Hugging Face's logo

msgasu
/
career-recommender

Joblib
Model card Files
xet
Community
career-recommender
File size: 4,581 Bytes 
e472d8a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
 
import joblib
import pandas as pd
import numpy as np
import gradio as gr

# Load your trained model
model = joblib.load('trained_model.joblib')

# Define grade to numeric conversion (same as before)
def grade_to_numeric(grade):
    if pd.isna(grade) or grade == "":
        return np.nan
    grade_map = {
        "A1": 1, "B2": 2, "B3": 3, "C4": 4, "C5": 5, "C6": 6,
        "D7": 7, "E8": 8, "F9": 9
    }
    return grade_map.get(grade, np.nan)

# Define your preprocessing function
def preprocess_input(desired_career, aggregate, english, core_maths, science, 

                    social_studies, electives, elective_maths=None, 

                    business_management=None, government=None, chemistry=None,

                    physics=None, economics=None, visual_arts=None, geography=None,

                    e_ict=None, literature=None, biology=None):
    
    # Create a dictionary with all inputs
    input_data = {
        "Desired_Career": desired_career,
        "Aggregate": aggregate,
        "English": english,
        "Core Maths": core_maths,
        "Science": science,
        "Social Studies": social_studies,
        "Electives": electives,
        "Elective Maths": elective_maths,
        "Business Management": business_management,
        "Government": government,
        "Chemistry": chemistry,
        "Physics": physics,
        "Economics": economics,
        "Visual Arts": visual_arts,
        "Geography": geography,
        "E-ICT": e_ict,
        "Literature": literature,
        "Biology": biology
    }
    
    # Convert to DataFrame
    student_df = pd.DataFrame([input_data])
    
    # Convert grades to numerical
    grade_cols = ['English', 'Core Maths', 'Science', 'Social Studies',
                 'Elective Maths', 'Business Management', 'Government',
                 'Chemistry', 'Physics', 'Economics', 'Visual Arts',
                 'Geography', 'E-ICT', 'Literature', 'Biology']
    
    for col in grade_cols:
        if col in student_df.columns:
            student_df[col] = student_df[col].apply(grade_to_numeric)
    
    return student_df

def predict_career(desired_career, aggregate, english, core_maths, science, 

                  social_studies, electives, elective_maths=None, 

                  business_management=None, government=None, chemistry=None,

                  physics=None, economics=None, visual_arts=None, geography=None,

                  e_ict=None, literature=None, biology=None):
    
    # Preprocess input
    processed_input = preprocess_input(
        desired_career, aggregate, english, core_maths, science,
        social_studies, electives, elective_maths, business_management,
        government, chemistry, physics, economics, visual_arts, geography,
        e_ict, literature, biology
    )
    
    # Make prediction
    prediction = model.predict(processed_input)
    probabilities = model.predict_proba(processed_input)[0]
    
    # Get top 3 recommendations
    class_indices = np.argsort(probabilities)[::-1][:3]
    classes = model.classes_
    recommendations = [
        (classes[idx], float(probabilities[idx])) 
        for idx in class_indices
    ]
    
    # Format output
    output = "\n".join(
        [f"{course}: {prob*100:.1f}%" for course, prob in recommendations]
    )
    
    return output

# Create Gradio interface
inputs = [
    gr.Dropdown(["Medicine", "Pharmacy", "Law", "Computer Science", "Engineering", 
                "Business", "Nursing", "Agriculture", "Journalism", "Education"], 
               label="Desired Career"),
    gr.Number(label="Aggregate Score"),
    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9"], label="English"),
    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9"], label="Core Maths"),
    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9"], label="Science"),
    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9"], label="Social Studies"),
    gr.Textbox(label="Electives (comma separated)"),
    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Elective Maths (optional)"),
    # Add other optional subjects similarly
]

outputs = gr.Textbox(label="Recommended Courses")

interface = gr.Interface(
    fn=predict_career,
    inputs=inputs,
    outputs=outputs,
    title="Career Path Recommender",
    description="Enter your academic information to get career recommendations"
)

interface.launch()