Beta Version 6

app.py

@@ -1,42 +1,45 @@
-#Generic
+# 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
+# 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")
+# 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
+    st.session_state["sessionMessages"] = []
+# General Instruction
 if "sessionMessages" not in st.session_state:
-    st.session_state.sessionMessage=[
+    st.session_state.sessionMessage = [
         SystemMessage(content="You are a medievel magical healer known for your peculiar sarcasm")
     ]
 
-#Configure the key
+# 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
+    convert_system_message_to_human=True
 )
 
-#User message
+# User message
 def load_answer(question):
     st.session_state.sessionMessages.append(HumanMessage(content=question))
     assistant_response = llm.invoke(st.session_state.sessionMessages)
@@ -51,27 +54,16 @@ def load_answer(question):
 
     return processed_content
 
-#Working with Responses
-#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
-
+# Get user input
 def get_text():
-    input_text=st.text_input("You: ", key=input)
+    input_text = st.text_input("You: ", key="input")
     return input_text
 
-#Implementing
-user_input=get_text()
-submit= st.button("Generate")
+# Implementing
+user_input = get_text()
+submit = st.button("Generate")
 
 if submit:
-    resp=load_answer(user_input)
-    st.subheader("Answer: ")
-    st.write(resp,key=1)
-
-
-
-    
+    resp = load_answer(user_input)
+    st.subheader("Answer:")
+    st.write(resp, key=1)