yes", "No"}, type="index", label="Do you ever seek any advices from senior or elders? *"),
-                gr.Dropdown(book_list, label="Select your interested genre of book!"),
-                gr.Radio({"Yes", "No"}, type="index", label="Are you an Introvert?| No - extrovert *"),
-                gr.Radio({"Yes", "No"}, type="index", label="Ever worked in a team? *"),
-                gr.Dropdown(Choice_list, label="Which area do you prefer: Management or Technical?"),
-                gr.Dropdown(worker_list, label="Are you a Smart worker or Hard worker?")
-                ],
-                outputs=gr.Label(num_top_classes=5), 
-                title="IT-Career Recommendation System: TMI4033 Colletive Intelligence, Group 12",
-                description="Members: Derrick Lim Kin Yeap 74597, Jason Jong Sheng Tat 75125, Jason Ng Yong Xing 75127, Muhamad Hazrie Bin Suhkery 73555 "
-                )
-
+# Create Gradio interface (modified to include model selection)
+demo = gr.Interface(
+    fn=rfprediction, 
+    inputs=[
+        gr.Dropdown(["Random Forest", "Decision Tree"], label="Select Machine Learning Model"),
+        gr.Textbox(placeholder="What is your name?", label="Name"),
+        gr.Slider(minimum=1, maximum=9, value=3, step=1, label="Are you a logical thinking person?", info="Scale: 1 - 9"),
+        gr.Slider(minimum=0, maximum=6, value=0, step=1, label="Do you attend any Hackathons?", info="Scale: 0 - 6 | 0 - if not attended any"),
+        gr.Slider(minimum=1, maximum=9, value=5, step=1, label="How do you rate your coding skills?", info="Scale: 1 - 9"),
+        gr.Slider(minimum=1, maximum=9, value=3, step=1, label="How do you rate your public speaking skills/confidency?", info="Scale: 1 - 9"),
+        gr.Radio({"Yes", "No"}, type="index", label="Are you a self-learning person? *"),
+        gr.Radio({"Yes", "No"}, type="index", label="Do you take extra courses in uni (other than IT)? *"),
+        gr.Dropdown(cert_list, label="Select a certificate you took!"),
+        gr.Dropdown(workshop_list, label="Select a workshop you attended!"),
+        gr.Dropdown(skill, label="Select your read and writing skill"),
+        gr.Dropdown(skill, label="Is your memory capability good?"),
+        gr.Dropdown(subject_list, label="What subject you are interested in?"),
+        gr.Dropdown(career_list, label="Which IT-Career do you have interests in?"),
+        gr.Dropdown(company_list, label="Do you have any interested company that you intend to settle in?"),
+        gr.Radio({"Yes", "No"}, type="index", label="Do you ever seek any advices from senior or elders? *"),
+        gr.Dropdown(book_list, label="Select your interested genre of book!"),
+        gr.Radio({"Yes", "No"}, type="index", label="Are you an Introvert?| No - extrovert *"),
+        gr.Radio({"Yes", "No"}, type="index", label="Ever worked in a team? *"),
+        gr.Dropdown(Choice_list, label="Which area do you prefer: Management or Technical?"),
+        gr.Dropdown(worker_list, label="Are you a Smart worker or Hard worker?")
+    ],
+    outputs=gr.Label(num_top_classes=5), 
+    title="IT-Career Recommendation System: TMI4033 Colletive Intelligence, Group 12",
+    description="Members: Derrick Lim Kin Yeap 74597, Jason Jong Sheng Tat 75125, Jason Ng Yong Xing 75127, Muhamad Hazrie Bin Suhkery 73555 "
+)
 
-#main
+# Main execution
 if __name__ == "__main__":
     demo.launch(share=True)