Upload 3 files

app.py

@@ -0,0 +1,209 @@
+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
+# -------------------------
+@st.cache_data
+def load_data():
+    df = pd.read_csv(r"C:\Users\91879\Downloads\fertility_synthetic_50000\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)

requirements (2).txt

@@ -0,0 +1,8 @@
+altair
+pandas
+streamlit
+streamlit_drawable_canvas
+joblib
+pillow
+opencv-python
+scikit-learn