Upload app.py

app.py

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+import streamlit as st
+import os
+import random, string
+
+from langchain.chains 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
+
+if 'chat_list' not in st.session_state:
+    st.session_state.chat_list = []
+
+
+def arr():
+    for c_list in st.session_state.chat_list:
+        with st.chat_message("user"):
+            st.write("Question : " + c_list["ques"])
+        with st.chat_message("machine"):
+            st.write("Answer : " + c_list["ans"])
+
+def main():
+
+    """
+    This function is the main entry point of the application. It sets up the Groq client, the Streamlit interface, and handles the chat interaction.
+    """
+
+    # Get Groq API key
+    groq_api_key = "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"
+    model = 'llama3-8b-8192'
+    # Initialize Groq Langchain chat object and conversation
+    groq_chat = ChatGroq(
+        groq_api_key=groq_api_key, 
+        model_name=model
+    )
+
+    st.title('Langchain Chatbot With llama3-8b-8192 model')
+    
+    # print("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.markdown("Hello! I'm your friendly Groq chatbot, dev by GJ. I can help answer your questions, provide information, or just chat. I'm also super fast! Let's start our conversation!")
+
+    system_prompt = 'You are a friendly conversational chatbot'
+    conversational_memory_length = 5 # number of previous messages the chatbot will remember during the conversation
+
+    if 'memory' not in st.session_state:
+        st.session_state.memory = ConversationBufferWindowMemory(k=conversational_memory_length, memory_key="chat_history", return_messages=True)
+    # st.write(st.session_state.memory)
+
+
+    # user_question = st.text_input("Ask a question: ")
+    user_question = st.chat_input("Ask a question:")
+    if user_question:
+        # Construct a chat prompt template using various components
+        prompt = ChatPromptTemplate.from_messages(
+            [
+                SystemMessage(
+                    content=system_prompt
+                ),  # This is the persistent system prompt that is always included at the start of the chat.
+
+                MessagesPlaceholder(
+                    variable_name="chat_history"
+                ),  # This placeholder will be replaced by the actual chat history during the conversation. It helps in maintaining context.
+
+                HumanMessagePromptTemplate.from_template(
+                    "{human_input}"
+                ),  # This template is where the user's current input will be injected into the prompt.
+            ]
+        )
+
+        # Create a conversation chain using the LangChain LLM (Language Learning Model)
+        conversation = LLMChain(
+            llm=groq_chat,  # The Groq LangChain chat object initialized earlier.
+            prompt=prompt,  # The constructed prompt template.
+            verbose=False,   # TRUE Enables verbose output, which can be useful for debugging.
+            memory=st.session_state.memory,  # The conversational memory object that stores and manages the conversation history.
+        )
+        # The chatbot's answer is generated by sending the full prompt to the Groq API.
+        response = conversation.predict(human_input=user_question)
+        # st.text("Question: " + user_question)
+        # st.text("Chatbot: " + response)
+        result = {"ques":user_question, "ans":response}
+        st.session_state.chat_list.append(result)
+        arr()
+        # st.write(st.session_state.memory)
+
+if __name__ == "__main__":
+    main()