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app.py

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+from fastapi import FastAPI, UploadFile, File
+import uvicorn
+import tensorflow as tf
+from tensorflow import keras
+from keras import models
+from PIL import Image
+from io import BytesIO
+import numpy as np
+import cv2
+
+# Some constants to be used in the program
+IMG_SIZE = (32,32)
+APP_HOST = '127.0.1.1'
+APP_PORT = '5000'
+
+# Character mapping for the character prediction
+char_map = {
+    0:'𑑐(0)',   1:'𑑑(1)',    2:'𑑒(2)',   3:'𑑓(3)',      4: '𑑔(4)',     5: '𑑕(5)',    6: '𑑖(6)',    7: '𑑗(7)',
+    8:'𑑘(8)',   9:'𑑙(9)',    10:'𑑉(OM)', 11:'𑐀(A)',    12: '𑐁(AA)',   13: '𑐀𑑅(AH)',  14: '𑐂(I)',    
+    15:'𑐃(II)',16:'𑐄(U)',   17:'𑐅(UU)',  18:'𑐆(R)',    19: '𑐆𑐺(RR)',  20: '𑐊(E)',   21: '𑐋(AI)',    22: '𑐌(O)',    
+    23:'𑐍(AU)', 24:'𑐈(L)',  25:'𑐉(LL)',   26:'𑐎(KA)',   27: '𑐎𑑂𑐳(KSA)', 28: '𑐏(KHA)',29: '𑐐(GA)',    30: '𑐑(GHA)',    
+    31:'𑐒(NGA)',32:'𑐔(CA)',  33:'𑐕(CHA)', 34:'𑐖(JA)',   35: '𑐖𑑂𑐘(JñA)',  36: '𑐗(JHA)',37: '𑐗(JHA-alt)',38: '𑐘(NYA)',    
+    39:'𑐚(TA)', 40:'𑐛(TTHA)', 41:'𑐜(DDA)', 42:'𑐝(DHA)',  43: '𑐞(NNA)', 44: '𑐟(TA)',  45: '𑐟𑑂𑐬(TRA)',    46: '𑐠(THA)',
+    47:'𑐡(DA)', 49:'𑐣(NA)',   50:'𑐥(PA)',  51:'𑐦(PHA)',  52: '𑐧(BA)',  53: '𑐨(BHA)',  54: '𑐩(MA)',    55: '𑐫(YA)', 
+    56:'𑐬(RA)', 57: '𑐮(LA)', 58:'𑐰(WA)', 59:'𑐱(SHA)',    60: '𑐱(SHA-alt)', 61: '𑐲(SSA)',    62: '𑐳(SA)', 63: '𑐴(HA)'
+}
+
+
+
+# Importing the model
+model = models.load_model('vgg.h5')
+
+
+# Defining the FastAPI instance here
+app = FastAPI()
+
+# Function for reading image
+def file_to_array(data) -> np.ndarray:
+    image = np.array(Image.open(BytesIO(data)))
+
+    return image 
+
+
+@app.get('/')
+async def root_func():
+    return {'message': 'this is the root function'}
+
+
+@app.post('/predict_image')
+async def upload_image(file: UploadFile = File(...)):
+    image  = Image.open(BytesIO(await file.read()))
+
+    image  = cv2.resize(np.array(image), IMG_SIZE)
+    image  = image.astype('float32')
+    image  = np.expand_dims(image, axis=0)
+
+    output = model.predict(image)
+    result = char_map[np.argmax(output)]
+    
+    return {'prediction': result}
+
+
+    
+if __name__ == "__main__":
+    uvicorn.run(app, host=APP_HOST, port=APP_PORT)

requirements.txt

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+absl-py==1.4.0
+anyio==3.6.2
+astunparse==1.6.3
+cachetools==5.3.0
+certifi==2022.12.7
+charset-normalizer==3.0.1
+click==8.1.3
+fastapi==0.92.0
+flatbuffers==23.1.21
+gast==0.4.0
+google-auth==2.16.1
+google-auth-oauthlib==0.4.6
+google-pasta==0.2.0
+grpcio==1.51.3
+gunicorn==20.1.0
+h11==0.14.0
+h5py==3.8.0
+httptools==0.5.0
+idna==3.4
+keras==2.11.0
+libclang==15.0.6.1
+Markdown==3.4.1
+MarkupSafe==2.1.2
+multipart==0.2.4
+numpy==1.24.2
+oauthlib==3.2.2
+opencv-python==4.7.0.72
+opt-einsum==3.3.0
+packaging==23.0
+protobuf==3.19.6
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pydantic==1.10.5
+python-dotenv==1.0.0
+PyYAML==6.0
+requests==2.28.2
+requests-oauthlib==1.3.1
+rsa==4.9
+six==1.16.0
+sniffio==1.3.0
+starlette==0.25.0
+tensorboard==2.11.2
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorflow==2.11.0
+tensorflow-estimator==2.11.0
+tensorflow-io-gcs-filesystem==0.31.0
+termcolor==2.2.0
+typing_extensions==4.5.0
+urllib3==1.26.14
+uvicorn==0.20.0
+uvloop==0.17.0
+watchfiles==0.18.1
+websockets==10.4
+Werkzeug==2.2.3
+wrapt==1.15.0

vgg.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3da1c33e2d6095b0705c84e44993387e92a4b9e8b3d5e753c31f44b3c96ef97c
+size 16506024