Upload predict.py with huggingface_hub

predict.py

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+import pickle
+import pandas as pd
+import numpy as np
+import os
+from huggingface_hub import hf_hub_download
+
+# Constants
+REPO_ID = "RealFishSam/DVAE26-proj"
+FILENAME = "stacked_ensemble_model.pkl"
+
+# Check locations
+possible_paths = [
+    FILENAME,
+    os.path.join('models', FILENAME),
+    os.path.join('..', 'models', FILENAME)
+]
+
+model_path = None
+for p in possible_paths:
+    if os.path.exists(p):
+        model_path = p
+        break
+
+with open(model_path, 'rb') as f:
+    components = pickle.load(f)
+
+preprocessor = components['preprocessor']
+base_models = components['base_models']
+meta_model = components['meta_model']
+threshold = components.get('threshold_stacked', 0.5)
+
+patient = pd.DataFrame([{
+    'gender': 'Male', # one of ['Male', 'Female'] # Other was dropped
+    'age': 75,
+    'hypertension': 1,
+    'heart_disease': 1,
+    'ever_married': 'Yes', # one of ['Yes', 'No']
+    'work_type': 'Private', # one of ['Private', 'Self-employed', 'Govt_job', 'Children', 'Never_worked']   
+    'Residence_type': 'Urban', # one of ['Urban', 'Rural']
+    'avg_glucose_level': 220.5,
+    'bmi': 30.1,
+    'smoking_status': 'formerly smoked' # one of ['formerly smoked', 'never smoked', 'smokes']
+}])
+
+# 1. Preprocess
+X = preprocessor.transform(patient)
+
+# 2. Base model predictions
+preds = []
+for name, m in base_models.items():
+    p = m.predict_proba(X)[:, 1]
+    preds.append(p)
+
+# 3. Meta prediction
+meta_X = np.column_stack(preds)
+final_prob = meta_model.predict_proba(meta_X)[:, 1][0]
+
+print(f"Stroke Probability: {final_prob}")