Update app.py

app.py

@@ -1,48 +1,57 @@
 import streamlit as st
-import os
-from dotenv import load_dotenv
 from langchain.chains import ConversationChain
 from langchain_community.chat_message_histories import StreamlitChatMessageHistory
 from langchain.memory import ConversationBufferMemory
-from langchain.chat_models import ChatOpenAI
-
-# Load API key
+import os
+from dotenv import load_dotenv
+from langchain.chat_models import ChatOpenAI  # ✅ Use ChatOpenAI instead of HuggingFaceEndpoint
 load_dotenv()
+
+# Set your HF_TOKEN
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 
-# Initialize model
+
+# Initialize the OpenAI model
 llm = ChatOpenAI(
     openai_api_key=OPENAI_API_KEY,
-    model_name="gpt-4o-mini",
+    model_name="gpt-4o-mini",  # or "gpt-3.5-turbo"
     temperature=0.7,
-    max_tokens=50,
+    max_tokens=50
 )
 
-# Page config
-st.set_page_config(page_title="LangChain Chatbot")
-st.title("🤖 LangChain Chatbot (OpenAI)")
+# Initialize Streamlit app
+st.set_page_config(page_title="LangChain Chatbot with Memory")
+st.title("🤖 LangChain Chatbot with Memory")
 
-# History and memory
+# Initialize chat message history
 history = StreamlitChatMessageHistory(key="chat_messages")
-memory = ConversationBufferMemory(chat_memory=history, return_messages=True)
-conversation = ConversationChain(llm=llm, memory=memory)
 
-# 💬 Display chat history (before new user input)
+# Display chat history
 for msg in history.messages:
     if msg.type == "human":
         st.chat_message("user").write(msg.content)
-    elif msg.type == "ai":
+    else:
         st.chat_message("assistant").write(msg.content)
 
-# 🔥 Chat input AFTER displaying history
+# Initialize memory with chat history
+memory = ConversationBufferMemory(chat_memory=history, return_messages=True)
+
+# Create conversation chain
+conversation = ConversationChain(llm=llm, memory=memory)
+
+# Chat input
 if prompt := st.chat_input("Say something..."):
-    # Display + store user message
-    st.chat_message("user").write(prompt)
+    # Add user message to history
     history.add_user_message(prompt)
+    st.chat_message("user").write(prompt)
 
-    # Response with word limit
-    response = conversation.predict(input=f"{prompt}\n\nPlease respond in complete sentence in no more than 50 words.")
-    
-    # Display + store assistant message
-    st.chat_message("assistant").write(response)
+    # Generate AI response with word limit instruction
+    limited_prompt = f"{prompt}\n\nPlease respond in complete sentence in no more than 50 words."
+    response = conversation.predict(input=limited_prompt)
+
+    # Add AI message to history
     history.add_ai_message(response)
+    st.chat_message("assistant").write(response)
+
+use following instead of huggingface endpoints
+from langchain.chat_models import ChatOpenAI