null

app.py

@@ -19,12 +19,18 @@ def normalize(value, cast=None):
 
 
 def predict_job(job_data):
-    df = pd.DataFrame([job_data])
+    import numpy as np
+    
+    # Replace None with np.nan to ensure numeric dtype
+    clean_data = {k: (np.nan if v is None else v) for k, v in job_data.items()}
+    df = pd.DataFrame([clean_data])
+    
+    # Force all columns to float64 to match training data
+    df = df.astype('float64')
     
     # Predict
-    probability = model.predict_proba(df)[0][1]  # Probability of HIRED
+    probability = model.predict_proba(df)[0][1]
     prediction = "HIRED" if probability > 0.5 else "NOT_HIRED"
-    
     return {
         'prediction': prediction,
         'hire_probability': f"{probability*100:.1f}",