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app.py

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+import streamlit as st
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.memory import ConversationBufferMemory
+import os
+
+# Set up your Hugging Face API token
+sec_key = os.getenv('HUGGINGFACE_API_TOKEN')
+os.environ['HUGGINGFACE_API_TOKEN'] = sec_key
+
+# Define your Hugging Face model
+repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+llm = HuggingFaceEndpoint(repo_id=repo_id, temperature=0.7)
+
+# Define the prompt template
+template = """The following is a conversation between a user and an AI assistant.
+history:{history}
+Final Message by Human: {user_input}
+Final Message by AI: """
+
+prompt = PromptTemplate(
+    template=template,
+    input_variables=["history", "user_input"],
+)
+
+# Initialize memory
+memory = ConversationBufferMemory()
+
+# Create the LLM chain
+llm_chain = LLMChain(
+    prompt=prompt,
+    llm=llm,
+    memory=memory
+)
+
+# Streamlit app
+st.title("AI Chatbot")
+st.write("Welcome to the AI Chatbot! Ask anything you like.")
+
+# User input
+user_input = st.text_input("You:", key="input")
+
+if st.button("Send"):
+    if user_input:
+        # Generate response
+        response = llm_chain.invoke({"history": memory.chat_memory.messages, 'user_input': user_input})
+        response_text = response['text']
+        # Display the response
+        st.text_area("ChatBot:", response_text, height=100)
+        # Save the interaction in memory
+        memory.save_context({"input": user_input}, {"output": response_text})

requirements.txt

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+streamlit
+langchain
+langchain_huggingface