DataScience / pages /statistics.py
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import streamlit as st
import os
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
hf = os.getenv('Data_science')
os.environ['HUGGINGFACEHUB_API_TOKEN'] = hf
os.environ['HF_TOKEN'] = hf
# --- Config ---
st.set_page_config(page_title="Statistics Mentor Chat", layout="centered")
st.title("πŸ“Š Statistics Mentor Chat")
# --- Sidebar for selections ---
st.sidebar.title("Mentor Preferences")
exp_options = ['Beginner', 'Intermediate', 'Experienced']
exp = st.sidebar.selectbox("Select your experience level:", exp_options)
# --- Initialize Chat Model ---
stats_model_skeleton = HuggingFaceEndpoint(
repo_id='THUDM/GLM-4-32B-0414',
provider='novita',
temperature=0.7,
max_new_tokens=110,
task='conversational'
)
stats_mentor = ChatHuggingFace(
llm=stats_model_skeleton,
repo_id='THUDM/GLM-4-32B-0414',
provider='novita',
temperature=0.7,
max_new_tokens=110,
task='conversational'
)
# --- Session State ---
if "chat_history_stats" not in st.session_state:
st.session_state.chat_history_stats = []
# --- Chat Form ---
with st.form(key="chat_form"):
user_input = st.text_input("Ask your question:")
submit = st.form_submit_button("Send")
# --- Chat Logic ---
if submit and user_input:
system_prompt = (
f"Act as a statistics mentor with {exp.lower()} experience level. "
f"Explain in a very friendly manner and keep answers within 150 words. "
f"If the question is not about statistics, politely say it is out of scope."
)
messages = [SystemMessage(content=system_prompt), HumanMessage(content=user_input)]
result = stats_mentor.invoke(messages)
st.session_state.chat_history_stats.append((user_input, result.content))
# --- Display Chat History ---
st.subheader("πŸ—¨οΈ Chat History")
for user, bot in st.session_state.chat_history_stats:
st.markdown(f"**You:** {user}")
st.markdown(f"**Mentor:** {bot}")
st.markdown("---")