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This is a Flask web application that reads a pickle file to load a machine learning model,
accepts user input from a web form, and returns a prediction using the model.
from flask import Flask, render_template, request
import pickle
import numpy as np
# create flask app
app = Flask(__name__)
# Load the pickle model file
def load_model():
return pickle.load(open("model.pkl", "rb"))
return None
model = load_model()
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if model is None:
error_message = "Sorry, an error occurred while loading the model. Please try again later."
return render_template('index.html', result=error_message)
# Get the features from the request form and make a prediction
features = [float(x) for x in request.form.values()]
final_features = [np.array(features)]
prediction = model.predict(final_features)
result = round(prediction[0], 1)
# Create a result string to show to the user
result_string = f"The predicted diabetes outcome is {result}."
return render_template('index.html', result=result_string)
if __name__ == "__main__":