Update app.py

app.py

@@ -1,107 +1,4 @@
-import os
 import streamlit as st
-from langchain_chroma import Chroma
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.memory import ConversationBufferMemory
-from langchain.prompts import PromptTemplate
-from langchain_groq import ChatGroq
-from dotenv import load_dotenv
-from sentence_transformers import SentenceTransformer
 
-st.title("Chatbot")
-
-# Load environment variables
-load_dotenv()
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-assert GROQ_API_KEY, "GROQ_API_KEY environment variable not set."
-
-# One-time setup in session state
-if 'initialized' not in st.session_state:
-    st.session_state.initialized = False
-
-    try:
-        with st.spinner("Initializing..."):
-            # Initialize embeddings model
-            model_path = "sentence-transformers/all-MiniLM-L12-v2"  
-            st.session_state.embedding_function = HuggingFaceEmbeddings(
-                model_name=model_path,
-                model_kwargs={'device': 'cpu'},
-                encode_kwargs={'normalize_embeddings': False}
-            )
-            
-            # Set up document search
-            persist_directory = "doc_db"
-            st.session_state.docsearch = Chroma(
-                persist_directory=persist_directory,
-                embedding_function=st.session_state.embedding_function
-            )
-
-            # Initialize ChatGroq model
-            st.session_state.chat_model = ChatGroq(
-                model="llama-3.1-8b-instant",
-                temperature=0,
-                api_key=GROQ_API_KEY
-            )
-
-            # Define prompt template and memory
-            template = """You are a chatbot having a conversation with a human. Your name is Devrim.
-            Given the following extracted parts of a long document and a question, create a final answer. If the answer is not in the document or irrelevant, just say that you don't know, don't try to make up an answer.
-            {context}
-            {chat_history}
-            Human: {human_input}
-            Chatbot:"""
-
-            prompt = PromptTemplate(
-                input_variables=["chat_history", "human_input", "context"], template=template
-            )
-            st.session_state.memory = ConversationBufferMemory(memory_key="chat_history", input_key="human_input")
-
-            # Load QA chain
-            st.session_state.qa_chain = load_qa_chain(
-                llm=st.session_state.chat_model, 
-                chain_type="question_answering", 
-                memory=st.session_state.memory, 
-                prompt=prompt
-            )
-
-            st.session_state.initialized = True
-            st.success("Initialization successful.")
-
-    except Exception as e:
-        st.session_state.initialized = False
-        st.error(f"Initialization failed: {e}")
-
-# Clear chat history buttons
-if st.button("Clear Chat History"):
-    if 'memory' in st.session_state and st.session_state.memory:
-        st.session_state.memory.clear()
-    st.experimental_rerun()  # Refresh the app to reflect the cleared history
-
-# Display chat history if initialized
-if st.session_state.initialized and 'memory' in st.session_state and st.session_state.memory:
-    if st.session_state.memory.buffer_as_messages:
-        for message in st.session_state.memory.buffer_as_messages:
-            if message.type == "ai":
-                st.chat_message(name="ai", avatar="🤖").write(message.content)
-            else:
-                st.chat_message(name="human", avatar="👤").write(message.content)
-
-# Input for new query
-if 'initialized' in st.session_state and st.session_state.initialized:
-    query = st.chat_input("Ask something")
-    if query:
-        try:
-            with st.spinner("Answering..."):
-                # Perform similarity search and get response
-                docs = st.session_state.docsearch.similarity_search(query, k=1)  
-                response = st.session_state.qa_chain(
-                    {"input_documents": docs, "human_input": query}, 
-                    return_only_outputs=True
-                )["output_text"]
-
-                # Display new message
-                st.chat_message(name="human", avatar="👤").write(query)
-                st.chat_message(name="ai", avatar="🤖").write(response)
-
-        except Exception as e:
-            st.error(f"An error occurred: {e}")
+x = st.slider('Select a value')
+st.write(x, 'squared is', x * x)