Spaces:
Running
Running
Update app.py
Browse files
app.py
CHANGED
|
@@ -7,9 +7,9 @@ import streamlit as st
|
|
| 7 |
# -----------------------------------------------------------------------------
|
| 8 |
# 1. PAGE CONFIGURATION & CSS
|
| 9 |
# -----------------------------------------------------------------------------
|
| 10 |
-
st.set_page_config(page_title="AVP-
|
| 11 |
|
| 12 |
-
GITHUB_URL = "https://github.com/wendy1031/AVP-
|
| 13 |
LAB_URL = "http://www.jcu-qiulab.com"
|
| 14 |
|
| 15 |
st.markdown(
|
|
@@ -181,10 +181,10 @@ def clear_input():
|
|
| 181 |
st.markdown(
|
| 182 |
f"""
|
| 183 |
<div class="header-container">
|
| 184 |
-
<div class="main-title">AVP-
|
| 185 |
<div class="sub-title">Deep Learning for Antiviral Peptide Discovery</div>
|
| 186 |
<div class="copyright-info">
|
| 187 |
-
© 2025 AVP-
|
| 188 |
</div>
|
| 189 |
</div>
|
| 190 |
""",
|
|
@@ -195,7 +195,7 @@ st.markdown(
|
|
| 195 |
st.markdown(
|
| 196 |
"""
|
| 197 |
<div class="intro-box">
|
| 198 |
-
<b>AVP-
|
| 199 |
It integrates adaptive feature fusion and contrastive learning to better capture sequence dependencies.
|
| 200 |
Accurate identification of AVPs is critical for accelerating novel drug development.
|
| 201 |
</div>
|
|
@@ -204,11 +204,11 @@ st.markdown(
|
|
| 204 |
)
|
| 205 |
|
| 206 |
# Architecture
|
| 207 |
-
with st.expander("🧩 View AVP-
|
| 208 |
try:
|
| 209 |
c1, c2, c3 = st.columns([1, 8, 1])
|
| 210 |
with c2:
|
| 211 |
-
st.image("framework.png", caption="Figure 1: The overall architecture of AVP-
|
| 212 |
except Exception:
|
| 213 |
st.warning("Framework image not found.")
|
| 214 |
|
|
@@ -345,7 +345,7 @@ if run_btn:
|
|
| 345 |
st.download_button(
|
| 346 |
label="📥 Download Results as CSV",
|
| 347 |
data=csv,
|
| 348 |
-
file_name="
|
| 349 |
mime="text/csv",
|
| 350 |
type="primary"
|
| 351 |
)
|
|
@@ -361,7 +361,7 @@ st.write("")
|
|
| 361 |
st.write("")
|
| 362 |
st.markdown("---")
|
| 363 |
st.subheader("📂 Download Benchmark Datasets")
|
| 364 |
-
st.caption("Benchmark datasets used in AVP-
|
| 365 |
|
| 366 |
d_col1, d_col2, d_col3 = st.columns([1, 1, 2])
|
| 367 |
avp_content = read_file_content("AVP.txt")
|
|
|
|
| 7 |
# -----------------------------------------------------------------------------
|
| 8 |
# 1. PAGE CONFIGURATION & CSS
|
| 9 |
# -----------------------------------------------------------------------------
|
| 10 |
+
st.set_page_config(page_title="AVP-Pro", layout="wide", page_icon="🧬")
|
| 11 |
|
| 12 |
+
GITHUB_URL = "https://github.com/wendy1031/AVP-Pro"
|
| 13 |
LAB_URL = "http://www.jcu-qiulab.com"
|
| 14 |
|
| 15 |
st.markdown(
|
|
|
|
| 181 |
st.markdown(
|
| 182 |
f"""
|
| 183 |
<div class="header-container">
|
| 184 |
+
<div class="main-title">AVP-Pro</div>
|
| 185 |
<div class="sub-title">Deep Learning for Antiviral Peptide Discovery</div>
|
| 186 |
<div class="copyright-info">
|
| 187 |
+
© 2025 AVP-Pro Team | <a href="{LAB_URL}" target="_blank">JCU Qiu Lab</a>
|
| 188 |
</div>
|
| 189 |
</div>
|
| 190 |
""",
|
|
|
|
| 195 |
st.markdown(
|
| 196 |
"""
|
| 197 |
<div class="intro-box">
|
| 198 |
+
<b>AVP-Pro</b> is a deep learning framework designed to identify antiviral peptides (AVPs) with high precision.<br>
|
| 199 |
It integrates adaptive feature fusion and contrastive learning to better capture sequence dependencies.
|
| 200 |
Accurate identification of AVPs is critical for accelerating novel drug development.
|
| 201 |
</div>
|
|
|
|
| 204 |
)
|
| 205 |
|
| 206 |
# Architecture
|
| 207 |
+
with st.expander("🧩 View AVP-Pro Architecture (Click to expand)"):
|
| 208 |
try:
|
| 209 |
c1, c2, c3 = st.columns([1, 8, 1])
|
| 210 |
with c2:
|
| 211 |
+
st.image("framework.png", caption="Figure 1: The overall architecture of AVP-Pro.", use_container_width=True)
|
| 212 |
except Exception:
|
| 213 |
st.warning("Framework image not found.")
|
| 214 |
|
|
|
|
| 345 |
st.download_button(
|
| 346 |
label="📥 Download Results as CSV",
|
| 347 |
data=csv,
|
| 348 |
+
file_name="avp_Pro_results.csv",
|
| 349 |
mime="text/csv",
|
| 350 |
type="primary"
|
| 351 |
)
|
|
|
|
| 361 |
st.write("")
|
| 362 |
st.markdown("---")
|
| 363 |
st.subheader("📂 Download Benchmark Datasets")
|
| 364 |
+
st.caption("Benchmark datasets used in AVP-Pro study.")
|
| 365 |
|
| 366 |
d_col1, d_col2, d_col3 = st.columns([1, 1, 2])
|
| 367 |
avp_content = read_file_content("AVP.txt")
|