Add application file

Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+RUN python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["panel", "serve", "/code/RFPAgent.py", "--address", "0.0.0.0", "--port", "7860", "--allow-websocket-origin", "chukypedro-panel-doc-agent-qa.hf.space",  "--allow-websocket-origin", "0.0.0.0:7860"]
+
+RUN mkdir /.cache
+RUN chmod 777 /.cache
+RUN mkdir .chroma
+RUN chmod 777 .chroma

README copy.md

@@ -0,0 +1,10 @@
+---
+title: Panel Agent Document QA
+emoji: 📈
+colorFrom: pink
+colorTo: red
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

RFPAgent.py

@@ -0,0 +1,188 @@
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
+from langchain.llms import OpenAI
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, BSHTMLLoader, UnstructuredImageLoader
+# Import things that are needed generically
+from langchain.memory import ConversationBufferMemory
+from langchain.agents import initialize_agent, Tool
+from langchain.agents import AgentType
+from langchain import LLMMathChain
+#setting a memory for conversations
+import panel as pn
+import os
+from dotenv import load_dotenv
+load_dotenv()
+memory = ConversationBufferMemory(memory_key="chat_history")
+   
+    
+def qa_agent(file, query, chain_type, k):
+    """_summary_
+
+    Args:
+        file (_type_): _description_
+        query (_type_): _description_
+        chain_type (_type_): _description_
+        k (_type_): _description_
+
+    Returns:
+        _type_: _description_
+    """
+    llm = OpenAI(temperature=0)
+    llm_math_chain = LLMMathChain(llm=OpenAI(temperature=0))
+
+    # load document
+    if file.endswith('pdf'):
+        loader = PyPDFLoader(file)
+    elif file.endswith('docx'):
+        loader = Docx2txtLoader(file)
+    elif file.endswith('jpg') or file.endswith('jpg'):
+        loader = UnstructuredImageLoader(file, mode="elements")
+    else:
+        raise ValueError
+    
+    documents = loader.load()
+    # split the documents into chunks
+    text_splitter = CharacterTextSplitter(chunk_size=3228, 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=llm, chain_type=chain_type, retriever=retriever)
+    
+    '--------------------------------- CREATE AGENT ---------------------------------'
+    tools = [
+    Tool(
+        name = "Demo",
+        func=qa.run,
+        description="use this as the primary source of context information when you are asked the question. \
+                    Always search for the answers using only the provided tool, don't make up answers yourself"
+        
+    ),
+    
+    Tool(
+        name="Calculator",
+        func=llm_math_chain.run,
+        description="Useful for answering math-related questions within the given document. Avoid speculating beyond the document's content. If you don't know the answer to a question, simply state 'I don't know'.",
+       return_direct=True #return tool directly to the user
+    )
+
+    ]
+    # Construct the agent. We will use the default agent type here.
+    # See documentation for a full list of options.
+
+    agent = initialize_agent(
+        tools,
+        agent= AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        llm=llm,
+        memory=memory,
+        verbose=True,
+        )
+    
+    result = agent.run(input = query)
+
+    return result
+
+#'Explain what the proposed Approach in this Paper is all about'
+
+'------------------------------ Panel App ---------------------------------'
+
+pn.extension('texteditor', template="bootstrap", sizing_mode='stretch_width',theme='dark' )
+pn.state.template.param.update(
+    main_max_width="690px",
+    header_background="blue",
+    title='DocumentAgent Application'
+)
+
+#######Widget###########
+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!", margin=(25, 50), background='#f0f0f0', button_type='primary')
+
+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"],button_type='success'
+)
+
+
+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
+)
+
+convos = []  # store all panel objects in a list
+
+def agent_app(_):
+    os.environ["OPENAI_API_KEY"] = openaikey.value
+    
+    # save pdf file to a temp file 
+    if file_input.value is not None:
+        file_input.save(f"/.cache/{file_input.filename}")
+    
+        prompt_text = prompt.value
+        if prompt_text:
+            result = qa_agent(file=f"/.cache/{file_input.filename}", 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),
+                    pn.Column(
+                        "Relevant source text:",
+                        pn.pane.Markdown(result)
+                    )
+                )
+            ])
+            #return convos
+    return pn.Column(*convos, margin=15, width=575, min_height=400)
+
+
+qa_interactive = pn.panel(
+    pn.bind(agent_app, run_button),
+    loading_indicator=True,
+)
+
+output = pn.WidgetBox('*Output will show up here:*', qa_interactive, width=630, scroll=True)
+# Apply CSS styles to the WidgetBox
+output.background = 'blue'
+# layout
+pn.Column(
+    pn.pane.Markdown("""
+    ## \U0001F60A! Question Answering Agent with your Document file
+    
+    1) Upload a Document in [pdf, docx, .jpg, html] format. 2) Enter OpenAI API key. This costs $. Set up billing at [OpenAI](https://platform.openai.com/account). 3) Type a question and click "Run".
+    
+    """),
+    
+    pn.Row(file_input,openaikey),
+    output,
+    widgets,
+    css_classes=['body']).servable()
+
+
+
+
+
+

requirements.txt

@@ -0,0 +1,9 @@
+langchain
+panel
+python-dotenv
+openai
+chromadb
+pypdf
+tiktoken
+panel
+unstructured