app.py

import gradio as gr
import numpy as np
import pandas as pd
import joblib

# Load the trained models
rf_fullstk = joblib.load('rf_hacathon_fullstk.pkl')
rf_prodengg = joblib.load('rf_hacathon_prodengg.pkl')
rf_mkt = joblib.load('rf_hacathon_mkt.pkl')

# Define prediction functions for each model
def predict_fullstk(degree_p, internship, DSA, java):
    new_data = pd.DataFrame({
        'degree_p': degree_p,
        'internship': internship,
        'DSA': DSA,
        'java': java,
    }, index=[0])
    prediction = rf_fullstk.predict(new_data)[0]
    probability = rf_fullstk.predict_proba(new_data)[0][1]
    return 'Placed' if prediction == 1 else 'Not Placed', probability

def predict_prodengg(degree_p, internship, management, leadership):
    new_data = pd.DataFrame({
        'degree_p': degree_p,
        'internship': internship,
        'management': management,
        'leadership': leadership,
    }, index=[0])
    prediction = rf_prodengg.predict(new_data)[0]
    probability = rf_prodengg.predict_proba(new_data)[0][1]
    return 'Placed' if prediction == 1 else 'Not Placed', probability

def predict_mkt(degree_p, internship, communication, sales):
    new_data = pd.DataFrame({
        'degree_p': degree_p,
        'internship': internship,
        'communication': communication,
        'sales': sales,
    }, index=[0])
    prediction = rf_mkt.predict(new_data)[0]
    probability = rf_mkt.predict_proba(new_data)[0][1]
    return 'Placed' if prediction == 1 else 'Not Placed', probability

# Create input and output components for each model
fullstk_inputs = [
    gr.inputs.Number(label='Degree Percentage', min_value=0, max_value=100),
    gr.inputs.Radio(label='Internship', choices=[0, 1]),
    gr.inputs.Radio(label='DSA', choices=[0, 1]),
    gr.inputs.Radio(label='Java', choices=[0, 1])
]
fullstk_output = gr.outputs.Label(num_top_classes=2, label='Placement Status')

prodengg_inputs = [
    gr.inputs.Number(label='Degree Percentage', min_value=0, max_value=100),
    gr.inputs.Radio(label='Internship', choices=[0, 1]),
    gr.inputs.Radio(label='Management Skills', choices=[0, 1]),
    gr.inputs.Radio(label='Leadership Skills', choices=[0, 1])
]
prodengg_output = gr.outputs.Label(num_top_classes=2, label='Placement Status')

mkt_inputs = [
    gr.inputs.Number(label='Degree Percentage', min_value=0, max_value=100),
    gr.inputs.Radio(label='Internship', choices=[0, 1]),
    gr.inputs.Radio(label='Communication Skills', choices=[0, 1]),
    gr.inputs.Radio(label='Sales Skills', choices=[0, 1])
]
mkt_output = gr.outputs.Label(num_top_classes=2, label='Placement Status')

# Create the Gradio app
fullstk_interface = gr.Interface(
    fn=predict_fullstk,
    inputs=fullstk