add more details

app.py

@@ -6,6 +6,8 @@ import streamlit as st
 import pandas as pd
 import time
 
+import numpy as np
+
 # define some constants
 CODE_LLM = "Codex"
 DEFAULT_FIRST_EXAMPLE_IDX = 47
@@ -19,8 +21,8 @@ RESULT_FILES_DICTS = {
     "MBPP": "demo-mbpp-codex-results.jsonl"
 }
 
-N_MODELS = 21
-N_ORGS = 10
+N_MODELS = 54
+N_ORGS = 13
 
 #################### Setups must go first ####################
 st.set_page_config(layout="wide")
@@ -40,9 +42,7 @@ with st.sidebar:
 #################### START OF DEMO ####################
 
 # some basic intro
-# st.image("")
-st.markdown("![Sample Image](./images/l2ceval-logo.png)")
-
+st.image("images/l2ceval-logo.png", use_column_width="auto")
 st.markdown("### L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models")
 st.markdown("**Ansong Ni$^†$, Pengcheng Yin$^♣$, Yilun Zhao$^†$, Martin Riddell$^†$, Troy Feng$^†$, Rui Shen$^†$, Stephen Yin$^†$**")
 st.markdown("**Ye Liu$^♢$, Semih Yavuz$^♢$, " \
@@ -144,3 +144,35 @@ st.markdown("""
 - OpenAI code-davinci-002
 - OpenAI code-cushman-001
 """)
+
+st.divider()
+
+
+# read results from csv
+# results = pd.read_csv("data/scatter.csv")
+
+# st.info(results.to_markdown())
+# st.info(results.columns)
+# st.info(results.dtypes)
+
+# st.scatter_chart(
+#     results,
+#     x="Model Size",
+#     y="Avg. Perf.",
+#     color='Model Series',
+#     # size='Avg. Perf.',
+# )
+
+# chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["col1", "col2", "col3"])
+# chart_data['col4'] = np.random.choice(['A','B','C'], 20)
+
+# st.info(chart_data.to_markdown())
+# st.info(chart_data.dtypes)
+
+# st.scatter_chart(
+#     chart_data,
+#     x='col1',
+#     y='col2',
+#     color='col4',
+#     size='col3',
+# )