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| import json | |
| import os | |
| import gradio as gr | |
| import requests | |
| from lagent.schema import AgentStatusCode | |
| os.system("python -m mindsearch.app --lang cn --model_format internlm_silicon &") | |
| PLANNER_HISTORY = [] | |
| SEARCHER_HISTORY = [] | |
| def rst_mem(history_planner: list, history_searcher: list): | |
| ''' | |
| Reset the chatbot memory. | |
| ''' | |
| history_planner = [] | |
| history_searcher = [] | |
| if PLANNER_HISTORY: | |
| PLANNER_HISTORY.clear() | |
| return history_planner, history_searcher | |
| def format_response(gr_history, agent_return): | |
| if agent_return['state'] in [AgentStatusCode.STREAM_ING, AgentStatusCode.ANSWER_ING]: | |
| gr_history[-1][1] = agent_return['response'] | |
| elif agent_return['state'] == AgentStatusCode.PLUGIN_START: | |
| thought = gr_history[-1][1].split('```')[0] | |
| if agent_return['response'].startswith('```'): | |
| gr_history[-1][1] = thought + '\n' + agent_return['response'] | |
| elif agent_return['state'] == AgentStatusCode.PLUGIN_END: | |
| thought = gr_history[-1][1].split('```')[0] | |
| if isinstance(agent_return['response'], dict): | |
| gr_history[-1][1] = thought + '\n' + f'```json\n{json.dumps(agent_return["response"], ensure_ascii=False, indent=4)}\n```' | |
| elif agent_return['state'] == AgentStatusCode.PLUGIN_RETURN: | |
| assert agent_return['inner_steps'][-1]['role'] == 'environment' | |
| item = agent_return['inner_steps'][-1] | |
| gr_history.append([None, f"```json\n{json.dumps(item['content'], ensure_ascii=False, indent=4)}\n```"]) | |
| gr_history.append([None, '']) | |
| return | |
| def predict(history_planner, history_searcher): | |
| def streaming(raw_response): | |
| for chunk in raw_response.iter_lines(chunk_size=8192, decode_unicode=False, delimiter=b'\n'): | |
| if chunk: | |
| decoded = chunk.decode('utf-8') | |
| if decoded == '\r': | |
| continue | |
| if decoded[:6] == 'data: ': | |
| decoded = decoded[6:] | |
| elif decoded.startswith(': ping - '): | |
| continue | |
| response = json.loads(decoded) | |
| yield (response['response'], response['current_node']) | |
| global PLANNER_HISTORY | |
| PLANNER_HISTORY.append(dict(role='user', content=history_planner[-1][0])) | |
| new_search_turn = True | |
| url = 'http://localhost:8002/solve' | |
| headers = {'Content-Type': 'application/json'} | |
| data = {'inputs': PLANNER_HISTORY} | |
| raw_response = requests.post(url, headers=headers, data=json.dumps(data), timeout=20, stream=True) | |
| for resp in streaming(raw_response): | |
| agent_return, node_name = resp | |
| if node_name: | |
| if node_name in ['root', 'response']: | |
| continue | |
| agent_return = agent_return['nodes'][node_name]['detail'] | |
| if new_search_turn: | |
| history_searcher.append([agent_return['content'], '']) | |
| new_search_turn = False | |
| format_response(history_searcher, agent_return) | |
| if agent_return['state'] == AgentStatusCode.END: | |
| new_search_turn = True | |
| yield history_planner, history_searcher | |
| else: | |
| new_search_turn = True | |
| format_response(history_planner, agent_return) | |
| if agent_return['state'] == AgentStatusCode.END: | |
| PLANNER_HISTORY = agent_return['inner_steps'] | |
| yield history_planner, history_searcher | |
| return history_planner, history_searcher | |
| with gr.Blocks(css=".button-primary { background-color: #4CAF50; color: white; border-radius: 8px; border: none; padding: 10px 20px; font-size: 16px; cursor: pointer; transition: background-color 0.3s ease; } .button-primary:hover { background-color: #45a049; } .button-secondary { background-color: #f44336; color: white; border-radius: 8px; border: none; padding: 10px 20px; font-size: 16px; cursor: pointer; transition: background-color 0.3s ease; } .button-secondary:hover { background-color: #e53935; }") as demo: | |
| gr.HTML("""<h1 align="center" style="font-family: 'Arial', sans-serif; color: #4A90E2;">MindSearch Gradio Demo</h1>""") | |
| gr.HTML("""<p style="text-align: center; font-family: Arial, sans-serif; color: #333; max-width: 800px; margin: 0 auto;">MindSearch is an open-source AI Search Engine Framework with Perplexity.ai Pro performance. You can deploy your own Perplexity.ai-style search engine using either closed-source LLMs (GPT, Claude) or open-source LLMs (InternLM2.5-7b-chat).</p>""") | |
| gr.HTML(""" | |
| <div style="text-align: center; font-size: 16px; margin-bottom: 20px;"> | |
| <a href="https://github.com/InternLM/MindSearch" style="margin-right: 15px; text-decoration: none; color: #4A90E2; font-weight: bold;">🔗 GitHub</a> | |
| <a href="https://arxiv.org/abs/2407.20183" style="margin-right: 15px; text-decoration: none; color: #4A90E2; font-weight: bold;">📄 Arxiv</a> | |
| <a href="https://huggingface.co/papers/2407.20183" style="margin-right: 15px; text-decoration: none; color: #4A90E2; font-weight: bold;">📚 Hugging Face Papers</a> | |
| <a href="https://huggingface.co/spaces/internlm/MindSearch" style="text-decoration: none; color: #4A90E2; font-weight: bold;">🤗 Hugging Face Demo</a> | |
| </div> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=10): | |
| with gr.Row(): | |
| with gr.Column(): | |
| planner = gr.Chatbot(label='Planner', | |
| height=700, | |
| show_label=True, | |
| show_copy_button=True, | |
| bubble_full_width=False, | |
| render_markdown=True) | |
| with gr.Column(): | |
| searcher = gr.Chatbot(label='Searcher', | |
| height=700, | |
| show_label=True, | |
| show_copy_button=True, | |
| bubble_full_width=False, | |
| render_markdown=True) | |
| with gr.Row(): | |
| user_input = gr.Textbox(show_label=False, | |
| placeholder='帮我搜索一下 InternLM 开源体系', | |
| lines=5, | |
| container=False, | |
| css="border-radius: 8px; border: 1px solid #ddd; padding: 10px;") | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| submitBtn = gr.Button('Submit', css="button-primary") | |
| with gr.Column(scale=1, min_width=20): | |
| emptyBtn = gr.Button('Clear History', css="button-secondary") | |
| def user(query, history): | |
| return '', history + [[query, '']] | |
| submitBtn.click(user, [user_input, planner], [user_input, planner], | |
| queue=False).then(predict, [planner, searcher], | |
| [planner, searcher]) | |
| emptyBtn.click(rst_mem, [planner, searcher], [planner, searcher], | |
| queue=False) | |
| demo.queue() | |
| demo.launch(server_name='0.0.0.0', | |
| server_port=7860, | |
| inbrowser=True, | |
| share=True) | |