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AI_Chat_Bot.py

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+import streamlit as st
+
+st.title("AI Chat Bot Dashboard")
+
+st.header('Interactive Chatbot')
+
+st.write('''An interactive chatbot is designed to engage in dynamic, back-and-forth conversations with users.
+            These chatbots can understand and retain context from previous interactions, making their responses more
+            relevant and coherent as the conversation progresses. Interactive chatbots often use advanced natural language 
+            processing (NLP) techniques and memory management to provide a more human-like experience. They are commonly used
+            in applications where ongoing interaction and context awareness are crucial, such as customer support, virtual 
+            assistants, and personalized recommendations.''')
+
+st.header('Non-Interactive Chatbot')
+
+st.write('''A non-interactive chatbot, on the other hand, is designed for more straightforward, single-turn interactions. 
+            These chatbots do not retain context from previous interactions, meaning each user query is treated independently. 
+            Non-interactive chatbots are typically used for simple, transactional tasks where context is not required. They are 
+            easier to develop and deploy and are suitable for scenarios where the interaction is brief and to the point.''')

pages/1_Interactive Chat Bot.py

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+import streamlit as st
+import os
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.prompts import MessagesPlaceholder
+from langchain.memory import ConversationBufferWindowMemory
+from operator import itemgetter
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+
+# Set the API key for Google Generative AI
+os.environ['GOOGLE_API_KEY'] = 'AIzaSyBHPIIk4-BOgXvnQ2_o6c2wTGpY2ByRIDs'
+
+# Initialize the Google Generative AI model
+model_gemini = ChatGoogleGenerativeAI(model='gemini-pro', temperature=0, max_output_tokens=500, convert_system_message_to_human=True)
+
+# Define the prompt
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ('system', 'you are a good assistant.'),
+        MessagesPlaceholder(variable_name='history'),
+        ("human", "{input}")
+    ]
+)
+
+# Initialize memory in session state
+if 'memory' not in st.session_state:
+    st.session_state.memory = ConversationBufferWindowMemory(k=10, return_messages=True)
+
+# Define the chain
+chain = (RunnablePassthrough.assign(history=RunnableLambda(st.session_state.memory.load_memory_variables) | itemgetter("history")) |
+         prompt | model_gemini)
+
+# Streamlit app
+st.title("Interactive Chatbot")
+
+# Initialize session state for user input
+if 'user_input' not in st.session_state:
+    st.session_state.user_input = ""
+
+# Input from user
+user_input = st.text_area("User: ", st.session_state.user_input, height=100)
+
+if st.button("Submit"):
+    response = chain.invoke({"input": user_input})
+    st.write(f"Assistant: {response.content}")
+    st.session_state.memory.save_context({"input": user_input}, {"output": response.content})
+    st.session_state.user_input = ""  # Clear the input box
+
+# Display chat history
+if st.checkbox("Show Chat History"):
+    chat_history = st.session_state.memory.load_memory_variables({})
+    st.write(chat_history)

pages/2_Non-Interactive Chat Bot.py

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+import streamlit as st
+import os
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.prompts import ChatPromptTemplate
+
+# Set GOOGLE_API_KEY 
+os.environ['GOOGLE_API_KEY'] = 'AIzaSyBHPIIk4-BOgXvnQ2_o6c2wTGpY2ByRIDs'
+
+st.title("Non-Interactive Chat Bot")
+
+model_gemini = ChatGoogleGenerativeAI(
+    model='gemini-pro',
+    temperature=0,
+    max_output_tokens=500,
+    convert_system_message_to_human=True
+)
+
+# Assuming model_gemini is defined correctly elsewhere
+prompt = ChatPromptTemplate.from_template("{content}")
+chain = prompt | model_gemini
+
+input_text = st.text_area('Ask your Question :', height=100)
+
+# Add a button to trigger the chatbot response
+if st.button('Submit'):
+    # Invoke the chain with input text
+    response = chain.invoke({'content': input_text})
+    
+    # Display response on the Streamlit web interface
+    st.write(response.content)