Update app.py

app.py

@@ -3,7 +3,6 @@ from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 import joblib
 import numpy as np
-import pandas as pd
 
 # Load the trained model and scaler
 try:
@@ -41,21 +40,15 @@ class PCOSPrediction(BaseModel):
     prediction: int
     probability: float
 
-# Feature names for the model
-feature_names = ['Follicle_No_R', 'Follicle_No_L', 'Skin_darkening', 'hair_growth',
-                 'Weight_gain', 'Cycle_length', 'AMH', 'Fast_food', 'Cycle_R_I',
-                 'FSH_LH', 'PRL', 'Pimples', 'Age', 'BMI']
-
 # Define the prediction endpoint
 @app.post("/predict/", response_model=PCOSPrediction)
 def predict(data: PCOSInput):
     try:
-        # Convert input data to a DataFrame with feature names
-        input_data = pd.DataFrame([[data.Follicle_No_R, data.Follicle_No_L, data.Skin_darkening,
-                                     data.hair_growth, data.Weight_gain, data.Cycle_length, data.AMH,
-                                     data.Fast_food, data.Cycle_R_I, data.FSH_LH, data.PRL, data.Pimples,
-                                     data.Age, data.BMI]], columns=feature_names)
-
+        # Convert input data to array
+        input_data = np.array([[data.Follicle_No_R, data.Follicle_No_L, data.Skin_darkening, data.hair_growth,
+                                data.Weight_gain, data.Cycle_length, data.AMH, data.Fast_food, data.Cycle_R_I,
+                                data.FSH_LH, data.PRL, data.Pimples, data.Age, data.BMI]])
+        
         # Scale the input data using the loaded scaler
         scaled_input = scaler.transform(input_data)
 
@@ -65,4 +58,4 @@ def predict(data: PCOSInput):
 
         return PCOSPrediction(prediction=int(prediction[0]), probability=probability)
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Prediction error: {e}")
+        raise HTTPException(status_code=500, detail=f"Prediction error: {e}")