Update app.py

app.py

@@ -213,29 +213,29 @@ def set_seed():
 
 
  
-with st.sidebar:
-
-    st.image("Koya_Presentation-removebg-preview.png")
-    st.subheader("Abstract")
-    st.markdown(
-        """
-        <div style="text-align: justify">
-        <h6> Pretrained large language models (LLMs) are widely used for various downstream tasks in different languages. However, selecting the best
-         LLM (from a large set of potential LLMs) for a given downstream task and language is a challenging and computationally expensive task, making 
-         the efficient use of LLMs difficult for low-compute communities. To address this challenge, we present Koya, a recommender system built to assist 
-         researchers and practitioners in choosing the right LLM for their task and language, without ever having to finetune the LLMs. Koya is built with 
-         the Koya Pseudo-Perplexity (KPPPL), our adaptation of the pseudo perplexity, and ranks LLMs in order of compatibility with the language of interest, 
-         making it easier and cheaper to choose the most compatible LLM. By evaluating Koya using five pretrained LLMs and three African languages 
-         (Yoruba, Kinyarwanda, and Amharic), we show an average recommender accuracy of 95%, demonstrating its effectiveness. Koya aims to offer 
-         an easy to use (through a simple web interface accessible at https://huggingface.co/spaces/koya-recommender/system), cost-effective, fast and 
-         efficient tool to assist researchers and practitioners with low or limited compute access.</h6>
-        </div>
-
-        """,
-        unsafe_allow_html=True
-        )
-    url = "https://drive.google.com/file/d/1eWat34ot3j8onIeKDnJscKalp2oYnn8O/view"
-    st.write("check out the paper [here](%s)" % url)
+# with st.sidebar:
+
+#     st.image("Koya_Presentation-removebg-preview.png")
+#     st.subheader("Abstract")
+#     st.markdown(
+#         """
+#         <div style="text-align: justify">
+#         <h6> Pretrained large language models (LLMs) are widely used for various downstream tasks in different languages. However, selecting the best
+#          LLM (from a large set of potential LLMs) for a given downstream task and language is a challenging and computationally expensive task, making 
+#          the efficient use of LLMs difficult for low-compute communities. To address this challenge, we present Koya, a recommender system built to assist 
+#          researchers and practitioners in choosing the right LLM for their task and language, without ever having to finetune the LLMs. Koya is built with 
+#          the Koya Pseudo-Perplexity (KPPPL), our adaptation of the pseudo perplexity, and ranks LLMs in order of compatibility with the language of interest, 
+#          making it easier and cheaper to choose the most compatible LLM. By evaluating Koya using five pretrained LLMs and three African languages 
+#          (Yoruba, Kinyarwanda, and Amharic), we show an average recommender accuracy of 95%, demonstrating its effectiveness. Koya aims to offer 
+#          an easy to use (through a simple web interface accessible at https://huggingface.co/spaces/koya-recommender/system), cost-effective, fast and 
+#          efficient tool to assist researchers and practitioners with low or limited compute access.</h6>
+#         </div>
+
+#         """,
+#         unsafe_allow_html=True
+#         )
+#     url = "https://drive.google.com/file/d/1eWat34ot3j8onIeKDnJscKalp2oYnn8O/view"
+#     st.write("check out the paper [here](%s)" % url)
 with st.sidebar:
     footer()