Create api.py

api.py

@@ -0,0 +1,55 @@
+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',
+)
+
+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 cropdisease(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]}