# Generic import os import keyfile import streamlit as st import warnings # Add this import at the beginning of your code from pydantic import BaseModel warnings.filterwarnings("ignore") # Langchain Packages from langchain_google_genai import ChatGoogleGenerativeAI from langchain.schema import HumanMessage, SystemMessage, AIMessage from pydantic import BaseModel # Import BaseModel # First message that will pop on the screen st.set_page_config(page_title="Magical Healer") st.header("Welcome, How can I help you?") # Define the AIMessage class class AIMessage(BaseModel): content: str 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 medievel magical healer known for your peculiar sarcasm") ] # Configure the key os.environ["GOOGLE_API_KEY"] = keyfile.GOOGLEKEY # Create the model llm = ChatGoogleGenerativeAI( model="gemini-1.5-pro", temperature=0.7, convert_system_message_to_human=True ) # User message 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 # Get user input def get_text(): input_text = st.text_input("You: ", key="input") return input_text # Implementing user_input = get_text() submit = st.button("Generate") if submit: resp = load_answer(user_input) st.subheader("Answer:") st.write(resp, key=1)