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from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
import numpy as np
import cv2
import pickle
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
app = FastAPI()
print("app run")
# Load the model and the label binarizer
model = load_model('cnn_model.h5')
print("model loaded")
label_binarizer = pickle.load(open('label_transform.pkl', 'rb'))
print("labels loaded")
# Function to convert images to array
def convert_image_to_array(image_dir):
try:
image = cv2.imdecode(np.frombuffer(image_dir, np.uint8), cv2.IMREAD_COLOR)
if image is not None:
image = cv2.resize(image, (256, 256))
return img_to_array(image)
else:
return np.array([])
except Exception as e:
print(f"Error : {e}")
return None
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
try:
# Read the file and convert it to an array
image_data = await file.read()
image_array = convert_image_to_array(image_data)
if image_array.size == 0:
return JSONResponse(content={"error": "Invalid image"}, status_code=400)
# Normalize the image
image_array = np.array(image_array, dtype=np.float16) / 255.0
# Ensure the image_array has the correct shape (1, 256, 256, 3)
image_array = np.expand_dims(image_array, axis=0)
# Make a prediction
prediction = model.predict(image_array)
predicted_class = label_binarizer.inverse_transform(prediction)[0]
return {"prediction": predicted_class}
except Exception as e:
return JSONResponse(content={"error": str(e)}, status_code=500)
# Add a test GET endpoint to manually trigger the prediction
@app.get("/test-predict")
def test_predict():
try:
image_path = 'crop_image1.jpg'
image = cv2.imread(image_path)
image_array = cv2.resize(image, (256, 256))
image_array = img_to_array(image_array)
if image_array.size == 0:
return JSONResponse(content={"error": "Invalid image"}, status_code=400)
# Normalize the image
image_array = np.array(image_array, dtype=np.float16) / 255.0
# Ensure the image_array has the correct shape (1, 256, 256, 3)
image_array = np.expand_dims(image_array, axis=0)
# Make a prediction
prediction = model.predict(image_array)
predicted_class = label_binarizer.inverse_transform(prediction)[0]
return {"prediction": predicted_class}
except Exception as e:
return JSONResponse(content={"error": str(e)}, status_code=500)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="127.0.0.1", port=8000)
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