Add main application file

main.py

@@ -0,0 +1,148 @@
+import os
+from langchain.llms import OpenAI
+from langchain.chains import RetrievalQA
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.document_loaders import PyPDFLoader
+import panel as pn
+import PyPDF2
+import tempfile
+
+pn.extension(notifications=True)
+pn.extension('texteditor', template="bootstrap", sizing_mode='stretch_width')
+pn.state.template.param.update(
+    main_max_width="690px",
+    header_background="#F08080",
+)
+file_input = pn.widgets.FileInput(width=300)
+
+openaikey = pn.widgets.PasswordInput(
+    value="", placeholder="Enter your OpenAI API Key here...", width=300
+)
+prompt = pn.widgets.TextEditor(
+    value="", placeholder="Enter your questions here...", height=160, toolbar=False
+)
+run_button = pn.widgets.Button(name="Run!")
+
+select_k = pn.widgets.IntSlider(
+    name="Number of relevant chunks", start=1, end=5, step=1, value=2
+)
+select_chain_type = pn.widgets.RadioButtonGroup(
+    name='Chain type',
+    options=['stuff', 'map_reduce', "refine", "map_rerank"]
+)
+
+widgets = pn.Row(
+    pn.Column(prompt, run_button, margin=5),
+    pn.Card(
+        "Chain type:",
+        pn.Column(select_chain_type, select_k),
+        title="Advanced settings", margin=10
+    ), width=600
+)
+
+
+def is_valid_pdf(file_path):
+    try:
+        with open(file_path, 'rb') as f:
+            PyPDF2.PdfReader(f)
+        return True
+    except:
+        return False
+
+
+def qa(file, query, chain_type, k):
+    # load document
+    if not is_valid_pdf(file):
+        result = {'error': 'Invalid PDF file.'}
+        return result
+
+    loader = PyPDFLoader(file)
+    documents = loader.load()
+
+    # split the documents into chunks
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_documents(documents)
+
+    # select which embeddings we want to use
+    embeddings = OpenAIEmbeddings()
+
+    # create the vectorestore to use as the index
+    db = Chroma.from_documents(texts, embeddings)
+    # expose this index in a retriever interface
+    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
+    # create a chain to answer questions
+
+    qa = RetrievalQA.from_chain_type(
+        llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True)
+    result = qa({"query": query})
+    print(result['result'])
+    return result
+
+
+convos = []  # store all panel objects in a list
+
+
+def qa_result(_):
+    os.environ["OPENAI_API_KEY"] = openaikey.value
+    if not openaikey.value:
+        pn.state.notifications.error('Missing API key.', duration=2000)
+        return pn.Column(*convos, margin=15, width=575, min_height=400)
+
+    # save pdf file to a temp file
+    if file_input.value is not None:
+        file_input.save("temp.pdf")
+
+        prompt_text = prompt.value
+        if prompt_text:
+            result = qa(file="temp.pdf", query=prompt_text, chain_type=select_chain_type.value,
+                        k=select_k.value)
+            if result.get('error') is None:
+                convos.extend([
+                    pn.Row(
+                        pn.panel("\U0001F60A", width=10),
+                        prompt_text,
+                        width=600
+                    ),
+                    pn.Row(
+                        pn.panel("\U0001F916", width=10),
+                        pn.Column(
+                            result["result"],
+                            "Relevant source Panel Applicationtext:",
+                            pn.pane.Markdown(
+                                '\n--------------------------------------------------------------------\n'.join(
+                                    doc.page_content for doc in result["source_documents"]))
+                        )
+                    )
+                ])
+            else:
+                pn.state.notifications.error(result['error'], duration=2000)
+        else:
+            pn.state.notifications.error('Missing prompt.', duration=2000)
+    else:
+        pn.state.notifications.error('Missing file.', duration=2000)
+    return pn.Column(*convos, margin=15, width=575, min_height=400)
+
+
+qa_interactive = pn.panel(
+    pn.bind(qa_result, run_button),
+    loading_indicator=True,
+)
+
+output = pn.WidgetBox('*Output will show up here:*', qa_interactive, width=630, scroll=True)
+
+# layout
+pn.Column(
+    pn.pane.Markdown("""
+    ## \U0001F60A! Question Answering with your PDF file 
+    (original implementation: @sophiamyang)
+
+    1) Upload a PDF. 2) Enter OpenAI API key. 3) Type a question and click "Run".
+
+    """),
+    pn.Row(file_input, openaikey),
+    output,
+    widgets
+
+).servable()