import json import gradio as gr from flow import full_flow from utils import use_cost_tracker, get_costs, compute_hash with open('schemas.json', 'r') as f: SCHEMAS = json.load(f) def parse_raw_messages(messages_raw): messages_clean = [] messages_agora = [] for message in messages_raw: role = message['role'] message_without_role = dict(message) del message_without_role['role'] messages_agora.append({ 'role': role, 'content': '```\n' + json.dumps(message_without_role, indent=2) + '\n```' }) if message.get('status') == 'error': messages_clean.append({ 'role': role, 'content': f"Error: {message['message']}" }) else: messages_clean.append({ 'role': role, 'content': message['body'] }) return messages_clean, messages_agora def main(): with gr.Blocks() as demo: gr.Markdown("# Agora Demo") gr.Markdown("We will create a new Agora channel and offer it to Alice as a tool.") chosen_task = gr.Dropdown(choices=[ (v['display_name'], k) for k, v in SCHEMAS.items() ], label="Schema", value="weather_forecast") @gr.render(inputs=[chosen_task]) def render2(chosen_task): gr.Markdown('**Description**: ' + SCHEMAS[chosen_task]["description"]) custom_task = gr.Checkbox(label="Override Demo Parameters") STATE_TRACKER = {} @gr.render(inputs=[chosen_task, custom_task]) def render(chosen_task, custom_task): if STATE_TRACKER.get('chosen_task') != chosen_task: STATE_TRACKER['chosen_task'] = chosen_task for k, v in SCHEMAS[chosen_task]['schema'].items(): if isinstance(v, str): STATE_TRACKER[k] = v else: STATE_TRACKER[k] = json.dumps(v, indent=2) if custom_task: gr.Markdown('#### Custom Demo Parameters') gr.Markdown('You can override the default parameters for the demo. Note: recommended for advanced users only.') gr.Text(label="Description", value=STATE_TRACKER["description"], interactive=True).change(lambda x: STATE_TRACKER.update({'description': x})) gr.TextArea(label="Input Schema", value=STATE_TRACKER["input"], interactive=True).change(lambda x: STATE_TRACKER.update({'input': x})) gr.TextArea(label="Output Schema", value=STATE_TRACKER["output"], interactive=True).change(lambda x: STATE_TRACKER.update({'output': x})) gr.TextArea(label="Tools", value=STATE_TRACKER["tools"], interactive=True).change(lambda x: STATE_TRACKER.update({'tools': x})) gr.TextArea(label="Examples", value=STATE_TRACKER["examples"], interactive=True).change(lambda x: STATE_TRACKER.update({'examples': x})) model_options = [ ('GPT 4o (Camel AI)', 'gpt-4o'), ('GPT 4o-mini (Camel AI)', 'gpt-4o-mini'), ('Claude 3 Sonnet (LangChain)', 'claude-3-5-sonnet-latest'), ('Claude 3 Haiku (LangChain)', 'claude-3-5-haiku-latest'), ('Gemini 1.5 Pro (Google GenAI)', 'gemini-1.5-pro'), ('Llama3 405B (Sambanova + LangChain)', 'llama3-405b') ] fallback_image = '' images = { 'gpt-4o': 'https://uxwing.com/wp-content/themes/uxwing/download/brands-and-social-media/chatgpt-icon.png', 'gpt-4o-mini': 'https://uxwing.com/wp-content/themes/uxwing/download/brands-and-social-media/chatgpt-icon.png', 'claude-3-5-sonnet-latest': 'https://play-lh.googleusercontent.com/4S1nfdKsH_1tJodkHrBHimqlCTE6qx6z22zpMyPaMc_Rlr1EdSFDI1I6UEVMnokG5zI', 'claude-3-5-haiku-latest': 'https://play-lh.googleusercontent.com/4S1nfdKsH_1tJodkHrBHimqlCTE6qx6z22zpMyPaMc_Rlr1EdSFDI1I6UEVMnokG5zI', 'gemini-1.5-pro': 'https://uxwing.com/wp-content/themes/uxwing/download/brands-and-social-media/google-gemini-icon.png', 'llama3-405b': 'https://www.designstub.com/png-resources/wp-content/uploads/2023/03/meta-icon-social-media-flat-graphic-vector-3-novem.png' } with gr.Row(equal_height=True): with gr.Column(scale=1): alice_model_dd = gr.Dropdown(label="Alice Model", choices=model_options, value="gpt-4o") with gr.Column(scale=1): bob_model_dd = gr.Dropdown(label="Bob Model", choices=model_options, value="gpt-4o") @gr.render(inputs=[alice_model_dd, bob_model_dd]) def render_with_images(alice_model, bob_model): button = gr.Button('Start', elem_id='start_button') gr.Markdown('## Natural Language') avatar_images = [images.get(alice_model, fallback_image), images.get(bob_model, fallback_image)] chatbot_nl = gr.Chatbot(type="messages", avatar_images=avatar_images) with gr.Accordion(label="Raw Messages", open=False): chatbot_nl_raw = gr.Chatbot(type="messages", avatar_images=avatar_images) gr.Markdown('## Negotiation') chatbot_negotiation = gr.Chatbot(type="messages", avatar_images=avatar_images) gr.Markdown('## Protocol') protocol_hash_result = gr.Text(interactive=False, label="Protocol Hash") protocol_result = gr.TextArea(interactive=False, label="Protocol") gr.Markdown('## Implementation') with gr.Row(): with gr.Column(scale=1): alice_implementation = gr.TextArea(interactive=False, label="Alice Implementation") with gr.Column(scale=1): bob_implementation = gr.TextArea(interactive=False, label="Bob Implementation") gr.Markdown('## Structured Communication') structured_communication = gr.Chatbot(type="messages", avatar_images=avatar_images) with gr.Accordion(label="Raw Messages", open=False): structured_communication_raw = gr.Chatbot(type="messages", avatar_images=avatar_images) gr.Markdown('## Cost') cost_info = gr.State(value=None) #cost_info = gr.TextArea(interactive=False, label="Cost") query_slider = gr.Slider(label="Number of queries", minimum=1, maximum=10_000, step=1, value=50, interactive=True) cost_display = gr.Markdown('') def render_info(query_count, cost_info): if not cost_info: return '' natural_cost = cost_info['conversation'] * query_count agora_cost = cost_info['negotiation'] + cost_info['programming'] cost_message = f'Cost of one natural language conversation: {cost_info["conversation"]:.4f} USD\n\n' cost_message += f'Cost of negotiating the protocol: {cost_info["negotiation"]:.4f} USD\n\n' cost_message += f'Cost of implementing the protocol: {cost_info["programming"]:.4f} USD\n\n' cost_message += f'Cost of {query_count} queries with natural language: {natural_cost:.4f} USD\n\n' cost_message += f'Cost of {query_count} queries with Agora: {agora_cost:.4f} USD\n\n' if natural_cost < agora_cost: factor = agora_cost / natural_cost cost_message += f'Natural language is {factor:.2f}x cheaper than Agora.' else: factor = natural_cost / agora_cost cost_message += f'Agora is {factor:.2f}x cheaper than natural language.' return cost_message cost_info.change(render_info, [query_slider, cost_info], [cost_display]) query_slider.change(render_info, [query_slider, cost_info], [cost_display]) def respond(chosen_task, custom_task, alice_model, bob_model, query_count): with use_cost_tracker(): yield gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False), \ None, None, None, None, None, None, None, None, None, None, None if custom_task: schema = dict(STATE_TRACKER) for k, v in schema.items(): if isinstance(v, str): try: schema[k] = json.loads(v) except: pass else: schema = SCHEMAS[chosen_task]["schema"] for nl_messages_raw, negotiation_messages, structured_messages_raw, protocol, alice_implementation, bob_implementation in full_flow(schema, alice_model, bob_model): nl_messages_clean, nl_messages_agora = parse_raw_messages(nl_messages_raw) structured_messages_clean, structured_messages_agora = parse_raw_messages(structured_messages_raw) protocol_hash = compute_hash(protocol) if protocol else None yield gr.update(), gr.update(), gr.update(), None, None, nl_messages_clean, nl_messages_agora, negotiation_messages, structured_messages_clean, structured_messages_agora, protocol, protocol_hash, alice_implementation, bob_implementation #yield from full_flow(schema, alice_model, bob_model) cost_data = get_costs() cost_data_formatted = render_info(query_count, cost_data) yield gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), cost_data, cost_data_formatted, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update() button.click(respond, [chosen_task, custom_task, alice_model_dd, bob_model_dd], [button, alice_model_dd, bob_model_dd, chatbot_nl, chatbot_nl_raw, chatbot_negotiation, structured_communication, structured_communication_raw, protocol_result, alice_implementation, bob_implementation]) demo.launch() if __name__ == '__main__': main()