panditamey/flowerClassification

1from fastapi import FastAPI,UploadFile,File2from fastapi.middleware.cors import CORSMiddleware3 4from pydantic import BaseModel5import pickle6import json7import pandas as pd8from tensorflow.keras.models import load_model9from tensorflow.keras.preprocessing import image10from tensorflow.keras.applications.inception_v3 import preprocess_input11import numpy as np12import os13import gdown14import lightgbm as lgb15from PIL import Image16 17CHUNK_SIZE = 102418 19app = FastAPI(20    title='Flower Classification API',21    description='API for Flower Classification',22)23origins = ["*"]24 25app.add_middleware(26    CORSMiddleware,27    allow_origins=origins,28    allow_credentials=True,29    allow_methods=["*"],30    allow_headers=["*"],31)32 33id = "1ry4L9L1-kyc79F1MnYMemJ5P81Gr_mHP"34output = "model_flowers_classification.h5"35gdown.download(id=id, output=output, quiet=False)36# from zipfile import ZipFile37# with ZipFile("modelcrops.zip", 'r') as zObject:38#     zObject.extractall(39#         path="")40    41 42predict_ml=load_model('model_flowers_classification.h5')43 44 45@app.post('/predict')46async def flowerpredict(file: UploadFile = File(...)):47    try:48        contents = file.file.read()49        with open(file.filename, 'wb') as f:50            f.write(contents)51    except Exception:52        return {"message": "There was an error uploading the file"}53    finally:54        file.file.close()55    classes = ['Lilly','Lotus','Orchid','Sunflower', 'Tulip']56    img=image.load_img(str(file.filename),target_size=(224,224))57    x=image.img_to_array(img)58    x=x/25559    img_data=np.expand_dims(x,axis=0)60    prediction = predict_ml.predict(img_data)61    predictions = list(prediction[0])62    max_num = max(predictions)63    index = predictions.index(max_num)64    print(classes[index])65    os.remove(str(file.filename))66    return {"output":classes[index]}