main.py

# 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('/', response_class=HTMLResponse)
async def html():
    content = open('static/index.html', 'r') 
    return content.read()