Create app.py

app.py

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+# enhanced_interactive_sleep_predictor.py
+
+import streamlit as st
+import pandas as pd
+import joblib
+import matplotlib.pyplot as plt
+from langchain_google_genai import GoogleGenerativeAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+# Load your trained model
+@st.cache_resource
+def load_model():
+    return joblib.load("sleep_model.pkl")  # Update with your actual model path
+
+model = load_model()
+
+# LangChain Setup
+api_key = st.secrets.get('genai_key')
+llm= GoogleGenerativeAI(model="gemini-1.5-pro", google_api_key=api_key)  # or "gpt-4", based on your requirement
+
+# Health tips prompt template
+prompt_template = """
+You are a health advisor. Based on the sleep duration (in hours), give personalized sleep and health tips.
+Sleep duration: {sleep_duration} hours.
+Health Tip:
+"""
+
+# LangChain chain for generating health tips
+def generate_health_tips(sleep_duration):
+    prompt = PromptTemplate(input_variables=["sleep_duration"], template=prompt_template)
+    chain = LLMChain(llm=llm, prompt=prompt)
+    return chain.run({"sleep_duration": sleep_duration})
+
+# Streamlit UI Enhancements
+st.set_page_config(page_title="Interactive Sleep Predictor", layout="wide")
+st.title("🛌 **Interactive Sleep Predictor**")
+st.markdown("""
+    ## Get Personalized Sleep & Health Predictions
+    Enter your step count and current hour to predict your sleep/wake status and receive personalized health tips and exercise suggestions.
+""")
+
+# User input form
+with st.form("predictor_form"):
+    step = st.number_input("🚶 **Step Count** (Enter your current steps)", min_value=0, step=10, label_visibility="visible")
+    hour = st.slider("⏰ **Hour of the Day** (24h format)", min_value=0, max_value=23, label_visibility="visible")
+    submit_button = st.form_submit_button("Predict Sleep/Wake State")
+
+# When the user clicks the predict button
+if submit_button:
+    # Prepare input for the model
+    input_df = pd.DataFrame([[step, hour]], columns=["step", "hour"])
+
+    # Predict sleep state (0 = awake, 1 = asleep)
+    prediction = model.predict(input_df)[0]
+
+    # Calculate sleep duration (use 8 hours if asleep, 0 if awake)
+    sleep_duration = 8 if prediction == 1 else 0
+
+    # Display prediction
+    if prediction == 1:
+        st.success(f"😴 **You are likely asleep**. You might sleep for **{sleep_duration} hours**.")
+    else:
+        st.info(f"🌞 **You are likely awake**. Stay active and hydrated today!")
+
+    # Generate and display health tips based on sleep duration
+    health_tips = generate_health_tips(sleep_duration)
+    st.markdown("### Health Tips:")
+    st.write(f"**{health_tips}**")
+
+    # Exercise Tips based on the user's state (asleep vs awake)
+    if prediction == 0:  # Awake
+        exercise_tips = """
+        - 🏃‍♂️ **Go for a walk or jog**: 20-30 minutes of aerobic exercise can boost your energy.
+        - 🧘‍♀️ **Stretch or do yoga**: Focus on flexibility and relaxation to stay active.
+        - 🏋️‍♂️ **Strength Training**: Include bodyweight exercises like squats and push-ups.
+        """
+    else:  # Asleep
+        exercise_tips = "Rest is the best exercise. Ensure you are getting quality sleep for good health."
+
+    st.markdown("### Exercise Suggestions:")
+    st.write(f"**{exercise_tips}**")
+
+    # Visual representation: Sleep Prediction Bar Chart
+    fig, ax = plt.subplots(figsize=(8, 4))
+    ax.barh(["Predicted Sleep"], sleep_duration, color="lightblue")
+    ax.set_xlim(0, 10)  # Limit max sleep time
+    ax.set_xlabel("Hours of Sleep")
+    ax.set_title("Predicted Sleep Duration")
+    st.pyplot(fig)
+
+# # Footer for the app
+# st.markdown("---")
+# st.markdown("""
+#     Built with ❤️ by **Pavan Manikanta Muthyala** | **Interactive Sleep Predictor** using LangChain and GenAI
+# """)
+