app.py

import os 

from langchain_core.prompts import ChatPromptTemplate
from langsmith import Client, traceable
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langsmith import Client, traceable
from langchain_core.output_parsers import StrOutputParser

from langchain_nomic.embeddings import NomicEmbeddings
from langchain_groq import ChatGroq
from dotenv import load_dotenv

load_dotenv()

GROQ_API_KEY = os.getenv('GROQ_API_KEY')
HF_API_KEY = os.getenv("HF_API_KEY")



#LANGSMITH_TRACING="true"
#LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
#LANGSMITH_API_KEY=os.getenv("LANGSMITH_API_KEY")
#LANGSMITH_PROJECT="pr-internal-hand-91"

model_name="llama-3.1-70b-versatile"
llm = ChatGroq(
    temperature=0,
    model= "llama-3.3-70b-versatile", #"llama3-70b-8192",
    api_key=GROQ_API_KEY,
    verbose= True,
    max_retries=2,


)


#@traceable
def get_answer(question):

  prompt = ChatPromptTemplate.from_messages([
      ("system", "You are a hydroponic AI assistant answer questions of the user with details and here is some facts about the hydroponic farming: {facts}"),
      ("user", "{question}")
  ])

  chain = prompt | llm

  parser = StrOutputParser()

  chain = prompt | llm | parser

  answer= chain.invoke({"question": question, "facts": fake_db_retrieval()})
  return answer





embedding_model = NomicEmbeddings(model="nomic-embed-text-v1.5", inference_mode="local")
db = "tomato1"
from langchain.vectorstores import Chroma

vector_store = Chroma(
    collection_name="chromadb3",
    persist_directory=db,
    embedding_function=embedding_model,
)

from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.chains import RetrievalQA

conversational_memory = ConversationBufferWindowMemory(
    memory_key='chat_history',
    k=5, #Number of messages stored in memory
    return_messages=True #Must return the messages in the response.
)

qa = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vector_store.as_retriever(k=5)
)

from langchain.agents import Tool

#Defining the list of tool objects to be used by LangChain.
tools = [
   Tool(
       name='Hydroponic Knowledge database',
       func=qa.run,
       description=(
          'use this tool when answering Hydroponic knowledge queries to get '
           'more information about the topic but if you dont know or it is not mentioned in the database do not say I can try to provide some general information just say what you know as if you know it from the knowledge database'
       )
   )
]

from langchain.agents import create_react_agent
from langchain import hub

prompt = hub.pull("hwchase17/react-chat")
agent = create_react_agent(
   tools=tools,
   llm=llm,
   prompt=prompt,
)



# Create an agent executor by passing in the agent and tools
from langchain.agents import AgentExecutor
agent_executor = AgentExecutor(agent=agent,
                               tools=tools,
                               verbose=True,
                               memory=conversational_memory,
                               max_iterations=30,
                               max_execution_time=600,
                               #early_stopping_method='generate',
                               handle_parsing_errors=True
                               )


# Function for continuing the conversation
import streamlit as st

# Function for continuing the conversation
def continue_conversation(input, history):
    # Invoke the agent and get the response
    response = agent_executor.invoke({"input": input})
    output = response['output']

    # Prepend the new input and output to the history (latest conversation comes first)
    history.insert(0, {"role": "Agricultor", "message": input})
    history.insert(0, {"role": "Hydroponic Agent", "message": output})

    # Return the current response and the full history (hidden state)
    return output, history

# Streamlit UI
def main():
    st.set_page_config(page_title="Hydroponic AI Agent", page_icon="👨‍⚕️")
    st.title("Hydroponic AI Agent")

    # Initialize the conversation history
    if 'history' not in st.session_state:
        st.session_state.history = []

    # Sidebar for memory display
    with st.sidebar:
        st.header("Conversation History")
        st.write("This section contains the conversation history.")

    # Create a container for the chat
    chat_container = st.container()

    # Display the chat history with the latest conversation at the top
    for chat in st.session_state.history:
        if chat['role'] == 'Agricultor':
            chat_container.markdown(f"**Agricultor:** {chat['message']}")
        else:
            chat_container.markdown(f"**Hydroponic AI Assistant:** {chat['message']}")

    # User input text box at the bottom
    user_input = st.text_input("Ask a question:", key="input", placeholder="Tell me what do you want to know ?")

    if user_input:
        # Get the response and update the conversation history
        output, updated_history = continue_conversation(user_input, st.session_state.history)
        
        # Update the session state with the new history
        st.session_state.history = updated_history

    # Display memory of past conversation in an expandable section
    with st.expander("Memory", expanded=True):
        for chat in st.session_state.history:
            st.write(f"**{chat['role']}:** {chat['message']}")

if __name__ == "__main__":
    main()