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from fastapi import FastAPI,UploadFile,File
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
CHUNK_SIZE = 1024
app = FastAPI(
title='Flower Classification API',
description='API for Flower Classification',
)
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
id = "1ry4L9L1-kyc79F1MnYMemJ5P81Gr_mHP"
output = "model_flowers_classification.h5"
gdown.download(id=id, output=output, quiet=False)
# from zipfile import ZipFile
# with ZipFile("modelcrops.zip", 'r') as zObject:
# zObject.extractall(
# path="")
predict_ml=load_model('model_flowers_classification.h5')
@app.post('/predict')
async def flowerpredict(file: UploadFile = File(...)):
try:
contents = file.file.read()
with open(file.filename, 'wb') as f:
f.write(contents)
except Exception:
return {"message": "There was an error uploading the file"}
finally:
file.file.close()
classes = ['Lilly','Lotus','Orchid','Sunflower', 'Tulip']
img=image.load_img(str(file.filename),target_size=(224,224))
x=image.img_to_array(img)
x=x/255
img_data=np.expand_dims(x,axis=0)
prediction = predict_ml.predict(img_data)
predictions = list(prediction[0])
max_num = max(predictions)
index = predictions.index(max_num)
print(classes[index])
os.remove(str(file.filename))
return {"output":classes[index]} |