added code, dockerfile and requirements.txt

.gitignore

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+.gitattributes
+.vscode/
+*.ipynb

Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+#CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["panel", "serve", "/code/app.py", "--address", "0.0.0.0","--port", "7860", "--allow-websocket-origin","sheraznaseer-test-pdfqa-2304.hf.space"]

app.py

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+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import TextLoader
+from langchain.document_loaders import PyPDFLoader
+from langchain.vectorstores.faiss import FAISS
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+import panel as pn
+import os
+import tempfile
+from langchain.chains import RetrievalQA
+
+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 qa(file, query, chain_type, k):
+    loader = PyPDFLoader(file)
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_documents(documents)
+    embeddings = OpenAIEmbeddings()
+    vector_store = FAISS.from_documents(texts, embeddings)
+    retriever = vector_store.as_retriever(
+        search_type="similarity", search_kwargs={"k": k})
+    model = ChatOpenAI(model='gpt-3.5-turbo')
+    # qa = ConversationalRetrievalChain.from_llm(
+    #    model, retriever=retriever, chain_type=chain_type)
+    qa = RetrievalQA.from_chain_type(
+        model, chain_type=chain_type, retriever=retriever, return_source_documents=False)
+
+    result = qa({"query": query})
+    # print(result['result'])
+    # return result['answer']
+    return result['result']
+
+
+convos = []  # store all panel objects in a list
+
+
+def temfile_create(file_input):
+    with tempfile.NamedTemporaryFile(suffix='.pdf', delete=False) as temp_file:
+        temp_file.write(file_input.value)
+        temp_file.flush()
+        temp_file.seek(0)
+        # Do something with the temporary file here, such as passing the file path to another function
+        return temp_file.name
+
+
+def qa_result(_):
+    os.environ["OPENAI_API_KEY"] = openaikey.value
+
+    if file_input.value is not None:
+        # file_input.save("/.cache/temp.pdf")
+        pdf_file = temfile_create(file_input)
+        prompt_text = prompt.value
+
+        if prompt_text:
+            result = qa(file=pdf_file, query=prompt_text,
+                        chain_type=select_chain_type.value, k=select_k.value)
+            convos.extend([
+                pn.Row(
+                    pn.panel("\U0001F60A", width=10),
+                    prompt_text,
+                    width=600
+                ),
+                pn.Row(
+                    pn.panel("\U0001F916", width=10),
+                    # result['answer'],
+                    result,
+                    width=600
+                )
+            ])
+    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
+
+# try:
+pn.Column(
+    pn.pane.Markdown("""
+    ## \U0001F60A! Question Answering with your PDF file
+    
+    Step 1: Upload a PDF file \n
+    Step 2: Enter your OpenAI API key. This costs $$. You will need to set up billing info at [OpenAI](https://platform.openai.com/account). \n
+    Step 3: Type your question at the bottom and click "Run" \n
+    
+    """),
+    pn.Row(file_input, openaikey),
+    output,
+    widgets
+
+).servable()
+# except Exception as ex:
+#    pass

requirements.txt

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+langchain==0.0.149
+panel
+openai
+pypdf
+tiktoken
+notebook
+faiss-cpu