def get_learner(
model_url="https://storage.googleapis.com/sv-fastai/models/apthunt/20200503_cltab.pkl"
):
print(f"downloading model from {model_url}")
path = maybe_download_model(model_url)
learn = load_learner(path)
return learn
def predict(img):
# Display the test image
st.image(img, use_column_width=True)
# Temporarily displays a message while executing
with st.spinner('Wait for it...'):
time.sleep(3)
# Load model and make prediction
learn_inf = load_learner('export.pkl')
pred, pred_idx, probs = learn_inf.predict(img)
st.success(f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}')
def load_learner_from_s3(pkl_name, update_ml=server_config.UPDATE_ML):
"""Import the latest trained model from S3
Keyword Arguments:
pkl_name {str} -- Name of pickled model to use (default: {'0_18_512_0.722.pkl'})
Returns:
fastai2_learner -- A learner with the model already loaded into memory
"""
pkl_path = Path(path_config.LOCAL_DIR) / "models" / pkl_name
if server_config.VERBOSE:
print("Loading Learner")
if update_ml:
clear(pkl_path)
if not pkl_path.exists(): # pylint: disable=no-member
src_path = "s3://skytruth-cerulean/model_artifacts/" + str(pkl_name)
download_str = f"aws s3 cp {src_path} {pkl_path}"
# print(download_str)
run(download_str, shell=True)
return load_learner(pkl_path)
from flask import Flask, jsonify, request
from flask import render_template
from flask_sockets import Sockets
from flask_cors import CORS
from fastai2.learner import load_learner
from dotenv import load_dotenv
load_dotenv()
app = Flask(__name__)
CORS(app)
sockets = Sockets(app)
trump = load_learner('model.pkl')
@app.route('/')
def index():
response = jsonify({'status': 'ok'})
response.status_code = 200
return response
@app.route('/api/1.0/classify-image', methods=['POST'])
def classify():
image = request.files['image']
res = trump.predict(image.read())
response = jsonify({'result': res[0]})
response.status_code = 200
#https://www.roytuts.com/upload-and-display-image-using-python-flask/
import os
import uuid
import urllib.request
from fastai2.learner import load_learner
from flask import Flask, flash, request, redirect, url_for, render_template
app = Flask(__name__)
learner_inference = load_learner('../models/coins.pkl')
@app.route('/')
def upload_form():
return render_template('upload.html')
@app.route('/', methods=['POST'])
def upload_image():
if 'file' not in request.files:
return redirect(request.url)
file = request.files['file']
if file.filename == '':
return redirect(request.url)
if file:
filename = str(uuid.uuid4())
filepath = os.path.join('./static/uploads', filename)
file.save(filepath)