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from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from fastapi.encoders import jsonable_encoder
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from PIL import Image
import tensorflow.keras.backend as K
import os
import uvicorn
import numpy as np
# Initialize FastAPI app
app = FastAPI()
origins = ['*']
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Custom F1 score function
def f1_score(y_true, y_pred):
precision = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) / K.maximum(
K.sum(K.round(K.clip(y_pred, 0, 1))), K.epsilon()
)
recall = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) / K.maximum(
K.sum(K.round(K.clip(y_true, 0, 1))), K.epsilon()
)
return 2 * (precision * recall) / (precision + recall + K.epsilon())
# Load model
MODEL_PATH = "Trained_after_EFF0.keras" # Ensure the path is correct
model = load_model(MODEL_PATH, custom_objects={'f1_score': f1_score})
# Image size for the model
IMAGE_SIZE = 224
# Preprocess image
def preprocess_image(image_path, target_size):
image = load_img(image_path, target_size=(target_size, target_size))
image_array = img_to_array(image)
image_array = np.expand_dims(image_array, axis=0)
return image_array
# API to predict image
@app.post("/predict")
async def predict_image(file: UploadFile = File(...)):
try:
upload_dir = "./uploads"
os.makedirs(upload_dir, exist_ok=True)
file_path = os.path.join(upload_dir, file.filename)
with open(file_path, "wb") as buffer:
buffer.write(await file.read())
image_array = preprocess_image(file_path, target_size=IMAGE_SIZE)
prediction = model.predict(image_array)
predicted_label = int(np.argmax(prediction))
confidence = float(np.max(prediction))
os.remove(file_path)
return JSONResponse(
content=jsonable_encoder(
{"predicted_label": predicted_label, "confidence": confidence}
)
)
except Exception as e:
return JSONResponse(content=jsonable_encoder({"error": str(e)}), status_code=500)
# Run FastAPI server
if __name__ == '__main__':
uvicorn.run(app, host="0.0.0.0", port=8002)
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