Update components/predict.py

components/predict.py

@@ -4,41 +4,33 @@ import pickle
 import numpy as np
 from utils.db import log_prediction
 
-# Load the machine learning model from the file
 try:
     model = pickle.load(open("model.pkl", "rb"))
 except FileNotFoundError:
-    raise RuntimeError("The 'model.pkl' file was not found. Please run the 'create_dummy_model.py' script first.")
-
+    raise RuntimeError("The 'model.pkl' file was not found.")
 
 def predict(pregnancies, glucose, blood_pressure, insulin, bmi, age, user_state):
-    """
-    Performs diabetes prediction based on user input.
-    """
-    # First, check if the user is logged in by checking the user_state
     if not user_state.get("logged_in"):
-        return "❌ Please log in on the 'Login / Signup' tab before making a prediction."
-
-    # Validate inputs
-    if any(v is None for v in [pregnancies, glucose, blood_pressure, insulin, bmi, age]):
-        return "❌ Please fill in all the fields."
-
-    # Prepare the data for the model
-    input_data = [pregnancies, glucose, blood_pressure, insulin, bmi, age]
+        return "❌ Please log in first."
+    
+    # If pregnancies field is hidden (None), default to 0 for the model
+    pregnancies_value = pregnancies if pregnancies is not None else 0
+    
+    if any(v is None for v in [glucose, blood_pressure, insulin, bmi, age]):
+        return "❌ Please fill in all the non-optional fields."
+    
+    input_data = [pregnancies_value, glucose, blood_pressure, insulin, bmi, age]
     data_np = np.array(input_data).reshape(1, -1)
-
-    # Make the prediction
+    
     try:
         prediction = model.predict(data_np)[0]
         result_text = "Diabetic" if prediction == 1 else "Not Diabetic"
-        
-        # Format the result with an emoji
         final_result = f"✅ Result: {result_text}"
-
-        # Log the prediction to the database in the background
-        log_prediction(input_data, result_text, user_state["email"])
+        
+        # Log prediction if user ID exists
+        if user_state.get("id"):
+            log_prediction(input_data, result_text, user_state["id"])
         
         return final_result
-
     except Exception as e:
         return f"An error occurred during prediction: {e}"