Create app.py

app.py

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+import streamlit as st
+from langchain.chat_models import ChatOpenAI
+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
+
+load_dotenv()
+
+# Set your HF_TOKEN
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+
+# Initialize the HuggingFace inference endpoint
+llm = HuggingFaceEndpoint(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+    huggingfacehub_api_token=HF_TOKEN.strip(),
+    temperature=0.7,
+    max_new_tokens=200
+)
+
+# Initialize Streamlit app
+st.set_page_config(page_title="LangChain Chatbot with Memory")
+st.title("🤖 LangChain Chatbot with Memory")
+
+# Initialize chat 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 = 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..."):
+    # Add user message to history
+    history.add_user_message(prompt)
+    st.chat_message("user").write(prompt)
+
+    # Generate AI response
+    response = conversation.predict(input=prompt)
+
+    # Add AI message to history
+    history.add_ai_message(response)
+    st.chat_message("assistant").write(response)