Update app.py

app.py

@@ -1,178 +1,169 @@
-import streamlit as st
-import pickle
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# ----------- Page Configuration ------------
-st.set_page_config("Sleep State Detection", layout="wide")
-st.title(" Sleep State Detection App")
-
-# ----------- Navigation Sidebar ------------
-page = st.sidebar.radio("📍 Navigation", ["Overview", "EDA", "Predict"])
-
-# ----------- Load Data & Model ------------
-@st.cache_data
-def load_data(filepath):
-    return pd.read_csv(filepath)
-
-@st.cache_data
-def load_model(filepath):
-    with open(filepath, "rb") as f:
-        return pickle.load(f)
-
-# ----------- Histogram Plot Function ------------
-def plot_histogram(df, column, color):
-    fig, ax = plt.subplots()
-    sns.histplot(df[column], bins=30, kde=True, color=color, ax=ax)
-    ax.set_title(f"Distribution of {column}")
-    st.pyplot(fig, use_container_width=True)
-    plt.close()
-
-# ----------- Overview Page ------------
-if page == "Overview":
-    st.header(" Project Overview")
-    st.markdown("This app detects **sleep onset** and **wake-up states** using `anglez` and `enmo` values from a wearable sensor.")
-
-    with st.expander(" Problem Statement"):
-        st.markdown("""
-        - Detect sleep and wake-up periods using wearable sensor data.  
-        - Sleep is estimated from low-movement patterns.
-        """)
-
-    with st.expander(" Objective"):
-        st.markdown("""
-        - Classify sleep vs wake states  
-        - Build an ML model that generalizes to real users  
-        """)
-
-    with st.expander(" Constraints"):
-        st.markdown("""
-        - Missing or noisy data  
-        - Ensure low false alarms  
-        - Simple, real-time capable models  
-        """)
-
-# ----------- EDA Page ------------
-elif page == "EDA":
-    st.header(" Exploratory Data Analysis")
-    df = load_data("cleaned_sleep_data.csv")
-
-    # ---- Multi-select Filter Sleep/Wake ----
-    st.markdown("### 🔎 Filter by Sleep State")
-    state_options = st.multiselect("Select sleep states to display", ["Sleep", "Wake-Up"], default=["Sleep", "Wake-Up"])
-
-    if state_options:
-        filter_map = {"Sleep": 1, "Wake-Up": 0}
-        selected_values = [filter_map[opt] for opt in state_options]
-        df = df[df["sleep"].isin(selected_values)]
-
-    # ---- Histograms ----
-    col1, col2 = st.columns(2)
-    with col1:
-        st.subheader(" Anglez")
-        plot_histogram(df, "anglez", "#74b9ff")
-        st.markdown("- Distribution typical of rest posture")
-
-    with col2:
-        st.subheader(" ENMO")
-        plot_histogram(df, "enmo", "#81ecec")
-        st.markdown("- ENMO reflects movement intensity")
-
-    # ---- Pairplot ----
-    st.subheader(" Feature Relationships")
-    with st.spinner("Creating pairplot..."):
-        pairplot_fig = sns.pairplot(df, vars=['anglez', 'enmo'], hue='sleep', palette='coolwarm')
-        st.pyplot(pairplot_fig.fig, use_container_width=True)
-        plt.close()
-
-    # ---- Boxplots ----
-    st.subheader(" Boxplots")
-    fig, axs = plt.subplots(1, 2, figsize=(12, 5))
-    sns.boxplot(y=df["anglez"], ax=axs[0], color='#74b9ff')
-    axs[0].set_title("Boxplot: Anglez")
-    sns.boxplot(y=df["enmo"], ax=axs[1], color='#81ecec')
-    axs[1].set_title("Boxplot: ENMO")
-    st.pyplot(fig, use_container_width=True)
-    plt.close()
-
-    # ---- Correlation ----
-    st.subheader(" Correlation Heatmap")
-    fig, ax = plt.subplots()
-    sns.heatmap(df[["anglez", "enmo"]].corr(), annot=True, cmap="coolwarm", ax=ax)
-    st.pyplot(fig, use_container_width=True)
-    plt.close()
-
-# ----------- Predict Page ------------
-elif page == "Predict":
-    st.header(" Sleep Prediction")
-    model = load_model("new_sleep_model.pkl")
-
-    # ---- Sleep/Wake Filter Dropdown
-    st.markdown("###  Select Sample Type")
-    state_choice = st.selectbox("Choose Sample Type", ["Custom Input", "Sleep Sample", "Wake-Up Sample"])
-
-    # ---- Default Values Based on Choice
-    if state_choice == "Sleep Sample":
-        default_anglez = -45.0
-        default_enmo = 0.01
-    elif state_choice == "Wake-Up Sample":
-        default_anglez = 20.0
-        default_enmo = 0.2
-    else:
-        default_anglez = 0.0
-        default_enmo = 0.0
-
-    # ---- Input Sliders
-    col1, col2 = st.columns(2)
-    with col1:
-        anglez = st.slider(" Anglez (-180° to 180°)", -180.0, 180.0, default_anglez)
-    with col2:
-        enmo = st.slider(" ENMO (0.0 to 1.0)", 0.0, 1.0, default_enmo)
-
-    # ---- Prediction
-    if st.button(" Predict Sleep State"):
-        input_vector = np.array([[anglez, enmo]])
-        prediction = model.predict(input_vector)[0]
-
-        if hasattr(model, "predict_proba"):
-            proba = model.predict_proba(input_vector)[0]
-            confidence = round(np.max(proba) * 100, 2)
-            st.metric(" Model Confidence", f"{confidence}%")
-
-        labels = {
-            0: (" Wake-Up", "You're likely **awake** — motion and posture detected."),
-            1: (" Sleep Onset", "Low motion detected — you may be **falling asleep**.")
-        }
-
-        label, message = labels.get(prediction, ("❓ Unknown", "⚠️ No clear state detected."))
-        st.success(f"** Predicted State:** {label}")
-        st.info(message)
-
-        # Display image based on prediction
-        if prediction == 1:
-            st.image(
-                "https://huggingface.co/spaces/Saidee156/AI_SLEEP_DETECTION/resolve/main/th%20(1).jpeg",
-                use_container_width=True,
-            )
-            st.markdown("""
-            ###  Personalized Sleep Tips
-             **Tips to Fall Asleep Faster**  
-            -  Avoid screens 30 mins before bed  
-            -  Keep the room cool and dark  
-            -  Try deep breathing or meditation  
-            -  Stick to a regular sleep schedule  
-            """)
-        else:
-            st.image(
-                "https://huggingface.co/spaces/Saidee156/AI_SLEEP_DETECTION/resolve/main/cute-little-boy-wake-up-in-morning-stretching-hands-on-bed-in-bedroom-vector.jpg",
-                use_container_width=True,
-            )
-            st.markdown("""
-            ###  Tips to Wake Up Refreshed
-            -  Get morning sunlight exposure  
-            -  Move or stretch your body  
-            -  Eat a light, energizing breakfast  
-            -  Cold water splash or shower helps  
-            """)
+import streamlit as st
+import pickle
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# ----------- Page Configuration ------------
+st.set_page_config("Smart Sleep Monitor", layout="wide")
+st.title(" 🌚 Smart Sleep Monitoring System")
+
+# ----------- Navigation Sidebar ------------
+page = st.sidebar.radio("📍 Navigate", ["Overview", "EDA", "Predict"])
+
+# ----------- Load Data & Model ------------
+@st.cache_data
+def load_data(filepath):
+    return pd.read_csv(filepath)
+
+@st.cache_data
+def load_model(filepath):
+    with open(filepath, "rb") as f:
+        return pickle.load(f)
+
+# ----------- Histogram Plot Function ------------
+def plot_histogram(df, column, color):
+    fig, ax = plt.subplots()
+    sns.histplot(df[column], bins=30, kde=True, color=color, ax=ax)
+    ax.set_title(f"Distribution of {column}")
+    st.pyplot(fig, use_container_width=True)
+    plt.close()
+
+# ----------- Overview Page ------------
+if page == "Overview":
+    st.header(" 🔎 Project Overview")
+    st.markdown("This application predicts **sleep** and **wake** states using data from wearable sensors, focusing on `anglez` and `enmo` values to monitor body motion and position.")
+
+    with st.expander(" 📄 Problem Statement"):
+        st.markdown("""
+        - Increasing mental health issues in fast-paced environments are linked to poor sleep quality.
+        - This system helps in early identification of irregular sleep patterns to support healthier lifestyles.
+        """)
+
+    with st.expander(" 🔮 Objective"):
+        st.markdown("""
+        - Accurately classify sleep vs wake states using sensor inputs
+        - Provide doctors and users with actionable insights into sleep behavior
+        - Develop lightweight, real-time deployable models
+        """)
+
+    with st.expander(" ⚠️ Constraints"):
+        st.markdown("""
+        - Noisy or missing data from wearables
+        - Balancing sensitivity with low false-positive rate
+        - Ensuring usability across diverse users and environments
+        """)
+
+# ----------- EDA Page ------------
+elif page == "EDA":
+    st.header(" 🔍 Exploratory Data Analysis")
+    df = load_data("cleaned_sleep_data.csv")
+
+    st.markdown("### 🔍 Filter by Sleep State")
+    state_options = st.multiselect("Select sleep states to display", ["Sleep", "Wake-Up"], default=["Sleep", "Wake-Up"])
+
+    if state_options:
+        filter_map = {"Sleep": 1, "Wake-Up": 0}
+        selected_values = [filter_map[opt] for opt in state_options]
+        df = df[df["sleep"].isin(selected_values)]
+
+    col1, col2 = st.columns(2)
+    with col1:
+        st.subheader(" Anglez")
+        plot_histogram(df, "anglez", "#6c5ce7")
+        st.markdown("- Postural behavior during rest")
+
+    with col2:
+        st.subheader(" ENMO")
+        plot_histogram(df, "enmo", "#00cec9")
+        st.markdown("- Captures motion intensity")
+
+    st.subheader(" Feature Relationships")
+    with st.spinner("Creating pairplot..."):
+        pairplot_fig = sns.pairplot(df, vars=['anglez', 'enmo'], hue='sleep', palette='Set2')
+        st.pyplot(pairplot_fig.fig, use_container_width=True)
+        plt.close()
+
+    st.subheader(" Boxplots")
+    fig, axs = plt.subplots(1, 2, figsize=(12, 5))
+    sns.boxplot(y=df["anglez"], ax=axs[0], color='#6c5ce7')
+    axs[0].set_title("Boxplot: Anglez")
+    sns.boxplot(y=df["enmo"], ax=axs[1], color='#00cec9')
+    axs[1].set_title("Boxplot: ENMO")
+    st.pyplot(fig, use_container_width=True)
+    plt.close()
+
+    st.subheader(" Correlation Heatmap")
+    fig, ax = plt.subplots()
+    sns.heatmap(df[["anglez", "enmo"]].corr(), annot=True, cmap="Purples", ax=ax)
+    st.pyplot(fig, use_container_width=True)
+    plt.close()
+
+# ----------- Predict Page ------------
+elif page == "Predict":
+    st.header(" 🤖 Sleep Prediction")
+    model = load_model("new_sleep_model.pkl")
+
+    st.markdown("###  Select Sample Type")
+    state_choice = st.selectbox("Choose Sample Type", ["Custom Input", "Sleep Sample", "Wake-Up Sample"])
+
+    if state_choice == "Sleep Sample":
+        default_anglez = -45.0
+        default_enmo = 0.01
+    elif state_choice == "Wake-Up Sample":
+        default_anglez = 20.0
+        default_enmo = 0.2
+    else:
+        default_anglez = 0.0
+        default_enmo = 0.0
+
+    col1, col2 = st.columns(2)
+    with col1:
+        anglez = st.slider(" Anglez (-180° to 180°)", -180.0, 180.0, default_anglez)
+    with col2:
+        enmo = st.slider(" ENMO (0.0 to 1.0)", 0.0, 1.0, default_enmo)
+
+    if st.button(" 🔎 Predict Sleep State"):
+        input_vector = np.array([[anglez, enmo]])
+        prediction = model.predict(input_vector)[0]
+
+        if hasattr(model, "predict_proba"):
+            proba = model.predict_proba(input_vector)[0]
+            confidence = round(np.max(proba) * 100, 2)
+            st.metric(" Model Confidence", f"{confidence}%")
+
+        labels = {
+            0: (" Wake-Up", "You're likely **awake** — motion and posture detected."),
+            1: (" Sleep Onset", "Low motion detected — you may be **falling asleep**.")
+        }
+
+        label, message = labels.get(prediction, ("❓ Unknown", "⚠️ No clear state detected."))
+        st.success(f"** Predicted State:** {label}")
+        st.info(message)
+
+        if prediction == 1:
+            st.image(
+                "https://huggingface.co/spaces/DasariHarshitha/Sleep_Detection_App/resolve/main/th%20(1).jpeg",
+                use_container_width=True,
+            )
+            st.markdown("""
+            ###  🛏️ Personalized Sleep Tips
+             **Tips to Fall Asleep Faster**  
+            -  Avoid screens 30 mins before bed  
+            -  Keep the room cool and dark  
+            -  Try deep breathing or meditation  
+            -  Stick to a regular sleep schedule  
+            """)
+        else:
+            st.image(
+                "https://huggingface.co/spaces/DasariHarshitha/Sleep_Detection_App/resolve/main/cute-little-boy-wake-up-in-morning-stretching-hands-on-bed-in-bedroom-vector.jpg",
+                use_container_width=True,
+            )
+            st.markdown("""
+            ###  🌞 Tips to Wake Up Refreshed
+            -  Get morning sunlight exposure  
+            -  Move or stretch your body  
+            -  Eat a light, energizing breakfast  
+            -  Cold water splash or shower helps  
+            """)