deepseek R1

app.py

@@ -1,69 +1,65 @@
 import os
+import re
 import streamlit as st
 from huggingface_hub import InferenceClient
 from dotenv import load_dotenv
 
-# 1. Load your HF token from .env
+# Load environment variables
 load_dotenv()
 
-# 2. Instantiate the Hugging Face Inference client once
+# Instantiate the HF Inference client
 client = InferenceClient(
     provider="auto",
     api_key=os.environ["HUGGINGFACEHUB_API_TOKEN"]
 )
 
-# 3. Streamlit page config
 st.set_page_config(page_title="Educational Chatbot", layout="wide")
 st.title("🎓 Educational Chatbot")
 
-# 4. Initialize chat history in session state
 if "history" not in st.session_state:
-    # history is a list of (sender, message) tuples
-    st.session_state.history = []
+    st.session_state.history = []  # list of (sender, message)
 
 def build_messages():
-    """
-    Convert session_state.history into the list of {"role", "content"} dicts
-    that the Hugging Face chat API expects.
-    """
-    msgs = []
-    for sender, text in st.session_state.history:
-        role = "user" if sender == "You" else "assistant"
-        msgs.append({"role": role, "content": text})
-    return msgs
+    return [
+        {"role": "user" if s == "You" else "assistant", "content": m}
+        for s, m in st.session_state.history
+    ]
 
-# 5. Render the existing chat as chat bubbles
-for sender, text in st.session_state.history:
+def clean_think_tags(text: str) -> str:
+    # remove <think>...</think> blocks
+    return re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL).strip()
+
+# Render chat history
+for sender, msg in st.session_state.history:
     if sender == "You":
-        st.chat_message("user").write(text)
+        st.chat_message("user").write(msg)
     else:
-        st.chat_message("assistant").write(text)
+        st.chat_message("assistant").write(msg)
 
-# 6. Get new user input
+# Input
 user_input = st.chat_input("Ask me anything…")
 
 if user_input:
-    # 7. Immediately record & display the user message
+    # show user turn
     st.session_state.history.append(("You", user_input))
     st.chat_message("user").write(user_input)
 
-    # 8. Show a placeholder for the assistant response
+    # placeholder for assistant
     placeholder = st.chat_message("assistant")
     placeholder.write("⏳ Thinking...")
 
-    # 9. Build the full message history to send
-    full_messages = build_messages()
-
-    # 10. Call the HF Inference API with the full conversation
+    # call HF chat endpoint with entire history
     try:
-        completion = client.chat.completions.create(
+        response = client.chat.completions.create(
             model="deepseek-ai/DeepSeek-R1",
-            messages=full_messages,
+            messages=build_messages()
         )
-        reply = completion.choices[0].message["content"]
+        raw = response.choices[0].message["content"]
+        # clean out think tags
+        reply = clean_think_tags(raw)
     except Exception as e:
         reply = f"❌ API Error: {e}"
 
-    # 11. Update the placeholder and session history
+    # display and store cleaned reply
     placeholder.write(reply)
     st.session_state.history.append(("Bot", reply))