Most powerful test. Help using Neyman Pearson $X_1,X_2,...$ independent continuous random variables with p.d.f
$f(x) = \theta x^{\theta-1}$ if $0<x<1 , 0 $ otherwise for $\theta > 0 $
sample size = 1

Use Neyman-Pearson Lemma to drive MP test for the hypothesis $$H_0: \theta = 4$$ $$H_1: \theta = 6$$ at level of significance $\alpha$


Derive the power of the above test at $\theta = 6$

So my thoughts are we use the NP lemma, so this would go $$\frac{L(\theta_0|\mathbf{x})}{L(\theta_1|\mathbf{x})}\leq c$$
this would be the same as $$\frac{4x^3}{6x^5} < c$$
simplifies to $\frac{2}{3x^2} < c$ which after this point I'm not too sure how to complete if i'm being honest
But im thinking along the lines of
$P(X> \sqrt \frac{2}{3c} | \theta_0 = 4) =\alpha$ and for the power part the same thing but use $\theta = 6$
If someone could walk me through this please, it'll be really helpful
Thank you

As was mentioned in the comment sections, $L$ depends on the entire observed simple random sample.  $$L(\theta|x_1,\ldots,x_n)=\theta^n (x_1 \times \ldots \times x_n)^{\theta -1}$$ This gives us the following likelihood ratio: $$\frac{L(\theta_0|x_1,\ldots,x_n)}{L(\theta_1|x_1,\ldots,x_n)}=\Big(\frac{\theta_0}{\theta_1}\Big)^n(x_1 \times \ldots \times x_n)^{\theta_0 - \theta_1}$$ Assume $\theta=\theta_0$ is true, and let $X_1,\ldots,X_n \sim f$ be iid. Put $$Q=-\Big[\ln(X_1)+\ldots + \ln(X_n)\Big]$$ You should check that, when $n$ is large, $Q$ is approximately $N\Big(\frac{n}{\theta_0},\frac{n}{\theta_0^2}\Big)$. Since $\theta_1>\theta_0$, $$P\Bigg(\frac{L(\theta_0|X_1,\ldots, X_n)}{L(\theta_1|X_1,\ldots, X_n)}<c\Bigg)=P\Bigg(Q<\frac{\ln\big[c(\theta_1/\theta_0)^n\big]}{\theta_1-\theta_0}\Bigg)\approx P(Z<z^*)$$ where $z^*=\frac{\theta_0 \ln\big[c(\theta_1/\theta_0)^n\big]}{(\theta_1 - \theta_0)\sqrt{n}}-\sqrt{n}$. If we want the aforementioned probability to equal $\alpha$, we can take $z^*=z_{1-\alpha}$ and solve this for $c$, yielding the following: $$c=(\theta_0/\theta_1)^n\exp\Bigg\{\frac{(z_{1-\alpha}+\sqrt{n})(\theta_1 - \theta_0)\sqrt{n}}{\theta_0}\Bigg\}$$
Remark: I neglected to see that you only had a single observation. This is how you'd proceed with you have a large sample size.
