Create app.py

app.py

@@ -0,0 +1,205 @@
+
+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("Dataset/mldata.csv")
+
+
+#load prediction model from notebook
+pickleFile = open('rfweights.pkl','rb')
+rfmodel = pickle.load(pickleFile)
+
+#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=" ",
+                description="Members: " "
+                )
+
+
+#main
+if __name__ == "__main__":
+    demo.launch(share=True)
+
+
+
+