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# app/main.py
from fastapi import FastAPI
from pydantic import BaseModel
from joblib import load
import numpy as np
from fastapi.responses import HTMLResponse
# Define FastAPI app
app = FastAPI()
# Load the trained model
model = load("model.joblib")
# Define request body schema using Pydantic BaseModel
class Item(BaseModel):
sepal_length: float
sepal_width: float
petal_length: float
petal_width: float
# Define endpoint to make predictions
@app.post("/predict")
async def predict(item: Item):
# Convert input to array
input_data = [item.sepal_length, item.sepal_width, item.petal_length, item.petal_width]
input_array = np.array([input_data])
# Make prediction
prediction = model.predict(input_array)[0]
# Map prediction to class label
class_label = {0: "setosa", 1: "versicolor", 2: "virginica"}
predicted_class = class_label[prediction]
# Return prediction
return {"predicted_class": predicted_class}
@app.get('/app', response_class=HTMLResponse)
async def html():
content = open('static/index.html', 'r')
return content.read()
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