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716d566 465b08b 40c7f6b 716d566 465b08b 716d566 40c7f6b 7ef829f 40c7f6b 465b08b 40c7f6b 716d566 de77399 465b08b 716d566 465b08b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
import joblib
import numpy as np
# Load the trained model and scaler
try:
model = joblib.load('LR1.pkl') # Ensure model path is correct
scaler = joblib.load('scaler1.pkl') # Ensure scaler is saved properly
except Exception as e:
raise RuntimeError(f"Failed to load model or scaler: {e}")
app = FastAPI()
# Root endpoint
@app.get("/")
def read_root():
return {"message": "Welcome to the PCOS Prediction API!"}
# Define the input data structure
class PCOSInput(BaseModel):
Follicle_No_R: int = Field(..., ge=0, description="Number of follicles in the right ovary (>=0)")
Follicle_No_L: int = Field(..., ge=0, description="Number of follicles in the left ovary (>=0)")
Skin_darkening: int = Field(..., ge=0, le=1, description="Skin darkening (1: Yes, 0: No)")
hair_growth: int = Field(..., ge=0, le=1, description="Hair growth (1: Yes, 0: No)")
Weight_gain: int = Field(..., ge=0, le=1, description="Weight gain (1: Yes, 0: No)")
Cycle_length: int = Field(..., ge=1, description="Menstrual cycle length in days (>=1)")
AMH: float = Field(..., gt=0, description="Anti-Müllerian Hormone level (positive float)")
Fast_food: int = Field(..., ge=0, le=1, description="Fast food consumption (1: Yes, 0: No)")
Cycle_R_I: int = Field(..., ge=0, le=1, description="Regular/Irregular cycle (1: Regular, 0: Irregular)")
FSH_LH: float = Field(..., gt=0, description="FSH/LH ratio (positive float)")
PRL: float = Field(..., gt=0, description="Prolactin level (positive float)")
Pimples: int = Field(..., ge=0, le=1, description="Pimples (1: Yes, 0: No)")
Age: int = Field(..., ge=12, le=60, description="Age in years (between 12 and 60)")
BMI: float = Field(..., gt=0, description="Body Mass Index (positive float)")
# Define the prediction response structure
class PCOSPrediction(BaseModel):
prediction: int
probability: float
# Define the prediction endpoint
@app.post("/predict/", response_model=PCOSPrediction)
def predict(data: PCOSInput):
try:
# 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]])
# Ensure scaler and model work with the same feature shape
if input_data.shape[1] != scaler.n_features_in_:
raise ValueError(f"Input data shape mismatch. Expected {scaler.n_features_in_}, got {input_data.shape[1]}")
# Scale the input data using the loaded scaler
scaled_input = scaler.transform(input_data)
# Make prediction
prediction = model.predict(scaled_input)
probability = model.predict_proba(scaled_input)[0][1] # Probability for class 1 (PCOS)
return PCOSPrediction(prediction=int(prediction[0]), probability=probability)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Prediction error: {e}")
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