Delete app.py

app.py

@@ -1,47 +0,0 @@
-import streamlit as st
-from langchain.llms import OpenAI
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import Chroma
-from langchain.chains import RetrievalQA
-
-def generate_response(uploaded_file, openai_api_key, query_text):
-    # Load document if file is uploaded
-    if uploaded_file is not None:
-        documents = [uploaded_file.read().decode()]
-        # Split documents into chunks
-        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-        texts = text_splitter.create_documents(documents)
-        # Select embeddings
-        embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
-        # Create a vectorstore from documents
-        db = Chroma.from_documents(texts, embeddings)
-        # Create retriever interface
-        retriever = db.as_retriever()
-        # Create QA chain
-        qa = RetrievalQA.from_chain_type(llm=OpenAI(openai_api_key=openai_api_key), chain_type='stuff', retriever=retriever)
-        return qa.run(query_text)
-
-
-# Page title
-st.set_page_config(page_title='🦜🔗 Ask the Doc App')
-st.title('🦜🔗 Ask the Doc App')
-
-# File upload
-uploaded_file = st.file_uploader('Upload an article', type='txt')
-# Query text
-query_text = st.text_input('Enter your question:', placeholder = 'Please provide a short summary.', disabled=not uploaded_file)
-
-# Form input and query
-result = []
-with st.form('myform', clear_on_submit=True):
-    openai_api_key = st.text_input('OpenAI API Key', type='password', disabled=not (uploaded_file and query_text))
-    submitted = st.form_submit_button('Submit', disabled=not(uploaded_file and query_text))
-    if submitted and openai_api_key.startswith('sk-'):
-        with st.spinner('Calculating...'):
-            response = generate_response(uploaded_file, openai_api_key, query_text)
-            result.append(response)
-            del openai_api_key
-
-if len(result):
-    st.info(response)