Create app.py

app.py

@@ -0,0 +1,45 @@
+from langchain.memory import ConversationBufferMemory
+from langchain_community.chat_message_histories import StreamlitChatMessageHistory
+from langchain_groq import ChatGroq
+from langchain.chains import LLMChain
+
+groq_api_key='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'
+
+llm = ChatGroq(model="gemma2-9b-it",api_key=groq_api_key)
+
+from langchain_core.prompts import PromptTemplate
+
+template = ("""You are a professional Maths tutor answer questions provided by user in step by step manner.
+    Use the provided context to answer the question.
+    try to engange with the user and follow up on questions asked
+    If you don't know the answer, say so. Explain your answer in detail.
+    Do not discuss the context in your response; just provide the answer directly.
+    
+    Question: {question}
+
+    Answer:""")
+
+rag_prompt = PromptTemplate.from_template(template)
+
+
+
+history = StreamlitChatMessageHistory(key="chat_messages")
+
+#Step 3 - here we create a memory object
+
+memory = ConversationBufferMemory(chat_memory=history)
+
+llm_chain = LLMChain(llm=llm, prompt=rag_prompt, memory=memory)
+
+import streamlit as st
+
+st.title('🦜🔗 Welcome to the MathLearn 🦜🔗')
+for msg in history.messages:
+    st.chat_message(msg.type).write(msg.content)
+
+if x := st.chat_input():
+    st.chat_message("human").write(x)
+
+    # As usual, new messages are added to StreamlitChatMessageHistory when the Chain is called.
+    response = llm_chain.invoke(x)
+    st.chat_message("ai").write(response["text"])