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from fastapi import FastAPI
from pydantic import BaseModel
import joblib
from huggingface_hub import hf_hub_download
import pandas as pd
# Define the FastAPI application
app = FastAPI()
# Download and load the model
# Replace "josequinonez/PIMA-Diabetes-Prediction-FastAPI" with your actual Hugging Face repo ID if different
latest_name = "best_pima_diabetes_model_latest.joblib"
model_path = hf_hub_download(repo_id="josequinonez/PIMA-Diabetes-Prediction-FastAPI",
filename=latest_name, repo_type="model")
model = joblib.load(model_path)
# Define the input data structure using Pydantic
class DiabetesFeatures(BaseModel):
preg: int
plas: int
pres: int
skin: int
test: int
mass: float
pedi: float
age: int
# Define the prediction endpoint
@app.post("/predict/")
async def predict_diabetes(features: DiabetesFeatures):
# Convert input features to a pandas DataFrame
input_data = pd.DataFrame([features.model_dump()])
# Make prediction
prediction = model.predict(input_data)[0]
# Return the prediction result
result = "Diabetic" if prediction == 1 else "Non-Diabetic"
return {"prediction": result}
# Optional: Add a root endpoint for testing
@app.get("/")
async def read_root():
return {"message": "PIMA Diabetes Prediction API is running!"}
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