Update app.py

app.py

@@ -1,52 +1,54 @@
-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})
+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)
+
+        st.write('----------------------------------------------------------------------------------------------')
+        st.write('History:')
+        st.write(response['history'])