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from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.responses import JSONResponse
from PIL import Image
import numpy as np
import cv2
import tensorflow as tf # Assuming you're using TensorFlow for loading your model
app = FastAPI()
# Load your model
model = tf.keras.models.load_model("Brain_tumor_pred_large.h5")
def predict_tumor(image: Image.Image):
# Convert the PIL image to OpenCV format
opencv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
img = cv2.resize(opencv_image, (128, 128))
img = img.reshape(1, 128, 128, 3)
# Predict using the model
predictions = model.predict(img)[0] # Get probabilities for each class
predicted_class = np.argmax(predictions) # Index of the predicted class
confidence = predictions[predicted_class] # Confidence of the predicted class
# Determine if a tumor is present
if confidence < 0.20:
if confidence < 0.10:
result = "No Tumor"
confidence = 1.0
else:
result = "Uncertain"
else:
result = "No Tumor" if predicted_class == 1 else "Tumor Detected"
return {"result": result, "confidence": f"{confidence:.2%}"}
@app.post("/predict")
async def predict(upload: UploadFile = File(...)):
try:
# Open and process the uploaded image file
image = Image.open(upload.file)
result = predict_tumor(image)
return JSONResponse(content=result)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
# To run the FastAPI app, use the following command:
# uvicorn app:app --reload
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