| import os |
| import gradio as gr |
| from huggingface_hub import InferenceClient |
|
|
| |
| client = InferenceClient( |
| model="deepseek-ai/DeepSeek-R1-0528", |
| token=os.environ["HF_TOKEN"] |
| ) |
|
|
| |
| def generate_software_spec(name, description, architecture, components, deployment, platform, extra): |
| prompt = f""" |
| You are a software architect assistant. Based on the following input, generate: |
| 1. A **Sequence Diagram** in Mermaid syntax |
| 2. A **Business Process Flow** in Mermaid syntax |
| 3. Code Snippets for each component based on the selected tech stack. |
| |
| App Name: {name} |
| Description: {description} |
| Architecture: {architecture} |
| Deployment: {deployment} |
| Platform: {platform} |
| Components & Tech Stack: |
| {components} |
| Extra requirements: {extra or "None"} |
| |
| Return the result in this format: |
| |
| ### Sequence Diagram (Mermaid) |
| ```mermaid |
| <sequence_diagram_here> |
| ``` |
| |
| ### Business Process Flow (Mermaid) |
| ```mermaid |
| <flowchart_here> |
| ``` |
| |
| ### Code Snippets |
| #### Component: <Component 1> |
| ```<language> |
| <code_here> |
| ``` |
| |
| #### Component: <Component 2> |
| ```<language> |
| <code_here> |
| ``` |
| """ |
| |
| print(prompt) |
| response = client.chat_completion( |
| messages=[ |
| {"role": "system", "content": "You are a helpful software architecture assistant."}, |
| {"role": "user", "content": prompt} |
| ] |
| ) |
| print(response) |
| return response.choices[0].message.content if response and response.choices else "❌ Error: No response received." |
|
|
| |
| with gr.Blocks() as demo: |
| gr.Markdown("## 🧠 AI Software Architecture Assistant") |
|
|
| with gr.Row(): |
| with gr.Column(): |
| name = gr.Textbox(label="App Name", placeholder="MyApp") |
| description = gr.Textbox(label="Short Description", lines=2, placeholder="A system for ...") |
| architecture = gr.Radio(["Monolithic", "Backend-Frontend"], label="Architecture Style", value="Backend-Frontend") |
| deployment = gr.Radio(["Serverless", "VM"], label="Deployment Style", value="Serverless") |
| platform = gr.Radio(["Web App", "Mobile App"], label="Target Platform", value="Web App") |
| components = gr.Textbox(label="Components & Tech Stack", lines=4, placeholder="Backend: Python + Flask, Frontend: React") |
| extra = gr.Textbox(label="Extra Requirements (optional)", lines=2) |
| submit = gr.Button("Generate Design") |
| status = gr.Markdown(visible=False) |
|
|
| with gr.Column(): |
| output = gr.HTML(label="Output") |
|
|
| def wrapper(name, description, architecture, components, deployment, platform, extra): |
| result = generate_software_spec(name, description, architecture, components, deployment, platform, extra) |
|
|
| |
| def render_mermaid_blocks(text): |
| import re |
| blocks = re.findall(r"```mermaid\n(.*?)```", text, re.DOTALL) |
| html_blocks = "".join([f"<div class='mermaid'>{block}</div>" for block in blocks]) |
| return html_blocks or text |
|
|
| mermaid_html = render_mermaid_blocks(result) |
| final_html = f""" |
| <link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/mermaid@10/dist/mermaid.min.css\"> |
| <script src=\"https://cdn.jsdelivr.net/npm/mermaid@10/dist/mermaid.min.js\"></script> |
| <script>mermaid.initialize({{startOnLoad:true}});</script> |
| {mermaid_html} |
| <pre style='white-space: pre-wrap;'>{result}</pre> |
| """ |
| return final_html, gr.update(visible=False) |
|
|
| def show_spinner(): |
| return gr.update(visible=True, value="<center>⏳ Generating... Please wait.</center>") |
|
|
| submit.click(fn=show_spinner, inputs=[], outputs=[status], queue=False) |
| submit.click( |
| fn=wrapper, |
| inputs=[name, description, architecture, components, deployment, platform, extra], |
| outputs=[output, status], |
| preprocess=False, |
| postprocess=False, |
| show_progress=True |
| ) |
|
|
| demo.launch() |
