Update app.py

app.py

@@ -1,40 +1,36 @@
 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
-import os
-from dotenv import load_dotenv
-from langchain_huggingface import HuggingFaceEndpoint
-load_dotenv()
-
-# Set your HF_TOKEN
-HF_TOKEN = os.getenv("HF_TOKEN")
+from langchain.chat_models import ChatOpenAI
 
+# Load OpenAI API key
+load_dotenv()
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 
-# Initialize the HuggingFace inference endpoint
-llm = HuggingFaceEndpoint(
-    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
-    huggingfacehub_api_token=HF_TOKEN.strip(),
+# Initialize OpenAI Chat Model (e.g., gpt-3.5-turbo)
+llm = ChatOpenAI(
+    openai_api_key=OPENAI_API_KEY,
+    model_name="gpt-4o-mini",
     temperature=0.7,
-    task="text-generation", 
-    max_new_tokens=100
+    max_tokens=50,
 )
 
-# Initialize Streamlit app
+# Streamlit UI
 st.set_page_config(page_title="LangChain Chatbot with Memory")
-st.title("🤖 LangChain Chatbot with Memory")
+st.title("🤖 LangChain Chatbot with Memory (OpenAI)")
 
-# Initialize chat message history
+# Message history
 history = StreamlitChatMessageHistory(key="chat_messages")
-
-# Display chat history
 for msg in history.messages:
     if msg.type == "human":
         st.chat_message("user").write(msg.content)
     else:
         st.chat_message("assistant").write(msg.content)
 
-# Initialize memory with chat history
+# Memory setup
 memory = ConversationBufferMemory(chat_memory=history, return_messages=True)
 
 # Create conversation chain
@@ -42,14 +38,14 @@ conversation = ConversationChain(llm=llm, memory=memory)
 
 # Chat input
 if prompt := st.chat_input("Say something..."):
-    # Add user message to history
+    # Display and store user message
     history.add_user_message(prompt)
     st.chat_message("user").write(prompt)
 
-    # Generate AI response with word limit instruction
+    # Add 50-word limit prompt
     limited_prompt = f"{prompt}\n\nPlease respond in no more than 50 words."
     response = conversation.predict(input=limited_prompt)
 
-    # Add AI message to history
+    # Display and store assistant message
     history.add_ai_message(response)
     st.chat_message("assistant").write(response)