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from fastapi import FastAPI
import pickle
from pydantic import BaseModel
import streamlit as st
import requests
app = FastAPI()
pickle_in = open("classifier.pkl", "rb")
classifier = pickle.load(pickle_in)
class Classe(BaseModel):
Sepal_Length: float
Sepal_Width: float
Petal_Length: float
Petal_Width: float
@app.get("/")
def index():
return {"hello": "FastAPI"}
@app.get('/{name}')
def get_name(name: str):
return {'message': f'hello, {name}'}
@app.post('/predict')
def predict_species(data: Classe):
Sepal_Length = data.Sepal_Length
Sepal_Width = data.Sepal_Width
Petal_Length = data.Petal_Length
Petal_Width = data.Petal_Width
prediction = classifier.predict([[Sepal_Length, Sepal_Width, Petal_Length, Petal_Width]])
if prediction[0] == 0:
species = "setosa"
elif prediction[0] == 1:
species = "virginica"
elif prediction[0] == 2:
species = "versicolor"
else:
species = "unknown"
return {'prediction': species}
if __name__ == "__main__":
import uvicorn
import subprocess
uvicorn_proc = subprocess.Popen(["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
st.title("Iris Species Prediction")
st.subheader("Enter the following parameters:")
sepal_length = st.number_input("Sepal Length", min_value=0.0, max_value=10.0, step=0.1, value=5.0)
sepal_width = st.number_input("Sepal Width", min_value=0.0, max_value=10.0, step=0.1, value=3.5)
petal_length = st.number_input("Petal Length", min_value=0.0, max_value=10.0, step=0.1, value=1.4)
petal_width = st.number_input("Petal Width", min_value=0.0, max_value=10.0, step=0.1, value=0.2)
submit = st.button("Predict")
if submit:
payload = {"Sepal_Length": sepal_length, "Sepal_Width": sepal_width, "Petal_Length": petal_length, "Petal_Width": petal_width}
prediction = st.empty()
with st.spinner("Predicting..."):
response = requests.post("http://localhost:8000/predict", json=payload)
if response.status_code == 200:
prediction_result = response.json()
prediction.success(f"Prediction: {prediction_result['prediction']}")
else:
prediction.error("Prediction failed.")
uvicorn_proc.kill()
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