Update app.py

app.py

@@ -1,20 +1,37 @@
 import streamlit as st
+import pandas as pd
 import joblib
-import numpy as np
 
-# Load your model
-model = joblib.load("log_reg_model.pkl")  # or "log_reg_model.pkl"
+# Load model
+model = joblib.load("log_reg_model.pkl")  # or log_reg_model.pkl
 
-# Streamlit App UI
-st.title("AI Sleep State Detector")
-st.write("Predict sleep state (`onset` or `wakeup`) using step count and hour.")
+st.title("Sleep State Prediction App")
 
-# Input Features
-step = st.number_input("Step count:", min_value=0, max_value=10000, value=0)
-hour = st.number_input("Hour of day (0–23):", min_value=0, max_value=23, value=0)
+# 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())
 
-# Predict Button
-if st.button("Predict Sleep State"):
-    input_data = np.array([[step, hour]])
-    prediction = model.predict(input_data)[0]
-    return "Sleep Onset" if prediction == 1 else "Wakeup"
+    # 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")
+
+    else:
+        st.error("Uploaded file must contain 'series_id', 'step', and 'hour' columns.")