# Generics
import os
import keyfile
import warnings
import streamlit as st
from pydantic import BaseModel
warnings.filterwarnings("ignore")

# Langchain packages
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.schema import HumanMessage, SystemMessage, AIMessage

# First message that will pop on the screen
st.set_page_config(page_title = "Magical Healer")
st.header("Welcome, What help do you need?")



class AIMessage(BaseModel):
    content: str
    
# initializing the sessionMessages
if "sessionMessages" not in st.session_state:
    st.session_state["sessionMessages"] = []
# General Instruction
if "sessionMessages" not in st.session_state:
    st.session_state.sessionMessage = [
         SystemMessage(content = "You are a medieval magical healer known for your peculiar sarcasm")
    ]

# Configuring the key
os.environ["GOOGLE_API_KEY"] = keyfile.GOOGLEKEY

# Create a model
llm = ChatGoogleGenerativeAI(
    model="gemini-1.5-pro",
    temperature=0.7,
    convert_system_message_to_human= True
)


# Response function
def load_answer(question):
    st.session_state.sessionMessages.append(HumanMessage(content=question))
    assistant_response = llm.invoke(st.session_state.sessionMessages)
    
    # Assuming assistant_response is an object with a 'content' attribute
    if hasattr(assistant_response, 'content') and isinstance(assistant_response.content, str):
        processed_content = assistant_response.content
        st.session_state.sessionMessages.append(AIMessage(content=processed_content))
    else:
        st.error("Invalid response received from AI.")
        processed_content = "Sorry, I couldn't process your request."

    return processed_content

# def load_answer(question):
#     st.session_state.sessionMessages.append(HumanMessage(content = question))
#     assistant_answer = llm.invoke(st.session_state.sessionMessages)
#     st.session_state.sessionMessages.append(AIMessage(content = assistant_answer))
#     return assistant_answer.content

# User message
def get_text():
    input_text = st.text_input("You: ", key = input)
    return input_text


# Implementation
user_input = get_text()
submit = st.button("Generate")

if submit:
    resp = load_answer(user_input)
    st.subheader("Answer: ")
    st.write(resp, key = 1)