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| import gradio as gr | |
| import os | |
| import asyncio | |
| import nest_asyncio | |
| from datetime import datetime | |
| from typing import Optional, Dict, Any | |
| from autogen_agentchat.agents import AssistantAgent, UserProxyAgent | |
| from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination | |
| from autogen_agentchat.teams import SelectorGroupChat | |
| from autogen_ext.models.openai import OpenAIChatCompletionClient | |
| from autogen_ext.agents.web_surfer import MultimodalWebSurfer | |
| # Enable nested event loops | |
| nest_asyncio.apply() | |
| class AIShoppingAnalyzer: | |
| def __init__(self, api_key: str): | |
| self.api_key = api_key | |
| os.environ["OPENAI_API_KEY"] = api_key | |
| self.model_client = OpenAIChatCompletionClient(model="gpt-4o") | |
| self.termination = MaxMessageTermination(max_messages=20) | TextMentionTermination("TERMINATE") | |
| def create_websurfer(self) -> MultimodalWebSurfer: | |
| """Initialize the web surfer agent for e-commerce research""" | |
| description = ( | |
| "E-commerce research specialist that:\n" | |
| "1. Searches multiple retailers for product options\n" | |
| "2. Compares prices and reviews\n" | |
| "3. Checks product specifications and availability\n" | |
| "4. Analyzes website structure and findability\n" | |
| "5. Detects and analyzes structured data (Schema.org, JSON-LD, Microdata)\n" | |
| "6. Evaluates product markup and rich snippets\n" | |
| "7. Checks for proper semantic HTML and data organization" | |
| ) | |
| return MultimodalWebSurfer( | |
| name="websurfer_agent", | |
| description=description, | |
| model_client=self.model_client, | |
| headless=True, | |
| browser_kwargs={ | |
| "args": [ | |
| "--disable-dev-shm-usage", | |
| "--no-sandbox", | |
| "--disable-setuid-sandbox" | |
| ] | |
| } | |
| ) | |
| def create_assistant(self) -> AssistantAgent: | |
| """Initialize the shopping assistant agent""" | |
| system_message = ( | |
| "You are an expert shopping assistant and e-commerce analyst. " | |
| "Analyze websites and provide reports in this format:\n\n" | |
| "📊 E-COMMERCE ANALYSIS REPORT\n" | |
| "============================\n" | |
| "Site: {url}\n" | |
| "Date: {date}\n\n" | |
| "🔍 FINDABILITY SCORE: [★★★★☆]\n" | |
| "-----------------------------\n" | |
| "• Category Organization\n" | |
| "• Navigation Structure\n" | |
| "• Filter Systems\n\n" | |
| "📝 INFORMATION QUALITY: [★★★★☆]\n" | |
| "------------------------------\n" | |
| "• Product Details\n" | |
| "• Image Quality\n" | |
| "• Technical Specs\n" | |
| "• Structured Data\n\n" | |
| "🔄 NAVIGATION & SEARCH: [★★★★☆]\n" | |
| "------------------------------\n" | |
| "• Search Features\n" | |
| "• User Experience\n" | |
| "• Mobile Design\n\n" | |
| "💰 PRICING TRANSPARENCY: [★★★★☆]\n" | |
| "------------------------------\n" | |
| "• Price Display\n" | |
| "• Special Offers\n" | |
| "• Comparison Tools\n\n" | |
| "📈 OVERALL ASSESSMENT\n" | |
| "-------------------\n" | |
| "[Summary]\n\n" | |
| "🔧 TECHNICAL INSIGHTS\n" | |
| "-------------------\n" | |
| "[Technical Details]" | |
| ) | |
| return AssistantAgent( | |
| name="assistant_agent", | |
| description="E-commerce shopping advisor and website analyzer", | |
| system_message=system_message, | |
| model_client=self.model_client | |
| ) | |
| def create_team(self, websurfer_agent: MultimodalWebSurfer, assistant_agent: AssistantAgent) -> SelectorGroupChat: | |
| """Set up the team of agents""" | |
| user_proxy = UserProxyAgent( | |
| name="user_proxy", | |
| description="An e-commerce site owner looking for AI shopping analysis" | |
| ) | |
| selector_prompt = ( | |
| "You are coordinating an e-commerce analysis system. Select the next role from these participants:\n" | |
| "- The websurfer_agent searches products and analyzes website structure\n" | |
| "- The assistant_agent evaluates findings and makes recommendations\n" | |
| "- The user_proxy provides input when needed\n\n" | |
| "Return only the role name." | |
| ) | |
| return SelectorGroupChat( | |
| participants=[websurfer_agent, assistant_agent, user_proxy], | |
| selector_prompt=selector_prompt, | |
| model_client=self.model_client, | |
| termination_condition=self.termination | |
| ) | |
| async def analyze_site(self, | |
| website_url: str, | |
| product_category: str, | |
| specific_product: Optional[str] = None) -> str: | |
| """Run the analysis with proper cleanup""" | |
| websurfer = None | |
| try: | |
| query = ( | |
| f"Analyze the e-commerce experience for {website_url} focusing on:\n" | |
| f"1. Product findability in the {product_category} category\n" | |
| "2. Product information quality\n" | |
| "3. Navigation and search functionality\n" | |
| "4. Price visibility and comparison features" | |
| ) | |
| if specific_product: | |
| query += f"\n5. Detailed analysis of this specific product: {specific_product}" | |
| websurfer = self.create_websurfer() | |
| assistant = self.create_assistant() | |
| team = self.create_team(websurfer, assistant) | |
| try: | |
| result = [] | |
| async for message in team.run_stream(task=query): | |
| if isinstance(message, str): | |
| result.append(message) | |
| else: | |
| result.append(str(message)) | |
| return "\n".join(result) | |
| except EOFError: | |
| return "Analysis completed with some limitations. Please try again if results are incomplete." | |
| except Exception as e: | |
| return f"Analysis error: {str(e)}" | |
| finally: | |
| if websurfer: | |
| try: | |
| await websurfer.close() | |
| except Exception as e: | |
| print(f"Cleanup error: {str(e)}") | |
| def create_gradio_interface() -> gr.Blocks: | |
| """Create the Gradio interface for the AI Shopping Analyzer""" | |
| css = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Open+Sans:wght@300;400;600;700&display=swap'); | |
| body { | |
| font-family: 'Open Sans', sans-serif !important; | |
| } | |
| .dashboard-container { | |
| border: 1px solid #e0e5ff; | |
| border-radius: 8px; | |
| background-color: #ffffff; | |
| } | |
| .token-header { | |
| font-size: 1.25rem; | |
| font-weight: 600; | |
| margin-top: 1rem; | |
| margin-bottom: 0.5rem; | |
| } | |
| .feature-button { | |
| display: inline-block; | |
| margin: 0.25rem; | |
| padding: 0.5rem 1rem; | |
| background-color: #f3f4f6; | |
| border: 1px solid #e5e7eb; | |
| border-radius: 0.375rem; | |
| font-size: 0.875rem; | |
| } | |
| .feature-button:hover { | |
| background-color: #e5e7eb; | |
| } | |
| .gr-form { | |
| background: transparent !important; | |
| border: none !important; | |
| box-shadow: none !important; | |
| } | |
| .gr-input, .gr-textarea { | |
| border: 1px solid #e5e7eb !important; | |
| border-radius: 6px !important; | |
| padding: 8px 12px !important; | |
| font-size: 14px !important; | |
| transition: all 0.2s !important; | |
| } | |
| .gr-input:focus, .gr-textarea:focus { | |
| border-color: #4c4ce3 !important; | |
| outline: none !important; | |
| box-shadow: 0 0 0 2px rgba(76, 76, 227, 0.2) !important; | |
| } | |
| .gr-button { | |
| background-color: #4c4ce3 !important; | |
| color: white !important; | |
| border-radius: 6px !important; | |
| padding: 8px 16px !important; | |
| font-size: 14px !important; | |
| font-weight: 600 !important; | |
| transition: all 0.2s !important; | |
| } | |
| .gr-button:hover { | |
| background-color: #3a3ab8 !important; | |
| } | |
| .analysis-output { | |
| background: white; | |
| padding: 20px; | |
| border-radius: 8px; | |
| border: 1px solid #e0e5ff; | |
| margin-top: 20px; | |
| } | |
| .analysis-output h1 { | |
| font-size: 1.5em; | |
| font-weight: bold; | |
| margin-bottom: 1em; | |
| } | |
| .analysis-output h2 { | |
| font-size: 1.25em; | |
| font-weight: 600; | |
| margin-top: 1.5em; | |
| margin-bottom: 0.5em; | |
| } | |
| .analysis-output h3 { | |
| font-size: 1.1em; | |
| font-weight: 600; | |
| margin-top: 1em; | |
| margin-bottom: 0.5em; | |
| } | |
| .analysis-output ul { | |
| margin-left: 1.5em; | |
| margin-bottom: 1em; | |
| } | |
| .analysis-output li { | |
| margin-bottom: 0.5em; | |
| } | |
| .analysis-output p { | |
| margin-bottom: 1em; | |
| line-height: 1.6; | |
| } | |
| .analysis-output code { | |
| background: #f3f4f6; | |
| padding: 0.2em 0.4em; | |
| border-radius: 4px; | |
| font-size: 0.9em; | |
| } | |
| """ | |
| async def run_analysis(api_key: str, | |
| website_url: str, | |
| product_category: str, | |
| specific_product: str) -> str: | |
| """Handle the analysis submission""" | |
| if not api_key.startswith("sk-"): | |
| return "Please enter a valid OpenAI API key (should start with 'sk-')" | |
| if not website_url: | |
| return "Please enter a website URL" | |
| if not product_category: | |
| return "Please specify a product category" | |
| try: | |
| analyzer = AIShoppingAnalyzer(api_key) | |
| result = await analyzer.analyze_site( | |
| website_url=website_url, | |
| product_category=product_category, | |
| specific_product=specific_product if specific_product else None | |
| ) | |
| return result | |
| except Exception as e: | |
| return f"Error during analysis: {str(e)}" | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML(""" | |
| <div class="dashboard-container p-6"> | |
| <h1 class="text-2xl font-bold mb-2">AI Shopping Agent Analyzer</h1> | |
| <p class="text-gray-600 mb-6">Analyze how your e-commerce site performs with AI shoppers</p> | |
| </div> | |
| """) | |
| with gr.Group(): | |
| api_key = gr.Textbox( | |
| label="OpenAI API Key", | |
| placeholder="sk-...", | |
| type="password", | |
| container=True | |
| ) | |
| website_url = gr.Textbox( | |
| label="Website URL", | |
| placeholder="https://your-store.com", | |
| container=True | |
| ) | |
| product_category = gr.Textbox( | |
| label="Product Category", | |
| placeholder="e.g., Electronics, Clothing, etc.", | |
| container=True | |
| ) | |
| specific_product = gr.Textbox( | |
| label="Specific Product (Optional)", | |
| placeholder="e.g., Blue Widget Model X", | |
| container=True | |
| ) | |
| analyze_button = gr.Button( | |
| "Analyze Site", | |
| variant="primary" | |
| ) | |
| analysis_output = gr.Markdown( | |
| label="Analysis Results", | |
| value="Results will appear here...", | |
| elem_classes="analysis-output" | |
| ) | |
| analyze_button.click( | |
| fn=run_analysis, | |
| inputs=[api_key, website_url, product_category, specific_product], | |
| outputs=analysis_output | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| print("Setting up Playwright...") | |
| try: | |
| import subprocess | |
| subprocess.run( | |
| ["playwright", "install", "chromium"], | |
| check=True, | |
| capture_output=True, | |
| text=True | |
| ) | |
| except Exception as e: | |
| print(f"Warning: Playwright setup encountered an issue: {str(e)}") | |
| print("Starting Gradio interface...") | |
| demo = create_gradio_interface() | |
| demo.launch() |