Remove examples

app.py

@@ -22,22 +22,6 @@ gradio_app = gr.Interface(
     outputs=[gr.Json(label="Model output")],
     title="En2Ru Scientific Translator",
     description="Translate scientific texts from English to Russian",
-    examples=[
-        [
-            r"There is no closed form to implement the KL divergence by the definition of (REF ) and (REF ) for "
-            r"Gaussian Mixture Models. Instead, we resort to the Monte Carlo simulation method proposed in [1]}. "
-            r"Then, the KL divergence can be caculated by: \(D_{KL_{MC}}(p||q) =\frac{1}{n} \sum _{i=1}^{n} log("
-            r"\frac{p(x_i)}{q(x_i)})\) \(D_{KL_{MC}}(q||p) =\frac{1}{n} \sum _{i=1}^{n} log(\frac{q(y_i)}{p(y_i)})\)"],
-        [
-            r"Almost all currently used classifiers are not intrinsically well-calibrated [1]}, which means their "
-            r"output scores can't be interpreted as probabilities. This is an issue when the model is used for "
-            r"decision making, as a component in a more general probabilistic pipeline, or simply when one needs a "
-            r"quantification of the uncertainty in model's predictions, for example in high risk applications."],
-        [
-            r"First, with the development of the high-torque electric actuators, such as [1]}, [2]} the robots are "
-            r"becoming more dynamical. These actuators allow them not only to move at high speeds, but also to "
-            r"rapidly create forces and torques to perform dynamic actions, such as running, jumping, etc."],
-    ],
 )
 
 if __name__ == "__main__":