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| """ | |
| 🚀 MissionControlMCP - Gradio Web Interface | |
| Beautiful GUI demo for all 8 tools! | |
| Run: python demo_gui.py | |
| Then share the public URL on LinkedIn! | |
| """ | |
| import gradio as gr | |
| import sys | |
| import os | |
| import json | |
| import base64 | |
| from io import BytesIO | |
| from PIL import Image | |
| # Setup paths | |
| SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) | |
| sys.path.append(SCRIPT_DIR) | |
| EXAMPLES_DIR = os.path.join(SCRIPT_DIR, "examples") | |
| # Import tools | |
| from tools.pdf_reader import read_pdf | |
| from tools.text_extractor import extract_text | |
| from tools.web_fetcher import fetch_web_content | |
| from tools.rag_search import search_documents | |
| from tools.data_visualizer import visualize_data | |
| from tools.file_converter import convert_file | |
| from tools.email_intent_classifier import classify_email_intent | |
| from tools.kpi_generator import generate_kpis | |
| # ============================================================================ | |
| # TOOL FUNCTIONS | |
| # ============================================================================ | |
| def tool_pdf_reader(pdf_file): | |
| """PDF Reader tool""" | |
| try: | |
| if pdf_file is None: | |
| return "❌ Please upload a PDF file!", None | |
| result = read_pdf(pdf_file.name) | |
| output = f"""✅ **PDF Analysis Complete!** | |
| 📄 **Metadata:** | |
| - Pages: {result['pages']} | |
| - Characters: {len(result['text']):,} | |
| - Author: {result['metadata'].get('author', 'N/A')} | |
| - Title: {result['metadata'].get('title', 'N/A')} | |
| 📝 **Extracted Text (first 1000 chars):** | |
| {result['text'][:1000]}... | |
| """ | |
| # Extract keywords | |
| keywords = extract_text(result['text'], operation="keywords") | |
| output += f"\n\n🔑 **Keywords:** {keywords['result']}" | |
| return output, None | |
| except Exception as e: | |
| return f"❌ Error: {str(e)}", None | |
| def tool_text_extractor(text, operation, max_length): | |
| """Text Extractor tool""" | |
| try: | |
| if not text.strip(): | |
| return "❌ Please enter some text!" | |
| result = extract_text(text, operation=operation, max_length=max_length) | |
| output = f"""✅ **Text Processing Complete!** | |
| 📊 **Operation:** {operation.upper()} | |
| 📏 **Word Count:** {result['word_count']} | |
| 📝 **Result:** | |
| {result['result']} | |
| """ | |
| return output | |
| except Exception as e: | |
| return f"❌ Error: {str(e)}" | |
| def tool_web_fetcher(url): | |
| """Web Fetcher tool""" | |
| try: | |
| if not url.strip(): | |
| return "❌ Please enter a URL!" | |
| result = fetch_web_content(url) | |
| if result['status_code'] == 999: | |
| return f"""⚠️ **Status 999 - Bot Detection** | |
| The website is blocking automated requests. | |
| This is common for LinkedIn, Facebook, etc. | |
| Try a different website!""" | |
| output = f"""✅ **Website Fetched Successfully!** | |
| 🌐 **URL:** {url} | |
| 📊 **Status:** {result['status_code']} | |
| 📄 **Title:** {result.get('title', 'N/A')} | |
| 📏 **Content Length:** {len(result['content']):,} characters | |
| 🔗 **Links Found:** {len(result.get('links', []))} | |
| 📝 **Content Preview (first 1000 chars):** | |
| {result['content'][:1000]}... | |
| """ | |
| # Extract keywords | |
| if len(result['content']) > 50: | |
| keywords = extract_text(result['content'], operation="keywords") | |
| output += f"\n\n🔑 **Keywords:** {keywords['result']}" | |
| return output | |
| except Exception as e: | |
| return f"❌ Error: {str(e)}" | |
| def tool_rag_search(query): | |
| """RAG Search tool""" | |
| try: | |
| if not query.strip(): | |
| return "❌ Please enter a search query!" | |
| # Load sample documents | |
| docs_file = os.path.join(EXAMPLES_DIR, "sample_documents.txt") | |
| with open(docs_file, "r", encoding="utf-8") as f: | |
| content = f.read() | |
| documents = [doc.strip() for doc in content.split("##") if doc.strip()] | |
| result = search_documents(query, documents, top_k=3) | |
| output = f"""✅ **Search Complete!** | |
| 🔍 **Query:** "{query}" | |
| 📚 **Documents Searched:** {len(documents)} | |
| 📊 **Results Found:** {len(result['results'])} | |
| 🎯 **Top Results:** | |
| """ | |
| for i, res in enumerate(result['results'], 1): | |
| preview = res['document'][:200].replace('\n', ' ') | |
| output += f""" | |
| **Result {i}** (Score: {res['score']:.4f}) | |
| {preview}... | |
| """ | |
| return output | |
| except Exception as e: | |
| return f"❌ Error: {str(e)}" | |
| def tool_data_visualizer(csv_data, chart_type, x_col, y_col, title): | |
| """Data Visualizer tool""" | |
| try: | |
| if not csv_data.strip(): | |
| return "❌ Please enter CSV data!", None | |
| result = visualize_data( | |
| data=csv_data, | |
| chart_type=chart_type, | |
| x_column=x_col, | |
| y_column=y_col, | |
| title=title | |
| ) | |
| # Convert base64 to image | |
| img_data = base64.b64decode(result['image_base64']) | |
| image = Image.open(BytesIO(img_data)) | |
| output = f"""✅ **Chart Created!** | |
| 📊 **Chart Type:** {chart_type.upper()} | |
| 📏 **Dimensions:** {result['dimensions']} | |
| 📈 **Title:** {title} | |
| """ | |
| return output, image | |
| except Exception as e: | |
| return f"❌ Error: {str(e)}", None | |
| def tool_email_classifier(email_text): | |
| """Email Intent Classifier tool""" | |
| try: | |
| if not email_text.strip(): | |
| return "❌ Please enter email text!" | |
| result = classify_email_intent(email_text) | |
| output = f"""✅ **Email Classified!** | |
| 🎯 **Primary Intent:** {result['intent'].upper()} | |
| 📊 **Confidence:** {result['confidence']:.2%} | |
| 💬 **Explanation:** | |
| {result['explanation']} | |
| """ | |
| if result['secondary_intents']: | |
| output += "\n\n📋 **Secondary Intents:**\n" | |
| for intent in result['secondary_intents'][:3]: | |
| output += f"- {intent['intent']}: {intent['confidence']:.2%}\n" | |
| return output | |
| except Exception as e: | |
| return f"❌ Error: {str(e)}" | |
| def tool_kpi_generator(business_json, metrics): | |
| """KPI Generator tool""" | |
| try: | |
| if not business_json.strip(): | |
| return "❌ Please enter business data!" | |
| # Validate JSON | |
| json.loads(business_json) | |
| result = generate_kpis(business_json, metrics=metrics) | |
| output = f"""✅ **KPIs Generated!** | |
| 📊 **Total KPIs Calculated:** {len(result['kpis'])} | |
| 📈 **Key Metrics:** | |
| """ | |
| # Display top 15 KPIs | |
| for i, (name, value) in enumerate(list(result['kpis'].items())[:15], 1): | |
| # Format based on metric type | |
| if 'percent' in name or 'rate' in name or 'margin' in name: | |
| formatted = f"{value:.1f}%" | |
| elif 'revenue' in name or 'profit' in name or 'cost' in name: | |
| formatted = f"${value:,.0f}" | |
| else: | |
| formatted = f"{value:,.2f}" | |
| display_name = name.replace('_', ' ').title() | |
| output += f"{i}. **{display_name}:** {formatted}\n" | |
| output += f"\n\n📝 **Executive Summary:**\n{result['summary']}" | |
| if result.get('trends'): | |
| output += "\n\n📊 **Key Trends:**\n" | |
| for trend in result['trends'][:5]: | |
| output += f"- {trend}\n" | |
| return output | |
| except json.JSONDecodeError: | |
| return "❌ Invalid JSON format! Please check your data." | |
| except Exception as e: | |
| return f"❌ Error: {str(e)}" | |
| # ============================================================================ | |
| # LOAD SAMPLE DATA | |
| # ============================================================================ | |
| def load_sample_csv(): | |
| csv_file = os.path.join(EXAMPLES_DIR, "business_data.csv") | |
| with open(csv_file, "r") as f: | |
| return f.read() | |
| def load_sample_email(): | |
| email_file = os.path.join(EXAMPLES_DIR, "sample_email_complaint.txt") | |
| with open(email_file, "r", encoding="utf-8") as f: | |
| return f.read() | |
| def load_sample_json(): | |
| return """{ | |
| "revenue": 5500000, | |
| "costs": 3400000, | |
| "customers": 2700, | |
| "current_revenue": 5500000, | |
| "previous_revenue": 5400000, | |
| "current_customers": 2700, | |
| "previous_customers": 2650, | |
| "employees": 50, | |
| "marketing_spend": 500000, | |
| "sales": 5500000, | |
| "cogs": 2000000 | |
| }""" | |
| # ============================================================================ | |
| # GRADIO INTERFACE | |
| # ============================================================================ | |
| # Custom CSS for beautiful UI | |
| custom_css = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap'); | |
| /* Global mobile-first settings */ | |
| * { | |
| box-sizing: border-box !important; | |
| } | |
| html, body { | |
| overflow-x: hidden !important; | |
| width: 100% !important; | |
| } | |
| .gradio-container { | |
| font-family: 'Inter', sans-serif !important; | |
| max-width: 1400px !important; | |
| margin: 0 auto !important; | |
| padding: 10px !important; | |
| width: 100% !important; | |
| overflow-x: hidden !important; | |
| } | |
| /* Header styling */ | |
| .gradio-container h1 { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| background-clip: text; | |
| font-size: 3em !important; | |
| font-weight: 700 !important; | |
| text-align: center; | |
| margin-bottom: 0.5em; | |
| } | |
| /* Mobile responsive header */ | |
| @media (max-width: 768px) { | |
| .gradio-container h1 { | |
| font-size: 2em !important; | |
| } | |
| .gradio-container h3 { | |
| font-size: 1.2em !important; | |
| } | |
| .gradio-container p { | |
| font-size: 0.9em !important; | |
| } | |
| } | |
| /* Tab styling */ | |
| .tab-nav { | |
| border-radius: 12px !important; | |
| background: linear-gradient(to right, #f8f9fa, #e9ecef) !important; | |
| padding: 8px !important; | |
| margin-bottom: 20px !important; | |
| } | |
| button.selected { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important; | |
| color: white !important; | |
| border-radius: 8px !important; | |
| font-weight: 600 !important; | |
| box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4) !important; | |
| } | |
| /* Mobile tab styling */ | |
| @media (max-width: 768px) { | |
| .tab-nav button { | |
| font-size: 0.85em !important; | |
| padding: 8px 12px !important; | |
| } | |
| } | |
| /* Button styling */ | |
| .primary-btn { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important; | |
| border: none !important; | |
| color: white !important; | |
| font-weight: 600 !important; | |
| border-radius: 10px !important; | |
| padding: 12px 24px !important; | |
| font-size: 16px !important; | |
| transition: all 0.3s ease !important; | |
| box-shadow: 0 4px 15px rgba(102, 126, 234, 0.4) !important; | |
| } | |
| .primary-btn:hover { | |
| transform: translateY(-2px) !important; | |
| box-shadow: 0 6px 20px rgba(102, 126, 234, 0.6) !important; | |
| } | |
| /* Mobile button styling */ | |
| @media (max-width: 768px) { | |
| .primary-btn { | |
| width: 100% !important; | |
| padding: 14px 20px !important; | |
| font-size: 15px !important; | |
| } | |
| } | |
| /* Input fields */ | |
| textarea, input[type="text"] { | |
| border-radius: 10px !important; | |
| border: 2px solid #e9ecef !important; | |
| padding: 12px !important; | |
| font-size: 15px !important; | |
| transition: border-color 0.3s ease !important; | |
| } | |
| textarea:focus, input[type="text"]:focus { | |
| border-color: #667eea !important; | |
| box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1) !important; | |
| } | |
| /* Mobile input fields */ | |
| @media (max-width: 768px) { | |
| textarea, input[type="text"] { | |
| font-size: 16px !important; | |
| padding: 10px !important; | |
| } | |
| } | |
| /* Output boxes */ | |
| .output-class { | |
| background: linear-gradient(to bottom, #ffffff, #f8f9fa) !important; | |
| border-radius: 12px !important; | |
| padding: 20px !important; | |
| border: 2px solid #e9ecef !important; | |
| } | |
| /* Cards and containers */ | |
| .gr-box { | |
| border-radius: 12px !important; | |
| border: 1px solid #e9ecef !important; | |
| box-shadow: 0 2px 8px rgba(0,0,0,0.05) !important; | |
| } | |
| /* Labels */ | |
| label { | |
| font-weight: 600 !important; | |
| color: #495057 !important; | |
| font-size: 14px !important; | |
| margin-bottom: 8px !important; | |
| } | |
| /* Examples */ | |
| .gr-samples-table { | |
| border-radius: 10px !important; | |
| overflow: hidden !important; | |
| } | |
| /* Footer */ | |
| .footer { | |
| text-align: center; | |
| padding: 30px; | |
| background: linear-gradient(to right, #f8f9fa, #e9ecef); | |
| border-radius: 12px; | |
| margin-top: 30px; | |
| } | |
| /* Image display */ | |
| .gr-image { | |
| border-radius: 12px !important; | |
| border: 2px solid #e9ecef !important; | |
| box-shadow: 0 4px 15px rgba(0,0,0,0.1) !important; | |
| } | |
| /* Radio buttons and checkboxes */ | |
| .gr-radio, .gr-checkbox { | |
| padding: 10px !important; | |
| border-radius: 8px !important; | |
| } | |
| /* File upload */ | |
| .gr-file { | |
| border: 2px dashed #667eea !important; | |
| border-radius: 12px !important; | |
| background: linear-gradient(to bottom, #ffffff, #f8f9fa) !important; | |
| padding: 30px !important; | |
| } | |
| .gr-file:hover { | |
| border-color: #764ba2 !important; | |
| background: #f8f9fa !important; | |
| } | |
| /* Mobile responsive layout */ | |
| @media (max-width: 768px) { | |
| .gr-row { | |
| flex-direction: column !important; | |
| gap: 15px !important; | |
| } | |
| .gr-column { | |
| width: 100% !important; | |
| max-width: 100% !important; | |
| min-width: 0 !important; | |
| flex: 0 0 100% !important; | |
| } | |
| .gr-file { | |
| padding: 20px !important; | |
| } | |
| /* Stack columns vertically on mobile */ | |
| .gradio-container .gr-form { | |
| display: flex !important; | |
| flex-direction: column !important; | |
| } | |
| /* Prevent text overflow */ | |
| .gradio-container { | |
| word-wrap: break-word !important; | |
| overflow-wrap: break-word !important; | |
| } | |
| /* Better spacing on mobile */ | |
| .gr-box { | |
| margin: 10px 0 !important; | |
| } | |
| /* Tabs scrollable on mobile */ | |
| .tab-nav { | |
| overflow-x: auto !important; | |
| -webkit-overflow-scrolling: touch !important; | |
| white-space: nowrap !important; | |
| } | |
| } | |
| /* Improve touch targets for mobile */ | |
| @media (max-width: 768px) { | |
| button, a, input, textarea, select { | |
| min-height: 44px !important; | |
| -webkit-tap-highlight-color: rgba(102, 126, 234, 0.2) !important; | |
| } | |
| label { | |
| font-size: 15px !important; | |
| margin-bottom: 10px !important; | |
| } | |
| /* Better radio and checkbox sizing */ | |
| .gr-radio label, .gr-checkbox label { | |
| padding: 12px !important; | |
| min-height: 44px !important; | |
| display: flex !important; | |
| align-items: center !important; | |
| } | |
| /* Mobile-friendly sliders */ | |
| input[type="range"] { | |
| height: 44px !important; | |
| padding: 10px 0 !important; | |
| } | |
| /* Examples section */ | |
| .gr-samples-table { | |
| overflow-x: auto !important; | |
| -webkit-overflow-scrolling: touch !important; | |
| } | |
| } | |
| /* Footer responsive */ | |
| @media (max-width: 768px) { | |
| .footer { | |
| padding: 20px 10px !important; | |
| } | |
| .footer h2 { | |
| font-size: 1.5em !important; | |
| } | |
| .footer p { | |
| font-size: 0.9em !important; | |
| } | |
| } | |
| /* Smaller screens (phones in portrait) */ | |
| @media (max-width: 480px) { | |
| .gradio-container h1 { | |
| font-size: 1.5em !important; | |
| } | |
| .tab-nav button { | |
| font-size: 0.75em !important; | |
| padding: 6px 10px !important; | |
| } | |
| .primary-btn { | |
| font-size: 14px !important; | |
| padding: 12px 16px !important; | |
| } | |
| /* Reduce line count for better mobile UX */ | |
| textarea { | |
| min-height: 100px !important; | |
| } | |
| } | |
| /* Landscape mobile */ | |
| @media (max-width: 768px) and (orientation: landscape) { | |
| .gradio-container h1 { | |
| font-size: 1.8em !important; | |
| margin-bottom: 0.3em !important; | |
| } | |
| } | |
| """ | |
| # Create Gradio interface | |
| with gr.Blocks(theme=gr.themes.Soft(), css=custom_css, title="MissionControlMCP Demo") as demo: | |
| # Add viewport meta tag for proper mobile rendering | |
| gr.HTML("""<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=5.0, user-scalable=yes">""") | |
| gr.Markdown("# 🚀 MissionControlMCP") | |
| gr.Markdown("### Enterprise Automation Tools - Powered by AI") | |
| gr.HTML(""" | |
| <div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 15px; color: white; margin-bottom: 30px;"> | |
| <h3 style="color: white; margin: 0; font-size: clamp(1rem, 4vw, 1.5rem);">✨ Try all 8 powerful tools in your browser - No installation needed! ✨</h3> | |
| <p style="margin: 10px 0 0 0; opacity: 0.9; font-size: clamp(0.8rem, 3vw, 1rem);">Built for HuggingFace Gradio Hackathon | Claude MCP Integration</p> | |
| </div> | |
| """) | |
| with gr.Tabs(): | |
| # ====== TAB 1: PDF READER ====== | |
| with gr.Tab("📄 PDF Reader"): | |
| gr.Markdown(""" | |
| ### 📄 Extract Text and Metadata from PDF Documents | |
| Upload any PDF file to extract its content, metadata, and keywords instantly. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| pdf_input = gr.File( | |
| label="📎 Upload PDF File", | |
| file_types=[".pdf"], | |
| elem_classes=["file-upload"] | |
| ) | |
| pdf_btn = gr.Button( | |
| "🔍 Extract Text from PDF", | |
| variant="primary", | |
| size="lg", | |
| elem_classes=["primary-btn"] | |
| ) | |
| gr.Markdown(""" | |
| **💡 Tips:** | |
| - Supports multi-page PDFs | |
| - Extracts metadata (author, title) | |
| - Automatically generates keywords | |
| **⚠️ Limitations:** | |
| - Max file size: 200 MB (HuggingFace limit) | |
| - Text-based PDFs only (scanned images need OCR) | |
| - Processing time: ~2-5 seconds per page | |
| """) | |
| with gr.Column(scale=2): | |
| pdf_output = gr.Textbox( | |
| label="📊 Extraction Results", | |
| lines=20, | |
| elem_classes=["output-class"] | |
| ) | |
| pdf_img = gr.Image(label="Preview", visible=False) | |
| pdf_btn.click(tool_pdf_reader, inputs=[pdf_input], outputs=[pdf_output, pdf_img]) | |
| gr.Markdown("*💡 Try uploading your resume, research paper, or any PDF document!*") | |
| # ====== TAB 2: TEXT EXTRACTOR ====== | |
| with gr.Tab("📝 Text Extractor"): | |
| gr.Markdown(""" | |
| ### 📝 AI-Powered Text Analysis | |
| Extract keywords, generate summaries, clean text, or split into chunks. | |
| **⚠️ Limitations:** | |
| - Max input: ~50,000 characters | |
| - Summary length: 100-1000 characters (adjustable) | |
| - Processing time: ~1-3 seconds | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| text_input = gr.Textbox( | |
| label="✍️ Enter Your Text", | |
| lines=10, | |
| placeholder="Paste any text here - articles, reports, emails, etc...", | |
| elem_classes=["input-field"] | |
| ) | |
| text_operation = gr.Radio( | |
| ["keywords", "summarize", "clean", "chunk"], | |
| label="🛠️ Select Operation", | |
| value="keywords", | |
| info="Choose what to do with your text" | |
| ) | |
| text_length = gr.Slider( | |
| 100, 1000, 300, | |
| label="📏 Max Length (for summarize/chunk)", | |
| info="Adjust output length" | |
| ) | |
| text_btn = gr.Button( | |
| "✨ Process Text", | |
| variant="primary", | |
| size="lg", | |
| elem_classes=["primary-btn"] | |
| ) | |
| with gr.Column(scale=2): | |
| text_output = gr.Textbox( | |
| label="📊 Processing Results", | |
| lines=20, | |
| elem_classes=["output-class"] | |
| ) | |
| text_btn.click( | |
| tool_text_extractor, | |
| inputs=[text_input, text_operation, text_length], | |
| outputs=[text_output] | |
| ) | |
| gr.Examples([ | |
| ["Artificial Intelligence is transforming businesses worldwide. Companies are leveraging AI for automation, decision-making, and customer service. Machine learning models can now process vast amounts of data and provide actionable insights.", "keywords", 300], | |
| ["Climate change is one of the most pressing challenges of our time. Rising temperatures, extreme weather events, and environmental degradation require urgent action.", "summarize", 300] | |
| ], inputs=[text_input, text_operation, text_length], label="📚 Try These Examples") | |
| # ====== TAB 3: WEB FETCHER ====== | |
| with gr.Tab("🌐 Web Fetcher"): | |
| gr.Markdown(""" | |
| ### 🌐 Scrape and Analyze Web Content | |
| Fetch content from any website, extract clean text, and analyze it. | |
| **⚠️ Limitations:** | |
| - Timeout: 30 seconds per request | |
| - Some sites block automated access (LinkedIn, Facebook, etc.) | |
| - JavaScript-heavy sites may not render fully | |
| - Rate limits apply to prevent abuse | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| web_input = gr.Textbox( | |
| label="🔗 Website URL", | |
| placeholder="https://example.com", | |
| value="https://example.com", | |
| info="Enter any public website URL" | |
| ) | |
| web_btn = gr.Button( | |
| "🌐 Fetch Website", | |
| variant="primary", | |
| size="lg", | |
| elem_classes=["primary-btn"] | |
| ) | |
| gr.Markdown(""" | |
| **💡 Tips:** | |
| - Works with most public websites | |
| - Extracts clean text (no HTML) | |
| - Finds all page links | |
| - Some sites block bots (e.g., LinkedIn) | |
| """) | |
| with gr.Column(scale=2): | |
| web_output = gr.Textbox( | |
| label="📊 Website Content", | |
| lines=20, | |
| elem_classes=["output-class"] | |
| ) | |
| web_btn.click(tool_web_fetcher, inputs=[web_input], outputs=[web_output]) | |
| gr.Examples([ | |
| ["https://example.com"], | |
| ["https://python.org"], | |
| ["https://github.com"] | |
| ], inputs=[web_input], label="📚 Try These Examples") | |
| # ====== TAB 4: RAG SEARCH ====== | |
| with gr.Tab("🔍 RAG Search"): | |
| gr.Markdown(""" | |
| ### 🔍 Semantic Document Search with AI | |
| Search through documents using AI-powered semantic understanding (RAG - Retrieval Augmented Generation). | |
| **⚠️ Limitations:** | |
| - Currently searches 5 pre-loaded sample documents | |
| - First search: ~5-10 seconds (loads AI model) | |
| - Subsequent searches: ~1-2 seconds | |
| - Max 1000 documents supported | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| rag_input = gr.Textbox( | |
| label="🔎 Search Query", | |
| placeholder="What are you looking for?", | |
| value="What is machine learning?", | |
| lines=3, | |
| info="Ask questions in natural language" | |
| ) | |
| rag_btn = gr.Button( | |
| "🔍 Search Documents", | |
| variant="primary", | |
| size="lg", | |
| elem_classes=["primary-btn"] | |
| ) | |
| gr.Markdown(""" | |
| **💡 How it works:** | |
| - Uses AI embeddings (FAISS) | |
| - Understands meaning, not just keywords | |
| - Searches 5 sample documents | |
| - Returns relevance scores | |
| """) | |
| with gr.Column(scale=2): | |
| rag_output = gr.Textbox( | |
| label="📊 Search Results", | |
| lines=20, | |
| elem_classes=["output-class"] | |
| ) | |
| rag_btn.click(tool_rag_search, inputs=[rag_input], outputs=[rag_output]) | |
| gr.Examples([ | |
| ["What is machine learning?"], | |
| ["How to reduce carbon emissions?"], | |
| ["What are modern web frameworks?"], | |
| ["Digital marketing strategies"] | |
| ], inputs=[rag_input], label="📚 Try These Searches") | |
| # ====== TAB 5: DATA VISUALIZER ====== | |
| with gr.Tab("📊 Data Visualizer"): | |
| gr.Markdown(""" | |
| ### 📊 Create Beautiful Charts from Your Data | |
| Transform CSV data into stunning visualizations - line charts, bar charts, pie charts, and scatter plots. | |
| **⚠️ Limitations:** | |
| - Max 10,000 rows of data | |
| - CSV format only (comma-separated) | |
| - Generated as PNG images (800x600px) | |
| - Numeric columns required for pie/scatter charts | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| viz_csv = gr.Textbox( | |
| label="📋 CSV Data", | |
| lines=10, | |
| value=load_sample_csv(), | |
| placeholder="month,revenue,costs\nJan,100000,60000", | |
| info="Paste your CSV data here" | |
| ) | |
| viz_chart = gr.Radio( | |
| ["line", "bar", "pie", "scatter"], | |
| label="📈 Chart Type", | |
| value="line", | |
| info="Select visualization style" | |
| ) | |
| viz_x = gr.Textbox(label="📍 X-Axis Column", value="month") | |
| viz_y = gr.Textbox(label="📍 Y-Axis Column", value="revenue") | |
| viz_title = gr.Textbox(label="📝 Chart Title", value="Monthly Revenue") | |
| viz_btn = gr.Button( | |
| "📊 Create Chart", | |
| variant="primary", | |
| size="lg", | |
| elem_classes=["primary-btn"] | |
| ) | |
| with gr.Column(scale=2): | |
| viz_output = gr.Textbox( | |
| label="📊 Chart Status", | |
| lines=5, | |
| elem_classes=["output-class"] | |
| ) | |
| viz_img = gr.Image(label="📈 Generated Chart", elem_classes=["chart-output"]) | |
| viz_btn.click( | |
| tool_data_visualizer, | |
| inputs=[viz_csv, viz_chart, viz_x, viz_y, viz_title], | |
| outputs=[viz_output, viz_img] | |
| ) | |
| gr.Markdown("*💡 Sample data is already loaded! Just click 'Create Chart' to see it in action.*") | |
| # ====== TAB 6: EMAIL CLASSIFIER ====== | |
| with gr.Tab("📧 Email Classifier"): | |
| gr.Markdown(""" | |
| ### 📧 AI-Powered Email Intent Detection | |
| Automatically classify email intent and detect sentiment - complaint, inquiry, urgent, etc. | |
| **⚠️ Limitations:** | |
| - Rule-based classification (not deep learning) | |
| - Max 10,000 characters per email | |
| - English language optimized | |
| - 10 intent categories supported | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| email_input = gr.Textbox( | |
| label="✉️ Email Content", | |
| lines=12, | |
| value=load_sample_email(), | |
| placeholder="Paste email content here...", | |
| info="Paste any email text for analysis" | |
| ) | |
| email_btn = gr.Button( | |
| "🎯 Classify Email", | |
| variant="primary", | |
| size="lg", | |
| elem_classes=["primary-btn"] | |
| ) | |
| gr.Markdown(""" | |
| **💡 Detects 10 intents:** | |
| - Complaint | |
| - Inquiry | |
| - Request | |
| - Feedback | |
| - Order | |
| - Meeting | |
| - Urgent | |
| - Application | |
| - Sales | |
| - Other | |
| """) | |
| with gr.Column(scale=2): | |
| email_output = gr.Textbox( | |
| label="📊 Classification Results", | |
| lines=20, | |
| elem_classes=["output-class"] | |
| ) | |
| email_btn.click(tool_email_classifier, inputs=[email_input], outputs=[email_output]) | |
| gr.Examples([ | |
| ["I am writing to complain about the poor service I received at your store yesterday."], | |
| ["Could you please send me more information about your pricing plans?"], | |
| ["URGENT: The server is down and customers cannot access the website!"] | |
| ], inputs=[email_input], label="📚 Try These Examples") | |
| # ====== TAB 7: KPI GENERATOR ====== | |
| with gr.Tab("📈 KPI Generator"): | |
| gr.Markdown(""" | |
| ### 📈 Business KPI & Analytics Dashboard | |
| Generate comprehensive business metrics and KPIs from your data automatically. | |
| **⚠️ Limitations:** | |
| - JSON format required | |
| - Max 50 KPIs calculated per request | |
| - Numeric data only for calculations | |
| - Processing time: ~1-2 seconds | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| kpi_json = gr.Textbox( | |
| label="📊 Business Data (JSON Format)", | |
| lines=14, | |
| value=load_sample_json(), | |
| placeholder='{"revenue": 1000000, "costs": 600000}', | |
| info="Enter your business metrics in JSON" | |
| ) | |
| kpi_metrics = gr.CheckboxGroup( | |
| ["revenue", "growth", "efficiency", "customer", "operational"], | |
| label="📋 Metrics to Calculate", | |
| value=["revenue", "growth", "efficiency"], | |
| info="Select which KPI categories to generate" | |
| ) | |
| kpi_btn = gr.Button( | |
| "📈 Generate KPIs", | |
| variant="primary", | |
| size="lg", | |
| elem_classes=["primary-btn"] | |
| ) | |
| gr.Markdown(""" | |
| **💡 Generates:** | |
| - Revenue metrics | |
| - Growth rates | |
| - Efficiency ratios | |
| - Customer metrics | |
| - Operational KPIs | |
| - Executive summary | |
| """) | |
| with gr.Column(scale=2): | |
| kpi_output = gr.Textbox( | |
| label="📊 KPI Report", | |
| lines=25, | |
| elem_classes=["output-class"] | |
| ) | |
| kpi_btn.click( | |
| tool_kpi_generator, | |
| inputs=[kpi_json, kpi_metrics], | |
| outputs=[kpi_output] | |
| ) | |
| gr.Markdown("*💡 Sample business data is already loaded! Just click 'Generate KPIs' to see results.*") | |
| # Footer | |
| gr.HTML(""" | |
| <div class="footer"> | |
| <h2 style="margin-bottom: 20px; font-size: clamp(1.2rem, 5vw, 2rem);">🎯 About MissionControlMCP</h2> | |
| <p style="font-size: clamp(0.9rem, 3vw, 1.125rem); margin-bottom: 20px;"> | |
| <strong>8 enterprise-grade automation tools</strong> integrated with Claude Desktop via Model Context Protocol (MCP) | |
| </p> | |
| <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 15px; margin: 30px 0;"> | |
| <div style="padding: 15px; background: white; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.1);"> | |
| <strong style="font-size: clamp(0.85rem, 2.5vw, 1rem);">📄 PDF Reader</strong><br/> | |
| <small style="font-size: clamp(0.7rem, 2vw, 0.875rem);">Extract text from documents</small> | |
| </div> | |
| <div style="padding: 15px; background: white; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.1);"> | |
| <strong style="font-size: clamp(0.85rem, 2.5vw, 1rem);">📝 Text Extractor</strong><br/> | |
| <small style="font-size: clamp(0.7rem, 2vw, 0.875rem);">Keywords & summaries</small> | |
| </div> | |
| <div style="padding: 15px; background: white; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.1);"> | |
| <strong style="font-size: clamp(0.85rem, 2.5vw, 1rem);">🌐 Web Fetcher</strong><br/> | |
| <small style="font-size: clamp(0.7rem, 2vw, 0.875rem);">Scrape websites</small> | |
| </div> | |
| <div style="padding: 15px; background: white; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.1);"> | |
| <strong style="font-size: clamp(0.85rem, 2.5vw, 1rem);">🔍 RAG Search</strong><br/> | |
| <small style="font-size: clamp(0.7rem, 2vw, 0.875rem);">Semantic search</small> | |
| </div> | |
| <div style="padding: 15px; background: white; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.1);"> | |
| <strong style="font-size: clamp(0.85rem, 2.5vw, 1rem);">📊 Data Visualizer</strong><br/> | |
| <small style="font-size: clamp(0.7rem, 2vw, 0.875rem);">Create charts</small> | |
| </div> | |
| <div style="padding: 15px; background: white; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.1);"> | |
| <strong style="font-size: clamp(0.85rem, 2.5vw, 1rem);">🔄 File Converter</strong><br/> | |
| <small style="font-size: clamp(0.7rem, 2vw, 0.875rem);">Format conversions</small> | |
| </div> | |
| <div style="padding: 15px; background: white; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.1);"> | |
| <strong style="font-size: clamp(0.85rem, 2.5vw, 1rem);">📧 Email Classifier</strong><br/> | |
| <small style="font-size: clamp(0.7rem, 2vw, 0.875rem);">Intent detection</small> | |
| </div> | |
| <div style="padding: 15px; background: white; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.1);"> | |
| <strong style="font-size: clamp(0.85rem, 2.5vw, 1rem);">📈 KPI Generator</strong><br/> | |
| <small style="font-size: clamp(0.7rem, 2vw, 0.875rem);">Business analytics</small> | |
| </div> | |
| </div> | |
| <div style="margin-top: 30px; padding-top: 20px; border-top: 2px solid #e9ecef;"> | |
| <p style="font-size: clamp(0.85rem, 2.5vw, 1rem); margin: 10px 0;"> | |
| 🔗 <a href="https://github.com/AlBaraa-1/CleanEye-Hackathon" target="_blank" style="color: #667eea; text-decoration: none; font-weight: 600;">View on GitHub</a> | |
| </p> | |
| <p style="margin: 10px 0; color: #6c757d; font-size: clamp(0.75rem, 2vw, 0.875rem);"> | |
| 🏆 Built for HuggingFace Gradio x BuildWithMCP Hackathon | |
| </p> | |
| <p style="margin: 10px 0; color: #6c757d; font-size: clamp(0.75rem, 2vw, 0.875rem);"> | |
| Made with ❤️ using Python, Gradio, Claude MCP, FAISS, and Sentence Transformers | |
| </p> | |
| </div> | |
| </div> | |
| """) | |
| # ============================================================================ | |
| # LAUNCH | |
| # ============================================================================ | |
| if __name__ == "__main__": | |
| print("\n" + "="*80) | |
| print("🚀 Launching MissionControlMCP Web Interface...") | |
| print("="*80) | |
| # Launch with public sharing enabled | |
| demo.launch( | |
| share=True, # Creates public URL! | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| show_error=True | |
| ) | |