deepseek R1

app.py

@@ -3,52 +3,67 @@ import streamlit as st
 from huggingface_hub import InferenceClient
 from dotenv import load_dotenv
 
-# Load .env (if you’re using one)
+# 1. Load your HF token from .env
 load_dotenv()
 
-# Instantiate once
+# 2. Instantiate the Hugging Face Inference client once
 client = InferenceClient(
     provider="auto",
     api_key=os.environ["HUGGINGFACEHUB_API_TOKEN"]
 )
 
-# Page config
+# 3. Streamlit page config
 st.set_page_config(page_title="Educational Chatbot", layout="wide")
 st.title("🎓 Educational Chatbot")
 
-# Initialize history
+# 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 = []
 
-# Render existing messages
-for sender, message in st.session_state.history:
+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
+
+# 5. Render the existing chat as chat bubbles
+for sender, text in st.session_state.history:
     if sender == "You":
-        st.chat_message("user").write(message)
+        st.chat_message("user").write(text)
     else:
-        st.chat_message("assistant").write(message)
+        st.chat_message("assistant").write(text)
 
-# Input box
+# 6. Get new user input
 user_input = st.chat_input("Ask me anything…")
 
 if user_input:
-    # Display user message immediately
-    st.chat_message("user").write(user_input)
+    # 7. Immediately record & display the user message
     st.session_state.history.append(("You", user_input))
+    st.chat_message("user").write(user_input)
 
-    # Bot placeholder
+    # 8. Show a placeholder for the assistant response
     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
     try:
-        # Call HF Inference API
         completion = client.chat.completions.create(
             model="deepseek-ai/DeepSeek-R1",
-            messages=[{"role": "user", "content": user_input}],
+            messages=full_messages,
         )
         reply = completion.choices[0].message["content"]
     except Exception as e:
         reply = f"❌ API Error: {e}"
 
-    # Update placeholder with real response
+    # 11. Update the placeholder and session history
     placeholder.write(reply)
     st.session_state.history.append(("Bot", reply))