diff --git a/week5/community-contributions/markdown_knowledge_worker.ipynb b/week5/community-contributions/markdown_knowledge_worker.ipynb
new file mode 100644
index 0000000..51597f5
--- /dev/null
+++ b/week5/community-contributions/markdown_knowledge_worker.ipynb
@@ -0,0 +1,359 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "c25c6e94-f3de-4367-b2bf-269ba7160977",
+ "metadata": {},
+ "source": [
+ "## An Expert Knowledge Worker Question-Answering Agent using RAG"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "15169580-cf11-4dee-8ec7-3a4ef59b19ee",
+ "metadata": {},
+ "source": [
+ "Aims\n",
+ "- Reads README.md files and loads data using TextLoader\n",
+ "- Splits into chunks using CharacterTextSplitter\n",
+ "- Converts chunks into vector embeddings and creates a datastore\n",
+ "- 2D and 3D visualisations\n",
+ "- Langchain to set up a conversation retrieval chain"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "051cf881-357d-406b-8eae-1610651e40f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# imports\n",
+ "\n",
+ "import os\n",
+ "import glob\n",
+ "from dotenv import load_dotenv\n",
+ "import gradio as gr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ccfd403a-5bdb-4a8c-b3fd-d47ae79e43f7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# imports for langchain, plotly and Chroma\n",
+ "\n",
+ "from langchain.document_loaders import DirectoryLoader, TextLoader\n",
+ "from langchain.text_splitter import CharacterTextSplitter\n",
+ "from langchain.schema import Document\n",
+ "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
+ "from langchain.embeddings import HuggingFaceEmbeddings\n",
+ "from langchain_chroma import Chroma\n",
+ "from langchain.memory import ConversationBufferMemory\n",
+ "from langchain.chains import ConversationalRetrievalChain\n",
+ "import numpy as np\n",
+ "from sklearn.manifold import TSNE\n",
+ "import plotly.graph_objects as go\n",
+ "import plotly.express as px\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2d853868-d2f6-43e1-b27c-b8e91d06b724",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MODEL = \"gpt-4o-mini\"\n",
+ "db_name = \"vector_db\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f152fc3b-0bf4-4d51-948f-95da1ebc030a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load environment variables in a file called .env\n",
+ "\n",
+ "load_dotenv(override=True)\n",
+ "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "24e621ac-df06-4af6-a60d-a9ed7adb884a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read in documents using LangChain's loaders\n",
+ "\n",
+ "folder = \"my-knowledge-base/\"\n",
+ "text_loader_kwargs={'autodetect_encoding': True}\n",
+ "\n",
+ "loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n",
+ "folder_docs = loader.load()\n",
+ "\n",
+ "for doc in folder_docs:\n",
+ " filename_md = os.path.basename(doc.metadata[\"source\"]) \n",
+ " filename, _ = os.path.splitext(filename_md) \n",
+ " doc.metadata[\"filename\"] = filename\n",
+ "\n",
+ "documents = folder_docs \n",
+ "\n",
+ "text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=200)\n",
+ "chunks = text_splitter.split_documents(documents)\n",
+ "\n",
+ "print(f\"Total number of chunks: {len(chunks)}\")\n",
+ "print(f\"Files found: {set(doc.metadata['filename'] for doc in documents)}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f02f08ee-5ade-4f79-a500-045a8f1a532f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n",
+ "\n",
+ "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+ "\n",
+ "# Delete if already exists\n",
+ "\n",
+ "if os.path.exists(db_name):\n",
+ " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n",
+ "\n",
+ "# Create vectorstore\n",
+ "\n",
+ "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n",
+ "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7f665f4d-ccb1-43fb-b901-040117925732",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Let's investigate the vectors\n",
+ "\n",
+ "collection = vectorstore._collection\n",
+ "count = collection.count()\n",
+ "\n",
+ "sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n",
+ "dimensions = len(sample_embedding)\n",
+ "print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6208a971-e8b7-48bc-be7a-6dcb82967fd2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pre work\n",
+ "\n",
+ "result = collection.get(include=['embeddings','documents','metadatas'])\n",
+ "vectors = np.array(result['embeddings']) \n",
+ "documents = result['documents']\n",
+ "metadatas = result['metadatas']\n",
+ "filenames = [metadata['filename'] for metadata in metadatas]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eb27bc8a-453b-4b19-84b4-dc495bb0e544",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "def random_color():\n",
+ " return f\"rgb({random.randint(0,255)},{random.randint(0,255)},{random.randint(0,255)})\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "78db67e5-ef10-4581-b8ac-3e0281ceba45",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def show_embeddings_2d(result):\n",
+ " vectors = np.array(result['embeddings']) \n",
+ " documents = result['documents']\n",
+ " metadatas = result['metadatas']\n",
+ " filenames = [metadata['filename'] for metadata in metadatas]\n",
+ " filenames_unique = sorted(set(filenames))\n",
+ "\n",
+ " # color assignment\n",
+ " color_map = {name: random_color() for name in filenames_unique}\n",
+ " colors = [color_map[name] for name in filenames]\n",
+ "\n",
+ " tsne = TSNE(n_components=2, random_state=42,perplexity=4)\n",
+ " reduced_vectors = tsne.fit_transform(vectors)\n",
+ "\n",
+ " # Create the 2D scatter plot\n",
+ " fig = go.Figure(data=[go.Scatter(\n",
+ " x=reduced_vectors[:, 0],\n",
+ " y=reduced_vectors[:, 1],\n",
+ " mode='markers',\n",
+ " marker=dict(size=5,color=colors, opacity=0.8),\n",
+ " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
+ " hoverinfo='text'\n",
+ " )])\n",
+ "\n",
+ " fig.update_layout(\n",
+ " title='2D Chroma Vector Store Visualization',\n",
+ " scene=dict(xaxis_title='x',yaxis_title='y'),\n",
+ " width=800,\n",
+ " height=600,\n",
+ " margin=dict(r=20, b=10, l=10, t=40)\n",
+ " )\n",
+ "\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2c250166-cb5b-4a75-8981-fae2d6dfe509",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "show_embeddings_2d(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b290e38-0800-4453-b664-7a7622ff5ed2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def show_embeddings_3d(result):\n",
+ " vectors = np.array(result['embeddings']) \n",
+ " documents = result['documents']\n",
+ " metadatas = result['metadatas']\n",
+ " filenames = [metadata['filename'] for metadata in metadatas]\n",
+ " filenames_unique = sorted(set(filenames))\n",
+ "\n",
+ " # color assignment\n",
+ " color_map = {name: random_color() for name in filenames_unique}\n",
+ " colors = [color_map[name] for name in filenames]\n",
+ "\n",
+ " tsne = TSNE(n_components=3, random_state=42)\n",
+ " reduced_vectors = tsne.fit_transform(vectors)\n",
+ "\n",
+ " fig = go.Figure(data=[go.Scatter3d(\n",
+ " x=reduced_vectors[:, 0],\n",
+ " y=reduced_vectors[:, 1],\n",
+ " z=reduced_vectors[:, 2],\n",
+ " mode='markers',\n",
+ " marker=dict(size=5, color=colors, opacity=0.8),\n",
+ " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
+ " hoverinfo='text'\n",
+ " )])\n",
+ "\n",
+ " fig.update_layout(\n",
+ " title='3D Chroma Vector Store Visualization',\n",
+ " scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
+ " width=900,\n",
+ " height=700,\n",
+ " margin=dict(r=20, b=10, l=10, t=40)\n",
+ " )\n",
+ "\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "45d1d034-2503-4176-b1e4-f248e31c4770",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "show_embeddings_3d(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e79946a1-f93a-4b3a-8d19-deef40dec223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a new Chat with OpenAI\n",
+ "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n",
+ "\n",
+ "# set up the conversation memory for the chat\n",
+ "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
+ "\n",
+ "# the retriever is an abstraction over the VectorStore that will be used during RAG\n",
+ "retriever = vectorstore.as_retriever(search_kwargs={\"k\": 50})\n",
+ "\n",
+ "# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n",
+ "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "59f90c85-c113-4482-8574-8a728ef25459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def chat(question, history):\n",
+ " result = conversation_chain.invoke({\"question\": question})\n",
+ " return result[\"answer\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0520a8ff-01a4-4fa6-9dc8-57da87272edc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b4949b17-cd9c-4bff-bd5b-0f80df72e7dc",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week5/community-contributions/ui_markdown_knowledge_worker.ipynb b/week5/community-contributions/ui_markdown_knowledge_worker.ipynb
new file mode 100644
index 0000000..5bf6f56
--- /dev/null
+++ b/week5/community-contributions/ui_markdown_knowledge_worker.ipynb
@@ -0,0 +1,353 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d13be0fd-db15-4ab1-860a-b00257051339",
+ "metadata": {},
+ "source": [
+ "## Gradio UI for Markdown-Based Q&A with Visualization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bc63fbdb-66a9-4c10-8dbd-11476b5e2d21",
+ "metadata": {},
+ "source": [
+ "This interface enables users to:\n",
+ "- Upload Markdown files for processing\n",
+ "- Visualize similarity between document chunks in 2D and 3D using embeddings\n",
+ "- Ask questions and receive RAG enabled responses\n",
+ "- Mantain conversation context for better question answering\n",
+ "- Clear chat history when required for fresh sessions\n",
+ "- Store and retrieve embeddings using ChromaDB\n",
+ "\n",
+ "Integrates LangChain, ChromaDB, and OpenAI to process, store, and retrieve information efficiently."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91da28d8-8e29-44b7-a62a-a3a109753727",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# imports\n",
+ "\n",
+ "import os\n",
+ "from dotenv import load_dotenv\n",
+ "import gradio as gr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e47f670a-e2cb-4700-95d0-e59e440677a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# imports for langchain, plotly and Chroma\n",
+ "\n",
+ "from langchain.document_loaders import DirectoryLoader, TextLoader\n",
+ "from langchain.text_splitter import CharacterTextSplitter\n",
+ "from langchain.schema import Document\n",
+ "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
+ "from langchain.embeddings import HuggingFaceEmbeddings\n",
+ "from langchain_chroma import Chroma\n",
+ "from langchain.memory import ConversationBufferMemory\n",
+ "from langchain.chains import ConversationalRetrievalChain\n",
+ "import numpy as np\n",
+ "from sklearn.manifold import TSNE\n",
+ "import plotly.graph_objects as go\n",
+ "import plotly.express as px\n",
+ "import matplotlib.pyplot as plt\n",
+ "from random import randint\n",
+ "import shutil"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "362d4976-2553-4ed8-8fbb-49806145cad1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install --upgrade gradio"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "968b6e96-557e-439f-b2f1-942c05168641",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MODEL = \"gpt-4o-mini\"\n",
+ "db_name = \"vector_db\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "537f66de-6abf-4b34-8e05-6b9a9df8ae82",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load environment variables in a file called .env\n",
+ "\n",
+ "load_dotenv(override=True)\n",
+ "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "246c1c1b-fcfa-4f4c-b99c-024598751361",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "folder = \"my-knowledge-base/\"\n",
+ "db_name = \"vectorstore_db\"\n",
+ "\n",
+ "def process_files(files):\n",
+ " os.makedirs(folder, exist_ok=True)\n",
+ "\n",
+ " processed_files = []\n",
+ " for file in files:\n",
+ " file_path = os.path.join(folder, os.path.basename(file)) # Get filename\n",
+ " shutil.copy(file, file_path)\n",
+ " processed_files.append(os.path.basename(file))\n",
+ "\n",
+ " # Load documents using LangChain's DirectoryLoader\n",
+ " text_loader_kwargs = {'autodetect_encoding': True}\n",
+ " loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n",
+ " folder_docs = loader.load()\n",
+ "\n",
+ " # Assign filenames as metadata\n",
+ " for doc in folder_docs:\n",
+ " filename_md = os.path.basename(doc.metadata[\"source\"])\n",
+ " filename, _ = os.path.splitext(filename_md)\n",
+ " doc.metadata[\"filename\"] = filename\n",
+ "\n",
+ " documents = folder_docs \n",
+ "\n",
+ " # Split documents into chunks\n",
+ " text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=200)\n",
+ " chunks = text_splitter.split_documents(documents)\n",
+ "\n",
+ " # Initialize embeddings\n",
+ " embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+ "\n",
+ " # Delete previous vectorstore\n",
+ " if os.path.exists(db_name):\n",
+ " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n",
+ "\n",
+ " # Store in ChromaDB\n",
+ " vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n",
+ "\n",
+ " # Retrieve results\n",
+ " collection = vectorstore._collection\n",
+ " result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n",
+ "\n",
+ " llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n",
+ " memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
+ " retriever = vectorstore.as_retriever(search_kwargs={\"k\": 35})\n",
+ " global conversation_chain\n",
+ " conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)\n",
+ "\n",
+ " processed_text = \"**Processed Files:**\\n\\n\" + \"\\n\".join(f\"- {file}\" for file in processed_files)\n",
+ " return result, processed_text"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48678d3a-0ab2-4aa4-aa9e-4160c6a9cb24",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def random_color():\n",
+ " return f\"rgb({randint(0,255)},{randint(0,255)},{randint(0,255)})\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6caed889-9bb4-42ad-b1c2-da051aefc802",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def show_embeddings_2d(result):\n",
+ " vectors = np.array(result['embeddings']) \n",
+ " documents = result['documents']\n",
+ " metadatas = result['metadatas']\n",
+ " filenames = [metadata['filename'] for metadata in metadatas]\n",
+ " filenames_unique = sorted(set(filenames))\n",
+ "\n",
+ " # color assignment\n",
+ " color_map = {name: random_color() for name in filenames_unique}\n",
+ " colors = [color_map[name] for name in filenames]\n",
+ "\n",
+ " tsne = TSNE(n_components=2, random_state=42,perplexity=4)\n",
+ " reduced_vectors = tsne.fit_transform(vectors)\n",
+ "\n",
+ " # Create the 2D scatter plot\n",
+ " fig = go.Figure(data=[go.Scatter(\n",
+ " x=reduced_vectors[:, 0],\n",
+ " y=reduced_vectors[:, 1],\n",
+ " mode='markers',\n",
+ " marker=dict(size=5,color=colors, opacity=0.8),\n",
+ " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
+ " hoverinfo='text'\n",
+ " )])\n",
+ "\n",
+ " fig.update_layout(\n",
+ " title='2D Chroma Vector Store Visualization',\n",
+ " scene=dict(xaxis_title='x',yaxis_title='y'),\n",
+ " width=800,\n",
+ " height=600,\n",
+ " margin=dict(r=20, b=10, l=10, t=40)\n",
+ " )\n",
+ "\n",
+ " return fig"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "de993495-c8cd-4313-a6bb-7d27494ecc13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def show_embeddings_3d(result):\n",
+ " vectors = np.array(result['embeddings']) \n",
+ " documents = result['documents']\n",
+ " metadatas = result['metadatas']\n",
+ " filenames = [metadata['filename'] for metadata in metadatas]\n",
+ " filenames_unique = sorted(set(filenames))\n",
+ "\n",
+ " # color assignment\n",
+ " color_map = {name: random_color() for name in filenames_unique}\n",
+ " colors = [color_map[name] for name in filenames]\n",
+ "\n",
+ " tsne = TSNE(n_components=3, random_state=42)\n",
+ " reduced_vectors = tsne.fit_transform(vectors)\n",
+ "\n",
+ " fig = go.Figure(data=[go.Scatter3d(\n",
+ " x=reduced_vectors[:, 0],\n",
+ " y=reduced_vectors[:, 1],\n",
+ " z=reduced_vectors[:, 2],\n",
+ " mode='markers',\n",
+ " marker=dict(size=5, color=colors, opacity=0.8),\n",
+ " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
+ " hoverinfo='text'\n",
+ " )])\n",
+ "\n",
+ " fig.update_layout(\n",
+ " title='3D Chroma Vector Store Visualization',\n",
+ " scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
+ " width=900,\n",
+ " height=700,\n",
+ " margin=dict(r=20, b=10, l=10, t=40)\n",
+ " )\n",
+ "\n",
+ " return fig"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7b7bf62b-c559-4e97-8135-48cd8d97a40e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def chat(question, history):\n",
+ " result = conversation_chain.invoke({\"question\": question})\n",
+ " return result[\"answer\"]\n",
+ "\n",
+ "def visualise_data(result):\n",
+ " fig_2d = show_embeddings_2d(result)\n",
+ " fig_3d = show_embeddings_3d(result)\n",
+ " return fig_2d,fig_3d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "99217109-fbee-4269-81c7-001e6f768a72",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "css = \"\"\"\n",
+ ".btn {background-color: #1d53d1;}\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1429ea1-1d9f-4be6-b270-01997864c642",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with gr.Blocks(css=css) as ui:\n",
+ " gr.Markdown(\"# Markdown-Based Q&A with Visualization\")\n",
+ " with gr.Row():\n",
+ " file_input = gr.Files(file_types=[\".md\"], label=\"Upload Markdown Files\")\n",
+ " with gr.Column(scale=1):\n",
+ " processed_output = gr.Markdown(\"Progress\")\n",
+ " with gr.Row():\n",
+ " process_btn = gr.Button(\"Process Files\",elem_classes=[\"btn\"])\n",
+ " with gr.Row():\n",
+ " question = gr.Textbox(label=\"Chat \", lines=10)\n",
+ " answer = gr.Markdown(label= \"Response\")\n",
+ " with gr.Row():\n",
+ " question_btn = gr.Button(\"Ask a Question\",elem_classes=[\"btn\"])\n",
+ " clear_btn = gr.Button(\"Clear Output\",elem_classes=[\"btn\"])\n",
+ " with gr.Row():\n",
+ " plot_2d = gr.Plot(label=\"2D Visualization\")\n",
+ " plot_3d = gr.Plot(label=\"3D Visualization\")\n",
+ " with gr.Row():\n",
+ " visualise_btn = gr.Button(\"Visualise Data\",elem_classes=[\"btn\"])\n",
+ "\n",
+ " result = gr.State([])\n",
+ " # Action: When button is clicked, process files and update visualization\n",
+ " clear_btn.click(fn=lambda:(\"\", \"\"), inputs=[],outputs=[question, answer])\n",
+ " process_btn.click(process_files, inputs=[file_input], outputs=[result,processed_output])\n",
+ " question_btn.click(chat, inputs=[question], outputs= [answer])\n",
+ " visualise_btn.click(visualise_data, inputs=[result], outputs=[plot_2d,plot_3d])\n",
+ "\n",
+ "# Launch Gradio app\n",
+ "ui.launch(inbrowser=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3686048-ac29-4df1-b816-e58996913ef1",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}