Upload 4 files

app-v1.py

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+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)

config.toml

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+[theme]
+primaryColor="#F63366"
+backgroundColor="#FFFFFF"
+secondaryBackgroundColor="#F0F2F6"
+textColor="#262730"
+font="sans serif"

requirements (2).txt

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+streamlit
+langchain
+openai
+chromadb
+tiktoken

streamlit_app.py

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+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)