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import streamlit as st
import numpy as np
import pandas as pd
import pickle
# Load model
try:
with open("final_model (3).pkl", "rb") as f:
model = pickle.load(f)
st.success("✅ Model loaded successfully!")
except FileNotFoundError:
st.error("❌ Model file not found! Please upload `final_model (3).pkl`.")
model = None
# Custom CSS for styling
st.markdown(
"""
<style>
.stApp {
background-color: #121212;
color: #FFFFFF;
}
.title {
text-align: center;
font-size: 38px;
font-weight: bold;
color: #BB86FC;
margin-top: 20px;
}
.stButton > button {
background-color: #6200EE;
color: white;
font-size: 18px;
border-radius: 8px;
padding: 10px 20px;
transition: 0.3s ease-in-out;
}
.stButton > button:hover {
background-color: #3700B3;
transform: scale(1.05);
}
.result-box {
text-align: center;
font-size: 22px;
font-weight: bold;
color: white;
padding: 16px;
border-radius: 10px;
margin-top: 30px;
box-shadow: 0px 4px 12px rgba(255, 255, 255, 0.3);
}
.high-risk {
background-color: #D32F2F;
}
.low-risk {
background-color: #388E3C;
}
.probability {
background-color: #FFA726;
margin-top: 12px;
}
</style>
""",
unsafe_allow_html=True,
)
st.markdown("<h1 class='title'>🩺 PCOS Risk Predictor</h1>", unsafe_allow_html=True)
# Collect user inputs
with st.expander("📄 Personal Information", expanded=True):
Age = st.slider("🎂 Age", 18, 44, 30)
with st.expander("🏥 Medical Information", expanded=True):
BMI = st.number_input("⚖️ BMI", min_value=8, max_value=50, value=23)
Menstrual_Irregularity = st.selectbox("🩸 Menstrual Irregularity (0 = No, 1 = Yes)", [0, 1])
Testosterone_Level = st.number_input("🧬 Testosterone Level (ng/dL)", min_value=20, max_value=135, value=53)
Antral_Follicle_Count = st.number_input("🧪 Antral Follicle Count", min_value=3, max_value=39, value=8)
# Predict
if st.button("🔍 Predict Risk"):
if model is None:
st.error("⚠️ Model is not loaded. Please ensure the model file exists.")
else:
try:
input_df = pd.DataFrame([[
Age,
BMI,
Menstrual_Irregularity,
Testosterone_Level,
Antral_Follicle_Count
]], columns=[
'Age',
'BMI',
'Menstrual_Irregularity',
'Testosterone_Level(ng/dL)',
'Antral_Follicle_Count'
])
prediction = model.predict(input_df)[0]
probability = model.predict_proba(input_df)[0][1] * 100
if prediction == 1:
result_text = "⚠️❌ High Risk of PCOS Detected!"
result_class = "high-risk"
else:
result_text = "✅ Low Risk of PCOS — You're Good!"
result_class = "low-risk"
st.markdown(
f"<div class='result-box {result_class}'>{result_text}</div>",
unsafe_allow_html=True
)
st.markdown(
f"<div class='result-box probability'>📊 Estimated Probability: {probability:.2f}%</div>",
unsafe_allow_html=True
)
except Exception as e:
st.error(f"🚫 Prediction failed: {e}")
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