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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import os | |
| import joblib | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.preprocessing import StandardScaler | |
| from tensorflow import keras | |
| # ------------------------- | |
| # Streamlit Page Config | |
| # ------------------------- | |
| st.set_page_config(page_title="Fertility Prediction", layout="wide") | |
| st.title("🧬 Fertility Health Prediction App") | |
| # ------------------------- | |
| # Sidebar Navigation | |
| # ------------------------- | |
| page = st.sidebar.radio("📂 Navigate", ["🔍 EDA", "🤖 Model Training", "🔮 Prediction"]) | |
| # ------------------------- | |
| # Load Data | |
| # ------------------------- | |
| def load_data(): | |
| df=pd.read_csv("fertility_synthetic_50000.csv") | |
| df.drop_duplicates(inplace=True) | |
| return df | |
| df = load_data() | |
| # ------------------------- | |
| # EDA Page | |
| # ------------------------- | |
| if page == "🔍 EDA": | |
| st.header("📊 Exploratory Data Analysis") | |
| st.subheader("📑 Dataset Overview") | |
| st.write(f"🗂️ Shape of dataset: {df.shape}") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.write("👀 First 5 rows:") | |
| st.dataframe(df.head()) | |
| with col2: | |
| st.write("📏 Basic statistics:") | |
| st.dataframe(df.describe()) | |
| st.subheader("❓ Missing Values") | |
| st.write(df.isna().sum()) | |
| st.subheader("📈 Data Visualization") | |
| # Target vs Sperm Count | |
| fig, ax = plt.subplots(figsize=(6,4)) | |
| sns.barplot(data=df, x='Target_HealthyOffspring', | |
| y='Male_SpermCount_million_per_mL', | |
| estimator=np.mean, ax=ax, palette="viridis") | |
| ax.set_title('🎯 Target vs Male Sperm Count (mean)') | |
| st.pyplot(fig) | |
| # Correlation heatmap | |
| st.write("🌡️ Correlation Heatmap:") | |
| fig, ax = plt.subplots(figsize=(12,10)) | |
| sns.heatmap(df.corr(numeric_only=True), annot=False, cmap="coolwarm", ax=ax) | |
| st.pyplot(fig) | |
| # ------------------------- | |
| # Model Training Page | |
| # ------------------------- | |
| elif page == "🤖 Model Training": | |
| st.header("⚙️ Model Training") | |
| # Prepare data | |
| X = df.drop("Target_HealthyOffspring", axis=1) | |
| y = df["Target_HealthyOffspring"] | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=29) | |
| # Scale data | |
| scaler = StandardScaler() | |
| X_train_scaled = scaler.fit_transform(X_train) | |
| X_test_scaled = scaler.transform(X_test) | |
| # Ensure models directory | |
| os.makedirs("models", exist_ok=True) | |
| joblib.dump(scaler, "models/fertility_scaler.pkl") | |
| # Model architecture | |
| model = keras.Sequential([ | |
| keras.layers.Input(shape=(X_train.shape[1],)), | |
| keras.layers.Dense(7, activation="relu"), | |
| keras.layers.Dense(5, activation="relu"), | |
| keras.layers.Dense(4, activation="relu"), | |
| keras.layers.Dense(2, activation="softmax") | |
| ]) | |
| model.compile(loss="sparse_categorical_crossentropy", | |
| optimizer="adam", | |
| metrics=["accuracy"]) | |
| # Train model | |
| if st.button("🚀 Train Model"): | |
| with st.spinner("⏳ Training in progress..."): | |
| history = model.fit(X_train_scaled, y_train, epochs=10, validation_split=0.2, verbose=1) | |
| # Save model | |
| model.save("models/fertility_model.h5") | |
| st.success("✅ Model trained and saved successfully!") | |
| # Plot training history | |
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) | |
| ax1.plot(history.history["accuracy"], label="Training Accuracy", color="green") | |
| ax1.plot(history.history["val_accuracy"], label="Validation Accuracy", color="blue") | |
| ax1.set_title("📈 Accuracy") | |
| ax1.set_xlabel("Epochs") | |
| ax1.set_ylabel("Accuracy") | |
| ax1.legend() | |
| ax2.plot(history.history["loss"], label="Training Loss", color="red") | |
| ax2.plot(history.history["val_loss"], label="Validation Loss", color="orange") | |
| ax2.set_title("📉 Loss") | |
| ax2.set_xlabel("Epochs") | |
| ax2.set_ylabel("Loss") | |
| ax2.legend() | |
| st.pyplot(fig) | |
| # Evaluate on test set | |
| test_loss, test_acc = model.evaluate(X_test_scaled, y_test, verbose=0) | |
| st.metric("🧪 Test Accuracy", f"{test_acc:.4f}") | |
| st.metric("📉 Test Loss", f"{test_loss:.4f}") | |
| # ------------------------- | |
| # Prediction Page | |
| # ------------------------- | |
| elif page == "🔮 Prediction": | |
| st.header("🔮 Make a Prediction") | |
| try: | |
| model = keras.models.load_model("models/fertility_model.h5") | |
| scaler = joblib.load("models/fertility_scaler.pkl") | |
| st.success("📂 Model & Scaler loaded successfully!") | |
| except: | |
| st.error("❌ Model not found. Please train it first under 'Model Training'.") | |
| st.stop() | |
| # Create input form | |
| with st.form("prediction_form"): | |
| st.subheader("🧾 Enter Patient Details") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown("**👨 Male Factors**") | |
| male_sperm_count = st.number_input("Sperm Count (million/mL)", min_value=0.0, value=15.0) | |
| male_sperm_motility = st.number_input("Sperm Motility (%)", min_value=0.0, max_value=100.0, value=40.0) | |
| male_sperm_morphology = st.number_input("Sperm Morphology (%)", min_value=0.0, max_value=100.0, value=4.0) | |
| male_testosterone = st.number_input("Testosterone (ng/dL)", min_value=0.0, value=300.0) | |
| male_fsh = st.number_input("Male FSH (mIU/mL)", min_value=0.0, value=1.5) | |
| with col2: | |
| st.markdown("**👩 Female Factors**") | |
| female_age = st.number_input("Female Age (years)", min_value=18, max_value=50, value=30) | |
| female_ovulation = st.number_input("Ovulation Regularity (days)", min_value=0, value=28) | |
| female_estradiol = st.number_input("Estradiol (pg/mL)", min_value=0.0, value=20.0) | |
| female_progesterone = st.number_input("Progesterone (ng/mL)", min_value=0.0, value=10.0) | |
| female_fsh = st.number_input("Female FSH (mIU/mL)", min_value=0.0, value=3.0) | |
| st.markdown("**💚 Lifestyle Factors**") | |
| col3, col4 = st.columns(2) | |
| with col3: | |
| intercourse_freq = st.number_input("Intercourse Frequency (per week)", min_value=0, value=2) | |
| folic_acid = st.number_input("Folic Acid Intake (mcg/day)", min_value=0, value=400) | |
| with col4: | |
| smoking = st.number_input("Cigarettes per day", min_value=0, value=0) | |
| alcohol = st.number_input("Alcoholic drinks per week", min_value=0, value=0) | |
| hba1c = st.number_input("HbA1c (%)", min_value=0.0, max_value=20.0, value=5.0) | |
| submitted = st.form_submit_button("✨ Predict") | |
| if submitted: | |
| input_data = np.array([[male_sperm_count, male_sperm_motility, male_sperm_morphology, | |
| male_testosterone, male_fsh, female_age, female_ovulation, | |
| female_estradiol, female_progesterone, female_fsh, | |
| intercourse_freq, folic_acid, smoking, alcohol, hba1c]]) | |
| scaled_input = scaler.transform(input_data) | |
| prediction = model.predict(scaled_input) | |
| predicted_class = np.argmax(prediction, axis=1) | |
| confidence = np.max(prediction) * 100 | |
| st.subheader("📌 Prediction Results") | |
| if predicted_class[0] == 1: | |
| st.success(f"✅ Likely to have healthy offspring (Confidence: {confidence:.2f}%)") | |
| else: | |
| st.error(f"❌ Unlikely to have healthy offspring (Confidence: {confidence:.2f}%)") | |
| st.progress(int(confidence)) | |
| # Probability distribution | |
| fig, ax = plt.subplots(figsize=(6, 4)) | |
| ax.bar(['❌ Unlikely (0)', '✅ Likely (1)'], prediction[0], | |
| color=['crimson', 'seagreen']) | |
| ax.set_title('📊 Prediction Probability Distribution') | |
| ax.set_ylabel('Probability') | |
| st.pyplot(fig) | |