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from flask import Flask, request, jsonify ,render_template , redirect
from pydantic import BaseModel
import pickle
import json
import pandas as pd
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.inception_v3 import preprocess_input
import numpy as np
import os
import gdown
import lightgbm as lgb
from PIL import Image
app = Flask(__name__)
id = "1ry4L9L1-kyc79F1MnYMemJ5P81Gr_mHP"
output = "model_flowers_classification.h5"
gdown.download(id=id, output=output, quiet=False)
crop_disease_ml=load_model('model_flowers_classification.h5')
@app.route("/upload-image", methods=["POST"])
def upload_image():
# if request.method == "POST":
if request.files:
imag = request.files["image"]
try:
contents = imag.file.read()
with open(imag.filename, 'wb') as f:
f.write(contents)
except Exception:
return {"message": "There was an error uploading the file"}
finally:
imag.file.close()
print(imag)
classes = ['Lilly','Lotus','Orchid','Sunflower', 'Tulip']
img=image.load_img(str(imag.filename),target_size=(224,224))
x=image.img_to_array(img)
x=x/255
img_data=np.expand_dims(x,axis=0)
prediction = crop_disease_ml.predict(img_data)
predictions = list(prediction[0])
max_num = max(predictions)
index = predictions.index(max_num)
print(classes[index])
os.remove(str(imag.filename))
return {"output":classes[index]}
if __name__ =="__main__":
app.run() |