fix repo id error

app.py

@@ -102,7 +102,7 @@ def stacked_model(model1,model2,model3):
             ax.plot([], [], " ", label=f"{name}: {score}")
         ax.legend(loc="upper left")
 
-    fig.suptitle("Single predictors versus stacked predictors")
+    fig.suptitle("Single predictor versus stacked predictors")
     fig.tight_layout()
     fig.subplots_adjust(top=0.9)
     return fig
@@ -112,14 +112,15 @@ with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown("""
     This app demonstrates combining 3 predictors trained on Ames housing dataset from OpenML using stacking and Ridge estimator as final estimator.  
-    Stacking uses a meta-learning algorithm to learn how to best combine the predictions from trained models. The OpenML Ames housing dataset is a processed version of the 'Ames Iowa Housing'with 81 features.
+    Stacking uses a meta-learning algorithm to learn how to combine the predictions from trained models. 
+    The OpenML Ames housing dataset is a processed version of the 'Ames Iowa Housing' with 81 features.
     This app is developed based on [scikit-learn example](https://scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py)
     """)
 
-    model1 = gr.Textbox(label="Repo id of first model", value="https://huggingface.co/haizad/ames-housing-random-forest-predictor")
-    model2 = gr.Textbox(label="Repo id of second model", value="https://huggingface.co/haizad/ames-housing-gbdt-predictor")
-    model3 = gr.Textbox(label="Repo id of third model", value="https://huggingface.co/haizad/ames-housing-lasso-predictor")
-    plot = gr.Plot()
+    model1 = gr.Textbox(label="Repo id of first model", value="haizad/ames-housing-random-forest-predictor")
+    model2 = gr.Textbox(label="Repo id of second model", value="haizad/ames-housing-gbdt-predictor")
+    model3 = gr.Textbox(label="Repo id of third model", value="haizad/ames-housing-lasso-predictor")
+    plot = gr.Plot("Comparison of single predictor against stacked predictor")
     stack_btn = gr.Button("Stack")
     stack_btn.click(fn=stacked_model, inputs=[model1,model2,model3], outputs=[plot])