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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
)
|