Update app.py

app.py

@@ -4,7 +4,7 @@ import numpy as np
 import threading
 import torch
 import numpy as np
-from styling import footer
+#from styling import footer
 from transformers import AutoTokenizer, AutoModelWithLMHead
 from huggingface_hub import HfApi, hf_hub_download
 from torch.utils.data import Dataset, DataLoader
@@ -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()
     
@@ -267,6 +267,7 @@ with st.sidebar:
                 sentence, tokenizer, model, MLM_MASK_TOKEN, MLM_UNK_TOKEN, BATCH_SIZE
             )
             scores[model_id] = score
+            st.write(index, len(selected_models))
             my_bar.progress(index + 1 / len(selected_models))
         scores = sort_dictionary(scores)
         st.write("Our recommendation is:", scores)