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{
"cells": [
{
"cell_type": "markdown",
"id": "d006b2ea-9dfe-49c7-88a9-a5a0775185fd",
"metadata": {},
"source": [
"# Project to take Audio Input to the Airlines ChatBot"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a07e7793-b8f5-44f4-aded-5562f633271a",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n",
"import base64\n",
"from io import BytesIO\n",
"from PIL import Image\n",
"from IPython.display import Audio, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e2315a3-f80c-4d3f-8073-f5b61d709564",
"metadata": {},
"outputs": [],
"source": [
"# Initialization\n",
"\n",
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "40da9de1-b350-49de-8acd-052f40ce5611",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n",
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n",
"system_message += \"Always be accurate. If you don't know the answer, say so.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5537635c-a60d-4983-8018-375c6a912e19",
"metadata": {},
"outputs": [],
"source": [
"# Let's start by making a useful function\n",
"\n",
"ticket_prices = {\"london\": \"$799\", \"paris\": \"$899\", \"tokyo\": \"$1400\", \"berlin\": \"$499\"}\n",
"\n",
"def get_ticket_price(destination_city):\n",
" print(f\"Tool get_ticket_price called for {destination_city}\")\n",
" city = destination_city.lower()\n",
" return ticket_prices.get(city, \"Unknown\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7132dd0-8788-4885-a415-d59664f68fd8",
"metadata": {},
"outputs": [],
"source": [
"# There's a particular dictionary structure that's required to describe our function:\n",
"\n",
"price_function = {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket to the destination city. Call this whenever you need to know the ticket price, for example when a customer asks 'How much is a ticket to this city'\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7703ca0c-5da4-4641-bcb1-7727d1b2f2bf",
"metadata": {},
"outputs": [],
"source": [
"# And this is included in a list of tools:\n",
"\n",
"tools = [{\"type\": \"function\", \"function\": price_function}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "29ce724b-d998-4c3f-bc40-6b8576c0fd34",
"metadata": {},
"outputs": [],
"source": [
"# We have to write that function handle_tool_call:\n",
"\n",
"def handle_tool_call(message):\n",
" tool_call = message.tool_calls[0]\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" city = arguments.get('destination_city')\n",
" price = get_ticket_price(city)\n",
" response = {\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps({\"destination_city\": city,\"price\": price}),\n",
" \"tool_call_id\": tool_call.id\n",
" }\n",
" return response, city"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "931d0565-b01d-4aa8-bd18-72bafff8fb3b",
"metadata": {},
"outputs": [],
"source": [
"def artist(city):\n",
" image_response = openai.images.generate(\n",
" model=\"dall-e-3\",\n",
" prompt=f\"An image representing a vacation in {city}, showing tourist spots and everything unique about {city}, in a vibrant pop-art style\",\n",
" size=\"1024x1024\",\n",
" n=1,\n",
" response_format=\"b64_json\",\n",
" )\n",
" image_base64 = image_response.data[0].b64_json\n",
" image_data = base64.b64decode(image_base64)\n",
" return Image.open(BytesIO(image_data))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fa165f7f-9796-4513-b923-2fa0b0b9ddd8",
"metadata": {},
"outputs": [],
"source": [
"import base64\n",
"from io import BytesIO\n",
"from PIL import Image\n",
"from IPython.display import Audio, display\n",
"\n",
"def talker(message):\n",
" response = openai.audio.speech.create(\n",
" model=\"tts-1\",\n",
" voice=\"onyx\",\n",
" input=message)\n",
"\n",
" audio_stream = BytesIO(response.content)\n",
" output_filename = \"output_audio.mp3\"\n",
" with open(output_filename, \"wb\") as f:\n",
" f.write(audio_stream.read())\n",
"\n",
" # Play the generated audio\n",
" display(Audio(output_filename, autoplay=True))\n",
"\n",
"talker(\"Well, hi there\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b512d4ff-0f7b-4148-b161-4ee0ebf14776",
"metadata": {},
"outputs": [],
"source": [
"def transcribe_audio(audio_file):\n",
" with open(audio_file, \"rb\") as f:\n",
" transcript = openai.audio.transcriptions.create(\n",
" model=\"whisper-1\",\n",
" file=f\n",
" )\n",
" return transcript.text"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3852570-fb26-4507-a001-f50fd94b7655",
"metadata": {},
"outputs": [],
"source": [
"# Translate between languages using GPT\n",
"def translate(text, source_lang, target_lang):\n",
" translation_prompt = (\n",
" f\"Translate the following text from {source_lang} to {target_lang}:\\n\\n{text}\"\n",
" )\n",
" response = openai.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages=[{\"role\": \"user\", \"content\": translation_prompt}]\n",
" )\n",
" return response.choices[0].message.content.strip()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d75abc2-870e-48af-a8fe-8dd463418b3d",
"metadata": {},
"outputs": [],
"source": [
"# Chatbot logic: handle both text and audio input\n",
"def chatbot_dual(history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
" image = None\n",
" \n",
" if response.choices[0].finish_reason==\"tool_calls\":\n",
" message = response.choices[0].message\n",
" response, city = handle_tool_call(message)\n",
" messages.append(message)\n",
" messages.append(response)\n",
" image = artist(city)\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
" \n",
" reply = response.choices[0].message.content\n",
" history += [{\"role\":\"assistant\", \"content\":reply}]\n",
"\n",
" # Comment out or delete the next line if you'd rather skip Audio for now..\n",
" # audio_response = talker(reply)\n",
" talker(reply)\n",
" return history, image# Chatbot logic here — replace with real logic"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "512fec09-c2f7-4847-817b-bc20f8b30319",
"metadata": {},
"outputs": [],
"source": [
"# More involved Gradio code as we're not using the preset Chat interface!\n",
"# Passing in inbrowser=True in the last line will cause a Gradio window to pop up immediately.\n",
"\n",
"with gr.Blocks() as ui:\n",
" with gr.Row():\n",
" chatbot = gr.Chatbot(height=500, type=\"messages\")\n",
" image_output = gr.Image(height=500)\n",
"\n",
" with gr.Row():\n",
" text_input = gr.Textbox(label=\"Chat with our AI Assistant:\")\n",
" audio_input = gr.Audio(sources=\"microphone\", type=\"filepath\", label=\"Or speak to the assistant\")\n",
"\n",
" with gr.Row():\n",
" # voice_output = gr.Audio(label=\"Bot Voice Reply\", autoplay=True)\n",
" clear = gr.Button(\"Clear\")\n",
"\n",
" def do_entry(message, audio, history):\n",
" if message:\n",
" history += [{\"role\":\"user\", \"content\":message}]\n",
" if audio:\n",
" history += [{\"role\":\"user\", \"content\":transcribe_audio(audio)}]\n",
" return \"\", None, history\n",
"\n",
" text_input.submit(do_entry, inputs=[text_input, audio_input, chatbot], outputs=[text_input, audio_input, chatbot]).then(chatbot_dual, inputs=chatbot, outputs=[chatbot, image_output]\n",
" )\n",
"\n",
" audio_input.change(do_entry, inputs=[text_input, audio_input, chatbot], outputs=[text_input, audio_input, chatbot]).then(chatbot_dual, inputs=chatbot, outputs=[chatbot, image_output]\n",
" )\n",
"\n",
" clear.click(lambda: None, inputs=None, outputs=chatbot, queue=False)\n",
"\n",
"ui.launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e1294e2-caf0-4f0f-b09e-b0d52c8ca6ec",
"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"
}
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"nbformat": 4,
"nbformat_minor": 5
}
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