fixed plot

.gitignore

@@ -0,0 +1,2 @@
+.gradio/
+__pycache__/

app.py

@@ -27,13 +27,13 @@ def _warmup():
     return f"Model loaded in {time.time() - t0:.1f} seconds."
 
 def ci_plot(mean: float, low: float, upp: float):
-    fig, ax = plt.subplots(figsize=(6, 1))
-    ax.hlines(1, 0, 100, linewidht=2, alpha=0.15)
-    ax.hlines(1, low, upp, linewidht=6)
-    ax.plot([mean], [1], "o")
+    fig, ax = plt.subplots(figsize=(6, 1.6))
+    ax.barh(y=0, width=100, linewidth=2, alpha=0.15)
+    ax.barh(y=0, width=max(0, upp-low), height=0.5)
+    ax.plot([mean], [0], "o")
     ax.set_xlim(0, 100)
     ax.set_yticks([])
-    ax.set_xlabel("Empathy Score (0-100)")
+    ax.set_xlabel("Empathy Score (0-100) +/- 95% CI")
     fig.tight_layout()
     return fig
 
@@ -63,7 +63,7 @@ with gr.Blocks(title="UPLME", theme=Soft(primary_hue="blue")) as demo:
             ci_low = gr.Number(label="95% CI Lower Bound", precision=2)
             ci_upp = gr.Number(label="95% CI Upper Bound", precision=2)
         
-            fig = gr.Plot(label="Prediction +/- 95% CI")
+            fig = gr.Plot(show_label=False)
 
         button.click(fn=predict_with_ci, inputs=[essay_input, article_input], outputs=[output_mean, ci_low, ci_upp, fig])
 
@@ -75,4 +75,4 @@ with gr.Blocks(title="UPLME", theme=Soft(primary_hue="blue")) as demo:
     """)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)