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import os
import re
import streamlit as st
from huggingface_hub import InferenceClient
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
# Set Streamlit page configuration
st.set_page_config(page_title="🎓 Education Fellow Chatbot", layout="wide")
st.title("🎓 Education Fellow Chatbot")
# Initialize session state for chat history
if "history" not in st.session_state:
st.session_state.history = [] # stores tuples of (sender, message)
# Initialize the Hugging Face Inference Client
try:
hf_token = os.environ["HUGGINGFACEHUB_API_TOKEN"]
client = InferenceClient(
model="deepseek-ai/DeepSeek-R1",
token=hf_token
)
except KeyError:
st.error("❌ HUGGINGFACEHUB_API_TOKEN is not set in environment variables.")
st.stop()
# Function to structure chat history as messages for the API
def build_messages():
return [
{"role": "user" if sender == "You" else "assistant", "content": message}
for sender, message in st.session_state.history
]
# Function to remove <think> tags from the model response
def clean_think_tags(text: str) -> str:
return re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL).strip()
# Display previous chat messages
for sender, message in st.session_state.history:
with st.chat_message("user" if sender == "You" else "assistant"):
st.write(message)
# Chat input box
user_input = st.chat_input("Ask me anything related to education, classrooms, or pedagogy…")
# If there's new input from the user
if user_input:
st.session_state.history.append(("You", user_input))
with st.chat_message("user"):
st.write(user_input)
with st.chat_message("assistant"):
placeholder = st.empty()
placeholder.write("⏳ Thinking...")
try:
response = client.chat.completions.create(
model="deepseek-ai/DeepSeek-R1",
messages=build_messages()
)
raw_output = response.choices[0].message["content"]
reply = clean_think_tags(raw_output)
except Exception as e:
reply = f"❌ API Error: {e}"
placeholder.write(reply)
st.session_state.history.append(("Bot", reply))
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