Delete app.py

app.py

@@ -1,63 +0,0 @@
-import streamlit as st
-import os
-from langchain_groq import ChatGroq
-from langchain_community.document_loaders import WebBaseLoader
-from langchain_community.embeddings import OllamaEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_core.prompts import ChatPromptTemplate
-from langchain.chains import create_retrieval_chain
-from langchain_community.vectorstores import FAISS
-import time
-
-from dotenv import load_dotenv
-
-load_dotenv()
-
-## Load Groq API Key
-groq_api_key = os.environ['GROQ_API_KEY']
-
-if "vector" not in st.session_state:
-    st.session_state.embeddings=OllamaEmbeddings()
-    st.session_state.loader=WebBaseLoader("https://docs.smith.langchain.com/")
-    st.session_state.docs=st.session_state.loader.load()
-
-    st.session_state.text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    st.session_state.final_documents=st.session_state.text_splitter.split_documents(st.session_state.docs[:50])
-    st.session_state.vectors=FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
-
-
-st.title("Chatgroq Demo")
-llm=ChatGroq(groq_api_key=groq_api_key,
-             model="gemma-7b-it")
-
-prompt = ChatPromptTemplate.from_template(
-    """
-Answer the question based on the provided context only.
-Please provide the most accurate response based on the question.
-<context>
-{context}
-<context>
-Question: {input}
-    """
-)
-
-
-document_chain = create_stuff_documents_chain(llm, prompt)
-retriver = st.session_state.vectors.as_retriever()
-retriver_chain = create_retrieval_chain(retriver, document_chain)
-
-prompt=st.text_input("Input your prompt here")
-
-if prompt:
-    start=time.process_time()
-    response = retriver_chain.invoke({"input": prompt})
-    print("Response time :",time.process_time() - start)
-    st.write(response['answer'])
-
-    # With a Streamlit expander
-    with st.expander("Document Similarity Search"):
-        # Find the relevant chunks
-        for i, doc in enumerate(response["context"]):
-            st.write(doc.page_content)
-            st.write("------------------------------------")