Update projects/ML_DiabetesPrediction/ML_DiabetesPrediction.py

projects/ML_DiabetesPrediction/ML_DiabetesPrediction.py

@@ -1,34 +1,15 @@
-from projects.ML_StudentPerformance.src.pipelines.predict_pipeline import CustomData, PredictPipeline
-from pydantic import BaseModel
-
-# Function to handle the prediction logic
-def predict_student_performance(data):
-    # Convert the incoming form data to a DataFrame
-    pred_df = data.get_data_as_dataframe()
-
-    # Initialize the prediction pipeline
-    predict_pipeline = PredictPipeline()
-    results = predict_pipeline.predict(pred_df)
-
-    return results[0]  # Return the first prediction result
-
-# Function to handle form data conversion
-def create_custom_data(gender, ethnicity, parental_level_of_education, lunch, test_preparation_course, reading_score, writing_score):
-    return CustomData(
-        gender=gender,
-        race_ethnicity=ethnicity,
-        parental_level_of_education=parental_level_of_education,
-        lunch=lunch,
-        test_preparation_course=test_preparation_course,
-        reading_score=float(reading_score),
-        writing_score=float(writing_score)
-    )
-
-class form1(BaseModel):
-    gender: str
-    ethnicity: str
-    parental_level_of_education: str
-    lunch: str
-    test_preparation_course: str
-    reading_score: float
-    writing_score: float
+from pydantic import BaseModel
+import pickle
+
+# loading the saved model
+model_ML_DiabetesPrediction = pickle.load(open('./projects/ML_DiabetesPrediction/model_DiabetesPrediction.sav', 'rb'))
+
+class form2(BaseModel):
+    Pregnancies: int
+    Glucose: int
+    BloodPressure: int
+    SkinThickness: int
+    Insulin: int
+    BMI: float
+    DiabetesPedigreeFunction: float
+    Age: int