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import streamlit as st
import requests
import os
from gliner import GLiNER
from streamlit_autorefresh import st_autorefresh
tok = os.getenv("TOK")
st_autorefresh(interval=5000, key="volter")
def Target_Identification(userinput):
model = GLiNER.from_pretrained("Ihor/gliner-biomed-bi-small-v1.0")
labels = ["Protein","Mutation"]
entities = model.predict_entities(userinput, labels, threshold=0.5)
for entity in entities:
if entity["label"] == "Protein":
return entity["text"]
def APP():
tab_map = {
0: "BIO ENGINEERING LAB @newMATTER",
}
tab_selection = st.pills(
"TABS",
options=tab_map.keys(),
format_func=lambda option: tab_map[option],
selection_mode="single",
)
def SHOWTABS():
if tab_selection == 0:
# Two-column split
left_col, right_col = st.columns([0.4, 0.6])
# CSS to make right column sticky
st.markdown("""
<style>
[data-testid="column"]:nth-of-type(2) {
position: sticky;
top: 0;
align-self: flex-start;
height: 100vh;
overflow-y: auto;
background-color: #0E1117;
padding: 10px;
border-left: 1px solid rgba(255,255,255,0.1);
}
</style>
""", unsafe_allow_html=True)
with left_col:
option_map = {
0: "@OriginAI Nanobody Engineering:",
}
selection = st.pills(
"BIOLOGICS",
options=option_map.keys(),
format_func=lambda option: option_map[option],
selection_mode="single",
)
if selection == 0:
st.markdown(
"<p style='color:white;background-color:orange;font-weight:bold'> Nanobody [CANCER targeted]</p>",
unsafe_allow_html=True,
)
projects = []
projectname = None
def scan_for_project_availability(user_id):
request_url = f"https://thexforce-combat-backend.hf.space/{user_id}/projects"
response = requests.get(
request_url,
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {tok}",
},
)
response_json = response.json()
pros = response_json.get("projects")
for pro in pros:
if isinstance(pro, dict):
projects.append(pro.get("project"))
else:
projects.append(pro)
scan_for_project_availability(st.user.email)
if len(projects) > 0:
projectname = st.selectbox("Select Project", projects)
else:
projectname = st.text_input("Enter project name:")
st.session_state.projectname = projectname
with right_col:
bio_input = st.chat_input(" Ready for Action ! ")
@st.cache_data(ttl=10)
def fetch_ops():
response = requests.get(
f"https://thexforce-combat-backend.hf.space/user/operations/{st.user.email}",
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {tok}",
},
)
return response.json()
if "messages" not in st.session_state:
st.session_state.messages = []
if len(st.session_state.messages) > 0:
for msg in st.session_state.messages:
with st.chat_message(msg["role"]):
st.markdown(msg["content"])
if bio_input:
st.session_state.messages.append({"role": "user", "content": bio_input})
with st.chat_message("user"):
st.markdown(bio_input)
if st.session_state.projectname in [None, ""]:
st.markdown(":orange-badge[⚠️ Set Projectname]")
else:
identified_target = Target_Identification(bio_input)
st.warning(f"TARGET IDENTIFIED IS : {identified_target}")
payload = {
"uid": st.user.email,
"pid": st.session_state.projectname,
"target": identified_target or None,
"high_level_bio_query": bio_input,
}
response = requests.post(
"https://thexforce-combat-backend.hf.space/application_layer_agent",
json=payload,
headers={
"Content-Type": "application/json",
"Authorization":f"Bearer {tok}",
},
)
plan_response = response.json()
with st.chat_message("assistant"):
st.markdown(plan_response)
fetch_ops_response = fetch_ops()
if fetch_ops_response.get("exp") is not None:
for op in fetch_ops_response.get("exp"):
with st.chat_message("assistant"):
st.markdown(op.get("operation"))
st.markdown(op.get("output"))
st.session_state.messages.append(
{"role": "assistant", "content": str(plan_response)}
)
if st.user.is_logged_in:
if st.button("🚪 Logout"):
st.logout()
st.rerun()
st.markdown(f"## {st.user.email}")
SHOWTABS()
else:
st.info("Please log in to access the Bio Lab")
if st.button("Log in"):
st.login("auth0")
st.stop()
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