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"cell_type": "markdown",
"id": "8ec2fef2",
"metadata": {
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"slide_type": "slide"
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},
"source": [
"# Creating a Functional Conversational Chatbot\n",
"* **Created by:** Eric Martinez\n",
"* **For:** Software Engineering 2\n",
"* **At:** University of Texas Rio-Grande Valley"
]
},
{
"cell_type": "markdown",
"id": "cd292e78",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Tutorial: A Basic Conversational Chatbot with LLM (has limitations)"
]
},
{
"cell_type": "markdown",
"id": "dce44841",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"#### Installing Dependencies"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1bae55e3",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"!pip -q install --upgrade gradio\n",
"!pip -q install --upgrade openai"
]
},
{
"cell_type": "markdown",
"id": "f762bbca",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Creating a basic Chatbot UI using Gradio"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "feb92318",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7875\n",
"Running on public URL: https://8b8aa5ec7c7f25f014.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
]
},
{
"data": {
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"<div><iframe src=\"https://8b8aa5ec7c7f25f014.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
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"metadata": {},
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"source": [
"import gradio as gr\n",
"import openai\n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv() # take environment variables from .env.\n",
"\n",
"# Define a function to get the AI's reply using the OpenAI API\n",
"def get_ai_reply(message, model=\"gpt-3.5-turbo\", system_message=None, temperature=0, message_history=[]):\n",
" # Initialize the messages list\n",
" messages = []\n",
" \n",
" # Add the system message to the messages list\n",
" if system_message is not None:\n",
" messages += [{\"role\": \"system\", \"content\": system_message}]\n",
"\n",
" # Add the message history to the messages list\n",
" if message_history is not None:\n",
" messages += message_history\n",
" \n",
" # Add the user's message to the messages list\n",
" messages += [{\"role\": \"user\", \"content\": message}]\n",
" \n",
" # Make an API call to the OpenAI ChatCompletion endpoint with the model and messages\n",
" completion = openai.ChatCompletion.create(\n",
" model=model,\n",
" messages=messages,\n",
" temperature=temperature\n",
" )\n",
" \n",
" # Extract and return the AI's response from the API response\n",
" return completion.choices[0].message.content.strip()\n",
"\n",
"def chat(message, history):\n",
" history = history or []\n",
" ai_reply = get_ai_reply(message) \n",
" history.append((message, ai_reply))\n",
" return None, history, history\n",
" \n",
"with gr.Blocks() as demo:\n",
" with gr.Tab(\"Conversation\"):\n",
" with gr.Row():\n",
" with gr.Column():\n",
" chatbot = gr.Chatbot(label=\"Conversation\")\n",
" message = gr.Textbox(label=\"Message\")\n",
" history_state = gr.State()\n",
" btn = gr.Button(value =\"Send\")\n",
" btn.click(chat, inputs = [message, history_state], outputs = [message, chatbot, history_state])\n",
" demo.launch(share=True)\n"
]
},
{
"cell_type": "markdown",
"id": "77c9ec20",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Limitations\n",
"* Hardcoded to 'gpt-3.5-turbo' in the UI\n",
"* No error-handling on the API request\n",
"* While the OpenAI function takes message history, the UI doesn't pass it through\n",
"* Doesn't use or allow prompt or 'system' message customization"
]
},
{
"cell_type": "markdown",
"id": "66215904",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Tutorial: Improved Chatbot"
]
},
{
"cell_type": "markdown",
"id": "99f57faf",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The following snippet adds conversation history to the Gradio chat functionality, handles errors, and passes along the system message."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "9e55e844",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"# Define a function to handle the chat interaction with the AI model\n",
"def chat(model, system_message, message, chatbot_messages, history_state):\n",
" # Initialize chatbot_messages and history_state if they are not provided\n",
" chatbot_messages = chatbot_messages or []\n",
" history_state = history_state or []\n",
" \n",
" # Try to get the AI's reply using the get_ai_reply function\n",
" try:\n",
" ai_reply = get_ai_reply(message, model=model, system_message=system_message, message_history=history_state)\n",
" except Exception as e:\n",
" # If an error occurs, raise a Gradio error\n",
" raise gr.Error(e)\n",
" \n",
" # Append the user's message and the AI's reply to the chatbot_messages list\n",
" chatbot_messages.append((message, ai_reply))\n",
" \n",
" # Append the user's message and the AI's reply to the history_state list\n",
" history_state.append({\"role\": \"user\", \"content\": message})\n",
" history_state.append({\"role\": \"assistant\", \"content\": ai_reply})\n",
" \n",
" # Return None (empty out the user's message textbox), the updated chatbot_messages, and the updated history_state\n",
" return None, chatbot_messages, history_state"
]
},
{
"cell_type": "markdown",
"id": "44d9fde1",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The following snippet adjusts the Gradio interface to include examples (included in a separate file in this repo), model selection, prompts or 'system' messages, storing conversation history."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "d45439f3",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"import examples as chatbot_examples\n",
"\n",
"# Define a function to return a chatbot app using Gradio\n",
"def get_chatbot_app(additional_examples=[], share=False):\n",
" # Load chatbot examples and merge with any additional examples provided\n",
" examples = chatbot_examples.load_examples(additional=additional_examples)\n",
" \n",
" # Define a function to get the names of the examples\n",
" def get_examples():\n",
" return [example[\"name\"] for example in examples]\n",
"\n",
" # Define a function to choose an example based on the index\n",
" def choose_example(index):\n",
" system_message = examples[index][\"system_message\"].strip()\n",
" user_message = examples[index][\"message\"].strip()\n",
" return system_message, user_message, [], []\n",
"\n",
" # Create the Gradio interface using the Blocks layout\n",
" with gr.Blocks() as app:\n",
" with gr.Tab(\"Conversation\"):\n",
" with gr.Row():\n",
" with gr.Column():\n",
" # Create a dropdown to select examples\n",
" example_dropdown = gr.Dropdown(get_examples(), label=\"Examples\", type=\"index\")\n",
" # Create a button to load the selected example\n",
" example_load_btn = gr.Button(value=\"Load\")\n",
" # Create a textbox for the system message (prompt)\n",
" system_message = gr.Textbox(label=\"System Message (Prompt)\", value=\"You are a helpful assistant.\")\n",
" with gr.Column():\n",
" # Create a dropdown to select the AI model\n",
" model_selector = gr.Dropdown(\n",
" [\"gpt-3.5-turbo\", \"gpt-4\"],\n",
" label=\"Model\",\n",
" value=\"gpt-3.5-turbo\"\n",
" )\n",
" # Create a chatbot interface for the conversation\n",
" chatbot = gr.Chatbot(label=\"Conversation\")\n",
" # Create a textbox for the user's message\n",
" message = gr.Textbox(label=\"Message\")\n",
" # Create a state object to store the conversation history\n",
" history_state = gr.State()\n",
" # Create a button to send the user's message\n",
" btn = gr.Button(value=\"Send\")\n",
"\n",
" # Connect the example load button to the choose_example function\n",
" example_load_btn.click(choose_example, inputs=[example_dropdown], outputs=[system_message, message, chatbot, history_state])\n",
" # Connect the send button to the chat function\n",
" btn.click(chat, inputs=[model_selector, system_message, message, chatbot, history_state], outputs=[message, chatbot, history_state])\n",
" # Return the app\n",
" return app"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "e166c472",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7881\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
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"<div><iframe src=\"http://127.0.0.1:7881/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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"source": [
"# Call the launch_chatbot function to start the chatbot interface using Gradio\n",
"# Set the share parameter to False, meaning the interface will not be publicly accessible\n",
"get_chatbot_app().launch()"
]
}
],
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