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import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import gradio as gr
import os
# Load the model
model_path = 'career_prediction_model.pkl'
with open(model_path, 'rb') as f:
saved_data = pickle.load(f)
model = saved_data['model']
label_encoders = saved_data['label_encoders']
target_encoder = saved_data['target_encoder']
features = saved_data['features']
target = 'What would you like to become when you grow up'
# Function for individual prediction
def predict_career(work_env, academic_perf, motivation, leadership, tech_savvy, risk_taking=5, financial_stability=5, work_exp="No Experience"):
# Prepare input data
input_data = pd.DataFrame({
'Preferred Work Environment': [work_env],
'Academic Performance (CGPA/Percentage)': [float(academic_perf)],
'Motivation for Career Choice ': [motivation], # Note the space at the end
'Leadership Experience': [leadership],
'Tech-Savviness': [tech_savvy],
'Risk-Taking Ability ': [float(risk_taking)], # Note the space at the end
'Financial Stability - self/family (1 is low income and 10 is high income)': [float(financial_stability)],
'Previous Work Experience (If Any)': [work_exp]
})
# Encode categorical features
for feature in features:
if feature in input_data.columns:
if feature in label_encoders and input_data[feature].dtype == 'object':
try:
input_data[feature] = label_encoders[feature].transform(input_data[feature])
except ValueError:
# Handle unknown categories
print(f"Warning: Unknown category in {feature}. Using most frequent category.")
input_data[feature] = 0 # Default to first category
else:
print(f"Warning: Feature {feature} not found in input data.")
# Make prediction
prediction = model.predict(input_data)[0]
predicted_career = target_encoder.inverse_transform([int(prediction)])[0]
# Get probabilities for all classes
if hasattr(model, 'predict_proba'):
probabilities = model.predict_proba(input_data)[0]
class_probs = {target_encoder.inverse_transform([i])[0]: prob
for i, prob in enumerate(probabilities)}
sorted_probs = dict(sorted(class_probs.items(), key=lambda x: x[1], reverse=True))
result = f"Predicted career: {predicted_career}\n\nProbabilities:\n"
for career, prob in sorted_probs.items():
result += f"{career}: {prob:.2f}\n"
return result
else:
return f"Predicted career: {predicted_career}"
# Get unique values for dropdowns
work_env_options = list(label_encoders['Preferred Work Environment'].classes_)
motivation_options = list(label_encoders['Motivation for Career Choice '].classes_)
leadership_options = list(label_encoders['Leadership Experience'].classes_)
tech_savvy_options = list(label_encoders['Tech-Savviness'].classes_)
# Get work experience options if available
work_exp_options = []
if 'Previous Work Experience (If Any)' in label_encoders:
work_exp_options = list(label_encoders['Previous Work Experience (If Any)'].classes_)
else:
work_exp_options = ["No Experience", "Internship", "Part Time", "Full Time"]
# Create the Gradio interface
iface = gr.Interface(
fn=predict_career,
inputs=[
gr.Dropdown(work_env_options, label="Preferred Work Environment"),
gr.Number(label="Academic Performance (CGPA/Percentage)", minimum=0, maximum=10),
gr.Dropdown(motivation_options, label="Motivation for Career Choice"),
gr.Dropdown(leadership_options, label="Leadership Experience"),
gr.Dropdown(tech_savvy_options, label="Tech-Savviness"),
gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Risk-Taking Ability"),
gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Financial Stability"),
gr.Dropdown(work_exp_options, label="Previous Work Experience")
],
outputs="text",
title="Career Prediction Model",
description="Enter your details to predict your future career path",
theme="huggingface"
)
# Create a separate interface for model evaluation
eval_iface = gr.Interface(
fn=evaluate_model_with_csv,
inputs=gr.File(label="Upload Test CSV File"),
outputs=[
gr.Textbox(label="Evaluation Results"),
gr.Image(label="Confusion Matrix")
],
title="Career Prediction Model Evaluation",
description="Upload a CSV file with test data to evaluate the model's performance",
theme="huggingface"
)
# Create a tabbed interface
demo = gr.TabbedInterface(
[iface, eval_iface],
["Individual Prediction", "Batch Evaluation"]
)
# Launch the interface
demo.launch()
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