Upload 2 files

app.py

@@ -0,0 +1,71 @@
+import streamlit as st
+from langchain.chains.llm import LLMChain
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+)
+from langchain_core.messages import SystemMessage
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+from langchain_groq import ChatGroq
+from dotenv import load_dotenv
+import os
+load_dotenv()
+
+def main():
+    groq_api_key = os.getenv("GROQ_API_KEY")
+    st.title("Chat with Groq!")
+    st.write("Hello! I'm your friendly Groq chatbot. I can help answer your questions, provide information, or just chat. I'm also super fast! Let's start our conversation!")
+
+    st.sidebar.title('Customization')
+    system_prompt = st.sidebar.text_input("System prompt:")
+    model = st.sidebar.selectbox(
+        'Choose a model',
+        ['llama3-8b-8192', 'mixtral-8x7b-32768', 'gemma-7b-it']
+    )
+    conversational_memory_length = st.sidebar.slider('Conversational memory length:', 1, 10, value = 5)
+
+    memory = ConversationBufferWindowMemory(k=conversational_memory_length, memory_key="chat_history", return_messages=True)
+
+    user_question = st.chat_input("Ask a question:") 
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history=[]
+    else:
+        for message in st.session_state.chat_history:
+            memory.save_context(
+                {'input':message['human']},
+                {'output':message['AI']}
+                )
+    groq_chat = ChatGroq(
+            groq_api_key=groq_api_key, 
+            model_name=model
+    )
+    if user_question:
+        prompt = ChatPromptTemplate.from_messages(
+            [
+                SystemMessage(
+                    content=system_prompt
+                ),
+
+                MessagesPlaceholder(
+                    variable_name="chat_history"
+                ),
+
+                HumanMessagePromptTemplate.from_template(
+                    "{human_input}"
+                ),
+            ]
+        )
+        conversation = LLMChain(
+            llm=groq_chat,
+            prompt=prompt,
+            verbose=True,
+            memory=memory,
+        )
+        response = conversation.predict(human_input=user_question)
+        message = {"human":user_question,"AI": response}
+        st.session_state.chat_history.append(message)
+        st.write("Chatbot:", response)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,6 @@
+langchain
+langchain_community
+langchain_core
+langchain_groq
+python-dotenv
+streamlit