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19b8dfa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | import os
from src.helpers.inference_helper_functions_v2 import InferenceClient
import streamlit as st
from dotenv import load_dotenv
st.set_page_config(
page_title="Jimmy", page_icon="😒", initial_sidebar_state="collapsed", layout="wide"
)
load_dotenv()
# Password protection
def check_password():
def password_entered():
if st.session_state["password"] == os.getenv("APP_PASSWORD"):
st.session_state["password_correct"] = True
del st.session_state["password"]
else:
st.session_state["password_correct"] = False
if not st.session_state.get("password_correct", False):
st.markdown("\n\n")
st.text_input(
"Enter the password",
type="password",
on_change=password_entered,
key="password",
)
st.divider()
st.info("Developed by Milan Mrdenovic - IBM Norway 2026")
if "password_correct" in st.session_state:
st.error("😕 Password incorrect")
return False
return True
if not check_password():
st.stop()
@st.cache_resource(show_spinner=False)
def get_client() -> InferenceClient:
"""Build the inference client exactly once.
@st.cache_resource caches the returned object globally (shared across reruns
and sessions); since this takes no arguments the cache key is constant, so
InferenceClient is instantiated a single time for the lifetime of the app.
"""
region = os.getenv("INF_REGION", "us-east")
project_id = os.getenv("INF_PROJECT_ID", "")
# INF_ENDPOINT is a template with {INF_REGION}/{INF_PROJECT_ID} placeholders.
endpoint = os.getenv("INF_ENDPOINT", "").format(
INF_REGION=region, INF_PROJECT_ID=project_id
)
return InferenceClient(
provider=os.getenv("INF_PROVIDER", "rhai"),
api_key=os.getenv("INF_API_KEY", ""),
url=endpoint,
project=project_id,
region=region,
model_id=os.getenv("INF_DEFAULT_MODEL", ""),
params={
"max_completion_tokens": int(os.getenv("INF_MAX_TOKENS", "450")),
"temperature": float(os.getenv("INF_TEMPERATURE", "0.7")),
"top_p": float(os.getenv("INF_TOP_P", "1.0")),
},
)
def initialize_session_state():
if "chat_history" not in st.session_state:
st.session_state.chat_history = [
{"role": "system", "content": os.getenv("PROMPT_TEMPLATE", "")}
]
def show_chat_turns() -> bool:
"""Whether to replay prior turns on screen (SHOW_CHAT_TURNS, default False)."""
return os.getenv("SHOW_CHAT_TURNS", "False").strip().lower() == "true"
def apply_message_font_scale(scale: float = 1.5) -> None:
"""Enlarge the text inside chat-message bubbles by ``scale`` (1.5 = +50%)."""
st.markdown(
f"""
<style>
[data-testid="stChatMessage"] [data-testid="stMarkdownContainer"] {{
font-size: {scale}em;
line-height: 1.4;
}}
</style>
""",
unsafe_allow_html=True,
)
def chat_interface():
st.subheader("Jimmy")
apply_message_font_scale(float(os.getenv("MESSAGE_FONT_SCALE", "1.35")))
replay = show_chat_turns()
# Replay the visible conversation (skip the system prompt) only when enabled.
if replay:
for message in st.session_state.chat_history[1:]:
avatar = "😒" if message["role"] == "assistant" else None
with st.chat_message(message["role"], avatar=avatar):
st.markdown(message["content"])
user_input = st.chat_input("You:", key="user_input")
if not user_input:
return
st.session_state.chat_history.append({"role": "user", "content": user_input})
if replay:
with st.chat_message("user"):
st.markdown(user_input)
with st.chat_message("assistant", avatar="😒"):
stream = get_client().stream_chat_inference(
messages=st.session_state.chat_history
)
content = st.write_stream(stream)
if not content:
content = "😒 not feeling it right now."
st.markdown(content)
st.session_state.chat_history.append({"role": "assistant", "content": content})
def main():
initialize_session_state()
chat_interface()
if __name__ == "__main__":
main()
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