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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7864\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7864/\" 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": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"\n",
"# Define some pre-written templates\n",
"templates = {\n",
" \"Friendly Chatbot\": \"You are a helpful, friendly chatbot that engages in light-hearted conversations.\",\n",
" \"Technical Assistant\": \"You are a technical assistant specialized in answering questions related to Python programming.\",\n",
" \"Nutrition Advisor\": \"You provide evidence-based advice on nutrition and healthy eating habits.\",\n",
"}\n",
"\n",
"# Chatbot logic: Takes system instructions and user query, returns a response\n",
"def chatbot_response(system_instructions, user_query):\n",
" if \"friendly\" in system_instructions.lower():\n",
" return f\"Friendly Chatbot says: Hi there! 😊 How can I assist you today?\"\n",
" elif \"technical\" in system_instructions.lower():\n",
" return f\"Technical Assistant says: Sure! Here's some information on Python: {user_query}\"\n",
" elif \"nutrition\" in system_instructions.lower():\n",
" return f\"Nutrition Advisor says: Here's some advice about healthy eating: {user_query}\"\n",
" else:\n",
" return f\"Custom Chatbot says: {user_query}\"\n",
"\n",
"# Function to update the interface when a selection is made from the dropdown\n",
"def update_interface(template_name, custom_instructions):\n",
" if template_name == \"Custom Instructions\":\n",
" return gr.update(visible=True), gr.update(visible(False))\n",
" else:\n",
" template_content = templates.get(template_name, \"\")\n",
" return gr.update(visible=False), gr.update(visible=True, value=template_content)\n",
"\n",
"# Chatbot conversation function\n",
"def chatbot_conversation(system_instructions, chat_history, user_query):\n",
" response = chatbot_response(system_instructions, user_query)\n",
" chat_history.append((user_query, response))\n",
" return chat_history, \"\"\n",
"\n",
"# Build the Gradio interface\n",
"with gr.Blocks() as demo:\n",
" \n",
" # Add the title and description\n",
" gr.Markdown(\"# **SRF Innovation Labs - AI Chatbot Use Case Explorer**\")\n",
" gr.Markdown(\"\"\"\n",
" Welcome to the SRF Innovation Labs AI Chatbot Use Case Explorer! \n",
" This tool allows you to experiment with different system prompts, \n",
" giving you control over how the chatbot behaves. You can either use pre-defined templates or write your own custom instructions.\n",
" \n",
" Additionally, the chatbot has access to a vector database where it can look up and retrieve learnings for various queries. \n",
" This makes it an excellent platform for exploring potential AI use cases in real-time.\n",
" \"\"\")\n",
"\n",
" # Section to select system instructions from dropdown\n",
" gr.Markdown(\"## **Chatbot Setup**\")\n",
"\n",
" # Dropdown for selecting a pre-written template or custom instructions\n",
" template_name = gr.Dropdown(choices=[\"Custom Instructions\"] + list(templates.keys()), label=\"Choose Instructions\", value=\"Friendly Chatbot\")\n",
" \n",
" # Textbox for custom chatbot instructions (only shown when \"Custom Instructions\" is selected)\n",
" custom_instructions = gr.Textbox(label=\"Custom Instructions\", visible=False, placeholder=\"Write your own instructions here...\")\n",
" \n",
" # Output field to display the selected pre-written template (not shown when Custom Instructions is selected)\n",
" template_display = gr.Textbox(label=\"Template Content\", interactive=False, visible=True)\n",
" \n",
" # Section for chat interface\n",
" gr.Markdown(\"## **Chatbot Interaction**\")\n",
"\n",
" # Chatbot interface with chat history\n",
" chatbot = gr.Chatbot(label=\"Chatbot Conversation\")\n",
" user_query = gr.Textbox(label=\"Your Query\", placeholder=\"Ask a question or say something to the chatbot...\")\n",
"\n",
" # Button to submit the query\n",
" submit_button = gr.Button(\"Send\")\n",
"\n",
" # Update logic to control the display based on the dropdown selection\n",
" template_name.change(fn=update_interface, \n",
" inputs=[template_name, custom_instructions], \n",
" outputs=[custom_instructions, template_display])\n",
"\n",
" # Chatbot interaction logic\n",
" submit_button.click(fn=chatbot_conversation, \n",
" inputs=[custom_instructions if template_name == \"Custom Instructions\" else template_display, chatbot, user_query], \n",
" outputs=[chatbot, user_query])\n",
"\n",
"# Launch the app\n",
"demo.launch()\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7865\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7865/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"\n",
"# Define some pre-written templates for Tab 1\n",
"templates = {\n",
" \"Friendly Chatbot\": \"You are a helpful, friendly chatbot that engages in light-hearted conversations.\",\n",
" \"Technical Assistant\": \"You are a technical assistant specialized in answering questions related to Python programming.\",\n",
" \"Nutrition Advisor\": \"You provide evidence-based advice on nutrition and healthy eating habits.\",\n",
"}\n",
"\n",
"# Define some agentic workflows for Tab 2\n",
"agentic_workflows = {\n",
" \"Blog Post Generator\": \"This agent is designed to help generate a blog post based on user input.\",\n",
" \"Document Summarizer\": \"This agent summarizes long documents by extracting key points.\",\n",
" \"Task Manager\": \"This agent helps you organize tasks and provides step-by-step guidance.\"\n",
"}\n",
"\n",
"# Chatbot logic for custom instructions or pre-written templates\n",
"def chatbot_response(system_instructions, user_query):\n",
" if \"friendly\" in system_instructions.lower():\n",
" return f\"Friendly Chatbot says: Hi there! 😊 How can I assist you today?\"\n",
" elif \"technical\" in system_instructions.lower():\n",
" return f\"Technical Assistant says: Sure! Here's some information on Python: {user_query}\"\n",
" elif \"nutrition\" in system_instructions.lower():\n",
" return f\"Nutrition Advisor says: Here's some advice about healthy eating: {user_query}\"\n",
" else:\n",
" return f\"Custom Chatbot says: {user_query}\"\n",
"\n",
"# Chatbot conversation function\n",
"def chatbot_conversation(system_instructions, chat_history, user_query):\n",
" response = chatbot_response(system_instructions, user_query)\n",
" chat_history.append((user_query, response))\n",
" return chat_history, \"\"\n",
"\n",
"# Chatbot conversation for predefined agentic workflows\n",
"def agentic_chatbot_conversation(workflow_instructions, chat_history, user_query):\n",
" response = f\"Agent Workflow ({workflow_instructions}) says: {user_query}\"\n",
" chat_history.append((user_query, response))\n",
" return chat_history, \"\"\n",
"\n",
"# Function to update the interface when a selection is made from the dropdown (Tab 1)\n",
"def update_interface(template_name, custom_instructions):\n",
" if template_name == \"Custom Instructions\":\n",
" return gr.update(visible=True), gr.update(visible=False)\n",
" else:\n",
" template_content = templates.get(template_name, \"\")\n",
" return gr.update(visible=False), gr.update(visible=True, value=template_content)\n",
"\n",
"# Build the Gradio interface with Tabs\n",
"with gr.Blocks() as demo:\n",
" \n",
" # Add Tabs\n",
" with gr.Tabs():\n",
" \n",
" # Tab 1: Custom Instructions or Pre-Written Templates\n",
" with gr.Tab(\"Custom Instructions Chatbot\"):\n",
" gr.Markdown(\"# **SRF Innovation Labs - AI Chatbot Use Case Explorer**\")\n",
" gr.Markdown(\"\"\"\n",
" This tool allows you to experiment with different system prompts, \n",
" giving you control over how the chatbot behaves. You can either use pre-defined templates or write your own custom instructions.\n",
" \"\"\")\n",
"\n",
" # Section to select system instructions from dropdown\n",
" gr.Markdown(\"## **Chatbot Setup**\")\n",
" template_name = gr.Dropdown(choices=[\"Custom Instructions\"] + list(templates.keys()), label=\"Choose Instructions\", value=\"Friendly Chatbot\")\n",
" custom_instructions = gr.Textbox(label=\"Custom Instructions\", visible=False, placeholder=\"Write your own instructions here...\")\n",
" template_display = gr.Textbox(label=\"Template Content\", interactive=False, visible=True)\n",
"\n",
" # Chatbot interface\n",
" gr.Markdown(\"## **Chatbot Interaction**\")\n",
" chatbot = gr.Chatbot(label=\"Chatbot Conversation\")\n",
" user_query = gr.Textbox(label=\"Your Query\", placeholder=\"Ask a question or say something to the chatbot...\")\n",
" submit_button = gr.Button(\"Send\")\n",
"\n",
" # Update logic for Tab 1\n",
" template_name.change(fn=update_interface, inputs=[template_name, custom_instructions], outputs=[custom_instructions, template_display])\n",
" submit_button.click(fn=chatbot_conversation, inputs=[custom_instructions if template_name == \"Custom Instructions\" else template_display, chatbot, user_query], outputs=[chatbot, user_query])\n",
"\n",
" # Tab 2: Predefined Agentic Workflows\n",
" with gr.Tab(\"Agentic Workflow Chatbots\"):\n",
" gr.Markdown(\"# **Agentic Workflow Explorer**\")\n",
" gr.Markdown(\"\"\"\n",
" This tab allows you to experiment with different agentic workflows that are predefined. \n",
" Each workflow executes a specific task, such as generating blog posts, summarizing documents, or managing tasks.\n",
" \"\"\")\n",
"\n",
" # Dropdown for selecting agentic workflows\n",
" workflow_name = gr.Dropdown(choices=list(agentic_workflows.keys()), label=\"Choose Agent Workflow\", value=\"Blog Post Generator\")\n",
" workflow_display = gr.Textbox(label=\"Workflow Description\", interactive=False, visible=True)\n",
" workflow_chatbot = gr.Chatbot(label=\"Agent Workflow Conversation\")\n",
" workflow_user_query = gr.Textbox(label=\"Your Query\", placeholder=\"Ask the agent to perform a task...\")\n",
" workflow_submit_button = gr.Button(\"Send\")\n",
"\n",
" # Chatbot interaction for agentic workflows\n",
" workflow_submit_button.click(fn=agentic_chatbot_conversation, inputs=[workflow_name, workflow_chatbot, workflow_user_query], outputs=[workflow_chatbot, workflow_user_query])\n",
"\n",
"# Launch the app\n",
"demo.launch()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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