Update app.py

app.py

@@ -1,40 +1,60 @@
-#Generics
+# Generics
 import os
 import keyfile
 import warnings
+import streamlit as st
 warnings.filterwarnings("ignore")
 
-#langchain pachages
+# Langchain packages
 from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.schema import HumanMessage , SystemMessage , ATMessage
+from langchain.schema import HumanMessage, SystemMessage, AIMessage
 
-#first message that will pop on the screen
+# First message that will pop on the screen
 st.set_page_config(page_title = "Magical Healer")
-st.header("welcome, what help do you need?")
-
-
-#general instruction
+st.header("Welcome, What help do you need?")
 
+# 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 medieva magical healer known for your peculiar sarcasm.")
+         SystemMessage(content = "You are a medieval magical healer known for your peculiar sarcasm")
     ]
-    
-#configuring the key
+
+# Configuring the key
 os.environ["GOOGLE_API_KEY"] = keyfile.GOOGLEKEY
 
-#Response function
+# 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.sessionMessage.append(HumanMessage(content = question))
+    # This is code, where we are adding new message to the model
+    st.session_state.sessionMessages.append(HumanMessage(content = question))
+    # We will get output from the model
     assistant_answer = llm.invoke(st.session_state.sessionMessages)
-    st.session_state.sessionMessages.append(AIMessge(content = assistant_answer))
-    return assistant_answer.content
+    # Appending the assistance answer in conversation
+    st.session_state.sessionMessages.append(AIMessage(content = assistant_answer))
 
-#user msg
+    return assistant_answer.content
 
+# User message
 def get_text():
     input_text = st.text_input("You: ", key = input)
-    return input_text
+    return str(input_text)
+
+
+# Implementation
+user_input = get_text()
+submit = st.button("Generate")
 
-#implementation
+if submit:
+    resp = load_answer(user_input)
+    st.subheader("Answer: ")
+    st.write(resp, key = 1)