feat: add new "FLUX.1 [dev] examples" tab in app.py with embedded Notion iframe, and update "About" section iframe for enhanced user engagement and information access

app.py

@@ -67,38 +67,18 @@ with gr.Blocks("ParityError/Interstellar", css=custom_css) as demo:
                 select_columns=df.columns.tolist(),
                 datatype="markdown",
             )
+        with gr.TabItem("FLUX.1 [dev] examples"):
+            gr.HTML(
+                """
+                <iframe src="https://pruna.notion.site/ebd/1d270a039e5f80c6a2a3c00fc0d75ef0" width="100%" height="900" frameborder="0" allowfullscreen />
+                """
+            )
         with gr.TabItem("About"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown(
-                        """
-                        # 💜 About Pruna AI
-                        We are [Pruna AI, an open source AI optimisation engine](https://github.com/PrunaAI/pruna) and we simply make your models cheaper, faster, smaller, greener!
-
-                        # 📊 About InferBench
-                        InferBench is a leaderboard for inference providers, focusing on cost, quality, and speed.
-                        Over the past few years, we’ve observed outstanding progress in image generation models fueled by ever-larger architectures.
-                        Due to their size, state-of-the-art models such as FLUX take more than 6 seconds to generate a single image on a high-end H100 GPU.
-                        While compression techniques can reduce inference time, their impact on quality often remains unclear.
-
-                        To bring more transparency around the quality of compressed models:
-
-                        - We release “juiced” endpoints for popular image generation models on Replicate, making it easy to play around with our compressed models.
-                        - We assess the quality of compressed FLUX-APIs from Replicate, fal, Fireworks AI and Together AI according to different benchmarks.
-
-                        FLUX-juiced was obtained using a combination of compilation and caching algorithms and we are proud to say that it consistently outperforms alternatives, while delivering performance on par with the original model.
-                        This combination is available in our Pruna Pro package and can be applied to almost every image generation model.
-
-                        - A full blogpost on the methodology can be found [here](https://pruna.ai/blog/flux-juiced).
-                        - A website that compares the outputs of the different models can be found [here](https://www.notion.so/FLUX-juiced-1d270a039e5f80c6a2a3c00fc0d75ef0?pvs=4).
-                        """
-                    )
-                with gr.Column(scale=1):
-                    gr.HTML(
-                        """
-                        <iframe src="https://www.notion.so/FLUX-juiced-1d270a039e5f80c6a2a3c00fc0d75ef0?pvs=4" width="100%" height="100%" frameborder="0"></iframe>
-                        """
-                    )
+            gr.HTML(
+                """
+                <iframe src="https://pruna.notion.site/ebd/1d870a039e5f8021aafdd19e844bf2c8" width="100%" height="900" frameborder="0" allowfullscreen />
+                """
+            )
 
         with gr.Accordion("🌍 Join the Pruna AI community!", open=False):
             gr.HTML(