Update app.py

app.py

@@ -1,37 +1,21 @@
 import streamlit as st
-import pandas as pd
-import joblib
+import numpy as np
+import joblib  # use joblib instead of pickle
 
-# Load model
-model = joblib.load("log_reg_model.pkl")  # or log_reg_model.pkl
+# Load model with joblib
+model = joblib.load("log_reg_model.pkl")
 
-st.title("Sleep State Prediction App")
+st.title("AI Sleep State Detection")
+st.markdown("Enter **angle** and **enmo** to predict the sleep state:")
 
-# Upload test data
-uploaded_file = st.file_uploader("Upload test_series.parquet", type=["parquet"])
-if uploaded_file is not None:
-    test_df = pd.read_parquet(uploaded_file)
-    st.write("Sample of uploaded data:")
-    st.dataframe(test_df.head())
+angle = st.number_input("Angle", min_value=0.0, max_value=360.0, step=0.1)
+enmo = st.number_input("ENMO", min_value=0.0, max_value=2.0, step=0.01)
 
-    # Check if required columns exist
-    if {'series_id', 'step', 'hour'}.issubset(test_df.columns):
-        # Predict sleep state
-        features = test_df[['step', 'hour']]
-        predictions = model.predict(features)
-
-        # Build submission-like DataFrame
-        test_df = test_df.reset_index(drop=True)
-        test_df["event"] = predictions
-        test_df["row_id"] = test_df["series_id"] + "_" + test_df.index.astype(str)
-        submission = test_df[["row_id", "event"]]
-
-        st.success("Predictions completed.")
-        st.write(submission.head())
-
-        # Option to download
-        csv = submission.to_csv(index=False).encode('utf-8')
-        st.download_button("Download submission.csv", csv, "submission.csv", "text/csv")
+if st.button("Detect Sleep State"):
+    input_data = np.array([[angle, enmo]])
+    prediction = model.predict(input_data)[0]
 
+    if prediction == 0:
+        st.success("Sleep State: **Wakeup**")
     else:
-        st.error("Uploaded file must contain 'series_id', 'step', and 'hour' columns.")
+        st.success("Sleep State: **Onset**")