Create app.py

app.py

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+import numpy as np
+import pandas as pd
+import configparser
+import streamlit as st
+import streamlit
+import chromadb
+import langchain
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import PyPDFLoader
+import tempfile
+import shutil
+import os
+import openai
+
+# You need your own API key
+api_key = st.secrets["OPENAI_API_KEY"]  # Get API key from Streamlit secrets
+
+if not api_key:
+    st.error("I can not find API key")
+else:
+    openai.api_key = api_key
+
+    st.title('PDF Documents Q&A App')
+
+    uploaded_file = st.file_uploader("Please upload your PDF file", type=["pdf"])
+    if uploaded_file is not None:
+        # Save as tempfile
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmpfile:
+            shutil.copyfileobj(uploaded_file, tmpfile)
+            file_path = tmpfile.name
+
+        # PyPDFLoader
+        loader = PyPDFLoader(file_path)
+        pages = loader.load_and_split()
+
+        # Model and vectorstore
+        embeddings = OpenAIEmbeddings()
+        vectorstore = Chroma.from_documents(pages, embedding=embeddings, persist_directory=".")
+        vectorstore.persist()
+        llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
+
+        # Q&A chain
+        pdf_qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)
+
+        # Get question from user
+        question = st.text_input("Please input your question:")
+        if st.button('Get Answer'):
+            result = pdf_qa({"question": question, "chat_history": []})
+            answer = result["answer"]
+            st.write("Answer:", answer)
+
+        # Delete tempfile
+        os.remove(file_path)  # Clean up the temporary file