app.py

import streamlit as st
import torch
import torch.nn as nn
import torch.nn.functional as F
import base64


st.markdown(
    """
    <style>
    /* Set background image for the entire app */
    .stApp {
        background: url('https://www.mrscindore.org/images/placements.jpg') no-repeat center center fixed;
        background-size: cover;
    }
    
    /* Style for the title */
    .stApp h1 {
        background-color: rgba(0, 0, 128, 0.7); 
        color: #ffffff; /* White */
        padding: 10px;
        border-radius: 5px;
        font-size: 2.5em; 
        text-align: center;
    }
    /* Style for input text area */
    .stTextArea textarea {
        background-color: rgba(255, 255, 255, 0.8); 
        color: #000000; /* Black */
        font-size: 1.2em; 
    }
    /* Style for the button */
    .stButton>button {
        background-color: #4CAF50; /* Green */
        color: white;
        font-size: 1.2em;
        border-radius: 10px;
        padding: 10px 24px;
        border: none;
    }
    /* Center the button */
    .stButton {
        display: flex;
        justify-content: center;
    }
    /* Style for the output container */
    .output-container {
        background-color: lightpink;
        color: black;
        font-size: 1.5em;
        padding: 15px;
        border-radius: 10px;
        margin-top: 20px;
        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
        width: 200%;
        margin-left: auto;
        margin-right: auto;
        text-align: center;
    }
    
    </style>
    """,
    unsafe_allow_html=True
)


st.title("Placement Analysis")

# Define the ANN model architecture
class ANN_Model(nn.Module):
    def __init__(self, input_cols=10, hidden1=20, hidden2=20, output=1):  
        super(ANN_Model, self).__init__()
        self.f_connected1 = nn.Linear(input_cols, hidden1)
        self.f_connected2 = nn.Linear(hidden1, hidden2)
        self.out = nn.Linear(hidden2, output)

    def forward(self, x):
        x = F.relu(self.f_connected1(x))
        x = F.relu(self.f_connected2(x))
        x = torch.sigmoid(self.out(x))  
        return x

# Load the model
model = ANN_Model()
model.load_state_dict(torch.load("ANN_model.pth"))
model.eval()

# Create three columns for input fields
col1, col2, col3 = st.columns(3)

with col1:
    SoftSkillsRating = st.number_input("Soft Skills Rating (0 to 5)", min_value=0, max_value=5, step=1)
    cgpa = st.number_input("Enter your CGPA (1 to 10)", min_value=0.0, max_value=10.0, step=0.01)
    internships = st.number_input("Number of Internships", min_value=0, max_value=10, step=1)
   


with col2:
    AptitudeTestScore = st.number_input("Aptitude Test Score(%)", min_value=0, max_value=100, step=1)
    SSC_Marks = st.number_input("SSC Marks (%)", min_value=0, max_value=100, step=1)
    HSC_Marks = st.number_input("HSC Marks(%)", min_value=0, max_value=100, step=1)


with col3:
    PlacementTraining = st.selectbox("Placement Training", ["Yes", "No"])
    PlacementTraining = 1 if PlacementTraining == "Yes" else 0
    certifications = st.selectbox("Do you have certifications?", ["Yes", "No"])
    certifications = 1 if certifications == "Yes" else 0
    ExtracurricularActivities = st.selectbox("Extracurricular Activities", ["Yes", "No"])
    ExtracurricularActivities = 1 if ExtracurricularActivities == "Yes" else 0
projects = st.number_input("Number of Projects", min_value=0, max_value=20, step=1)

# Predict Button
if st.button("Predict Placement"):
    # Prepare input for model
    input_data = torch.tensor([[
        cgpa, internships, projects, certifications, AptitudeTestScore, SoftSkillsRating,
        ExtracurricularActivities, PlacementTraining, SSC_Marks, HSC_Marks
    ]], dtype=torch.float32)

    # Make a prediction
    with torch.no_grad():
        output = model(input_data).item()
        if output >= 0.5:
            st.markdown(
                """
                <div style='background-color: #d4edda; padding: 10px; border-radius: 5px;'>
                    <h4 style='color: #155724;'>🎉 Congratulations! You are likely to get placed.</h4>
                </div>
                """,
                unsafe_allow_html=True
            )
        else:
            st.markdown(
                """
                <div style='background-color: #f8d7da; padding: 10px; border-radius: 5px;'>
                    <h4 style='color: #721c24;'>⚠️ You might need to improve your profile for better chances.</h4>
                </div>
                """,
                unsafe_allow_html=True
            )