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| import gradio as gr | |
| from config import check_openai_api_key | |
| from agent.research_agent import ResearchAgent | |
| from agent.toolkits import english_polishing | |
| from statics.style import * | |
| theme = gr.themes.Soft( | |
| primary_hue=gr.themes.Color(c100="#e0e7ff", c200="#c7d2fe", c300="#a5b4fc", c400="#818cf8", c50="#eef2ff", c500="#6366f1", c600="#5e5aaa", c700="#4338ca", c800="#3730a3", c900="#312e81", c950="#2b2c5e"), | |
| font_mono=[gr.themes.GoogleFont('Fira Code'), 'ui-monospace', 'Consolas', 'monospace'] | |
| ) | |
| check_openai_api_key() | |
| def run_agent(task, agent, report_type): | |
| assistant = ResearchAgent(task, agent) | |
| yield from assistant.write_report(report_type) | |
| with gr.Blocks(theme=gr.themes.Base(), | |
| title="AI Research Assistant", | |
| css=css) as demo: | |
| gr.HTML(top_bar) | |
| with gr.Tab(label="Report"): | |
| with gr.Column(): | |
| gr.HTML(research_report_html) | |
| research_report = gr.Markdown(value=" **Research report will appear here...**", | |
| elem_classes="output") | |
| with gr.Row(): | |
| agent_type = gr.Dropdown(label="# Agent Type", | |
| value="Default Agent", | |
| interactive=True, | |
| allow_custom_value=False, | |
| choices=["Default Agent", | |
| "Business Analyst Agent", | |
| "Finance Agent", | |
| "Travel Agent", | |
| "Academic Research Agent", | |
| "Computer Security Analyst Agent", | |
| "Clinical Medicine Agent", | |
| "Basic Medicine Agent", | |
| "Social Science Research Agent"]) | |
| report_type = gr.Dropdown(label="# Report Type", | |
| value="Research Report", | |
| interactive=True, | |
| allow_custom_value=False, | |
| choices=["Research Report", | |
| "Resource Report", | |
| "Outline Report"]) | |
| input_box = gr.Textbox(label="# What would you like to research next?", placeholder="Enter your question here") | |
| submit_btn = gr.Button("Generate Report") | |
| submit_btn.click(run_agent, inputs=[input_box, agent_type, report_type], | |
| outputs=research_report) | |
| gr.Examples(["Should I invest in the Large Language Model industry in 2023?", | |
| "Is it advisable to make investments in the electric car industry during the year 2023?", | |
| "What constitutes the optimal approach for investing in the Bitcoin industry during the year 2023?", | |
| "What are the most recent advancements in the domain of superconductors as of 2023?"], | |
| inputs=input_box) | |
| with gr.Tab("English Polishing"): | |
| gr.HTML(english_polishing_html) | |
| polished_result = gr.Markdown(" **Polished result will appear here...**", elem_classes="output") | |
| sentences = gr.Textbox(label="# What would you like to polish?", placeholder="Enter your sentence here") | |
| with gr.Row(): | |
| polish_btn = gr.Button("Polish") | |
| save_btn = gr.Button("Save") | |
| polish_btn.click(english_polishing, inputs=[sentences], outputs=polished_result) | |
| def save_result(history, origin, result): | |
| history += f"\n**Origin** : {origin}\n\n**Polished Result** : {result}" | |
| return history | |
| gr.HTML(history_result_html) | |
| history_result = gr.Markdown(" **History result will appear here...**") | |
| save_btn.click(save_result, inputs=[history_result, sentences, polished_result], outputs=history_result) | |
| with gr.Tab("Literature Review"): | |
| pass | |
| demo.queue().launch() |