Update app.py from Colab

app.py

@@ -6,8 +6,10 @@ import pandas  # For tabular data handling
 import gradio  # For interactive UI
 import huggingface_hub  # For downloading model assets
 import autogluon.tabular  # For loading and running AutoGluon predictors
-
+from huggingface_hub import HfApi
 # Settings
+api = HfApi()
+
 MODEL_REPO_ID = "jennifee/classical_automl_model"
 ZIP_FILENAME  = "autogluon_predictor_dir.zip"
 CACHE_DIR = pathlib.Path("hf_assets")
@@ -22,6 +24,7 @@ FEATURE_COLS = ['phone_hours',
 ]
 TARGET_COL = "sleep_quality"
 # Encoding for likert questions
+# Encoding for likert questions
 LIKERT5_LABELS = ["Never", "Rarely", "Sometimes", "Often", "Very Often"]
 LIKERT5_MAP = {label: idx for idx, label in enumerate(LIKERT5_LABELS)}
 
@@ -77,9 +80,9 @@ def do_predict(phone_hours, computer_hours, device_count, use_before_bed_label,
         FEATURE_COLS[0]: float(phone_hours),
         FEATURE_COLS[1]: float(computer_hours),
         FEATURE_COLS[2]: int(device_count),
-        FEATURE_COLS[4]: int(use_before_bed_code), # Index 4 for 'use_before_bed'
-        FEATURE_COLS[5]: float(sleep_time),
-        FEATURE_COLS[6]: float(sleep_hours),
+        FEATURE_COLS[3]: int(use_before_bed_code), # Index 3 for 'use_before_bed'
+        FEATURE_COLS[4]: float(sleep_time),
+        FEATURE_COLS[5]: float(sleep_hours),
     }
     X = pandas.DataFrame([row], columns=[col for col in FEATURE_COLS if col != TARGET_COL]) # Exclude target column from input
 
@@ -147,11 +150,11 @@ with gradio.Blocks() as demo:
         device_count = gradio.Number(value=3, precision=0, label=FEATURE_COLS[2])
 
     with gradio.Row():
-        use_before_bed_label = gradio.Radio(choices=LIKERT5_LABELS, value="Sometimes", label=FEATURE_COLS[4])
+        use_before_bed_label = gradio.Radio(choices=LIKERT5_LABELS, value="Sometimes", label=FEATURE_COLS[3]) # Corrected index to 3
 
     with gradio.Row():
-        sleep_time = gradio.Slider(0, 24, step=0.1, value=23.0, label=FEATURE_COLS[5])
-        sleep_hours = gradio.Slider(0, 12, step=0.1, value=7.0, label=FEATURE_COLS[6])
+        sleep_time = gradio.Slider(0, 24, step=0.1, value=23.0, label=FEATURE_COLS[4]) # Corrected index to 4
+        sleep_hours = gradio.Slider(0, 12, step=0.1, value=7.0, label=FEATURE_COLS[5]) # Corrected index to 5
 
 
     proba_pretty = gradio.Label(num_top_classes=2, label="Class probabilities") # Changed to 2 classes