File size: 2,486 Bytes
9d942e2 afa7629 9d942e2 0dd2821 9d942e2 473663e afa7629 9d942e2 0dd2821 9d942e2 8ca1104 afa7629 9d942e2 452c489 9d942e2 452c489 9d942e2 e0a5ec3 9d942e2 672fa5c 473663e 672fa5c 9d942e2 2bd045c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
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
from flask_cors import CORS, cross_origin
app = Flask(__name__)
id = "1dPrnyH7y9ojSHaOOOTkbGkCnhwYvMxab"
output = "disease_new.h5"
gdown.download(id=id, output=output, quiet=False)
CORS(app)
app.config['CORS_HEADERS'] = 'Content-Type'
crop_disease_ml=load_model('disease_new.h5')
@app.route("/predict", methods=["POST"])
@cross_origin()
def upload_image():
# if request.method == "POST":
if request.files:
imag = request.files["image"]
try:
contents = imag.read()
with open(imag.filename, 'wb') as f:
f.write(contents)
except Exception:
return {"message": "There was an error uploading the file"}
finally:
imag.close()
print(imag)
classes = ['Pepper bell Bacterial spot', 'Pepper bell healthy', 'Potato Early blight', 'Potato Late blight', 'Potato healthy', 'Tomato Bacterial spot', 'Tomato Early blight', 'Tomato Late blight', 'Tomato Leaf Mold', 'Tomato Septoria leaf spot', 'Tomato Spider mites Two spotted spider mite', 'Tomato Target Spot', 'Tomato Tomato YellowLeaf Curl Virus', 'Tomato Tomato mosaic virus', 'Tomato healthy']
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))
response = jsonify(output=classes[index])
# response.headers.add('Access-Control-Allow-Origin', '*')
# response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
# response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
return response
if __name__ =="__main__":
app.run(debug=False,host="0.0.0.0",port=5000) |