app.py

"""
🚀 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
custom_css = """
.gradio-container {
    font-family: 'Arial', sans-serif;
}
.tab-label {
    font-size: 16px !important;
}
"""

# Create Gradio interface
with gr.Blocks(theme=gr.themes.Soft(), css=custom_css, title="MissionControlMCP Demo") as demo:
    
    gr.Markdown("""
    # 🚀 MissionControlMCP - Enterprise Automation Tools
    
    **Try all 8 powerful tools in your browser!** No installation needed.
    
    📌 Built for the HuggingFace Gradio Hackathon | 🏆 Claude MCP Integration
    """)
    
    with gr.Tabs():
        
        # ====== TAB 1: PDF READER ======
        with gr.Tab("📄 PDF Reader"):
            gr.Markdown("### Extract text and metadata from PDF documents")
            
            with gr.Row():
                with gr.Column():
                    pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
                    pdf_btn = gr.Button("🔍 Extract Text", variant="primary")
                
                with gr.Column():
                    pdf_output = gr.Textbox(label="Results", lines=15)
                    pdf_img = gr.Image(label="Preview", visible=False)
            
            pdf_btn.click(tool_pdf_reader, inputs=[pdf_input], outputs=[pdf_output, pdf_img])
            
            gr.Examples([["Use the file upload above to try with your own PDF!"]], inputs=[])
        
        # ====== TAB 2: TEXT EXTRACTOR ======
        with gr.Tab("📝 Text Extractor"):
            gr.Markdown("### Extract keywords, generate summaries, clean text")
            
            with gr.Row():
                with gr.Column():
                    text_input = gr.Textbox(
                        label="Enter Text", 
                        lines=8,
                        placeholder="Paste your text here..."
                    )
                    text_operation = gr.Radio(
                        ["keywords", "summarize", "clean", "chunk"],
                        label="Operation",
                        value="keywords"
                    )
                    text_length = gr.Slider(100, 1000, 300, label="Max Length (for summarize/chunk)")
                    text_btn = gr.Button("✨ Process Text", variant="primary")
                
                with gr.Column():
                    text_output = gr.Textbox(label="Results", lines=15)
            
            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])
        
        # ====== TAB 3: WEB FETCHER ======
        with gr.Tab("🌐 Web Fetcher"):
            gr.Markdown("### Scrape and analyze web content")
            
            with gr.Row():
                with gr.Column():
                    web_input = gr.Textbox(
                        label="Website URL",
                        placeholder="https://example.com",
                        value="https://example.com"
                    )
                    web_btn = gr.Button("🌐 Fetch Website", variant="primary")
                
                with gr.Column():
                    web_output = gr.Textbox(label="Results", lines=15)
            
            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])
        
        # ====== TAB 4: RAG SEARCH ======
        with gr.Tab("🔍 RAG Search"):
            gr.Markdown("### Semantic document search with AI embeddings")
            
            with gr.Row():
                with gr.Column():
                    rag_input = gr.Textbox(
                        label="Search Query",
                        placeholder="What are you looking for?",
                        value="What is machine learning?"
                    )
                    rag_btn = gr.Button("🔍 Search Documents", variant="primary")
                
                with gr.Column():
                    rag_output = gr.Textbox(label="Search Results", lines=15)
            
            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])
        
        # ====== TAB 5: DATA VISUALIZER ======
        with gr.Tab("📊 Data Visualizer"):
            gr.Markdown("### Create beautiful charts from CSV data")
            
            with gr.Row():
                with gr.Column():
                    viz_csv = gr.Textbox(
                        label="CSV Data",
                        lines=8,
                        value=load_sample_csv(),
                        placeholder="month,revenue,costs\nJan,100000,60000"
                    )
                    viz_chart = gr.Radio(
                        ["line", "bar", "pie", "scatter"],
                        label="Chart Type",
                        value="line"
                    )
                    viz_x = gr.Textbox(label="X Column", value="month")
                    viz_y = gr.Textbox(label="Y Column", value="revenue")
                    viz_title = gr.Textbox(label="Chart Title", value="Monthly Revenue")
                    viz_btn = gr.Button("📊 Create Chart", variant="primary")
                
                with gr.Column():
                    viz_output = gr.Textbox(label="Status", lines=5)
                    viz_img = gr.Image(label="Chart")
            
            viz_btn.click(
                tool_data_visualizer,
                inputs=[viz_csv, viz_chart, viz_x, viz_y, viz_title],
                outputs=[viz_output, viz_img]
            )
        
        # ====== TAB 6: EMAIL CLASSIFIER ======
        with gr.Tab("📧 Email Classifier"):
            gr.Markdown("### Detect email intent with AI")
            
            with gr.Row():
                with gr.Column():
                    email_input = gr.Textbox(
                        label="Email Text",
                        lines=10,
                        value=load_sample_email(),
                        placeholder="Paste email content here..."
                    )
                    email_btn = gr.Button("🎯 Classify Email", variant="primary")
                
                with gr.Column():
                    email_output = gr.Textbox(label="Classification Results", lines=15)
            
            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])
        
        # ====== TAB 7: KPI GENERATOR ======
        with gr.Tab("📈 KPI Generator"):
            gr.Markdown("### Calculate business metrics and KPIs")
            
            with gr.Row():
                with gr.Column():
                    kpi_json = gr.Textbox(
                        label="Business Data (JSON)",
                        lines=12,
                        value=load_sample_json(),
                        placeholder='{"revenue": 1000000, "costs": 600000}'
                    )
                    kpi_metrics = gr.CheckboxGroup(
                        ["revenue", "growth", "efficiency", "customer", "operational"],
                        label="Metrics to Calculate",
                        value=["revenue", "growth", "efficiency"]
                    )
                    kpi_btn = gr.Button("📈 Generate KPIs", variant="primary")
                
                with gr.Column():
                    kpi_output = gr.Textbox(label="KPI Report", lines=20)
            
            kpi_btn.click(
                tool_kpi_generator,
                inputs=[kpi_json, kpi_metrics],
                outputs=[kpi_output]
            )
    
    # Footer
    gr.Markdown("""
    ---
    
    ### 🎯 About MissionControlMCP
    
    8 enterprise-grade automation tools integrated with Claude Desktop via Model Context Protocol (MCP).
    
    - **PDF Reader** - Extract text from documents
    - **Text Extractor** - Keywords, summaries, cleaning
    - **Web Fetcher** - Scrape websites
    - **RAG Search** - Semantic document search
    - **Data Visualizer** - Create charts
    - **File Converter** - Format conversions
    - **Email Classifier** - Intent detection
    - **KPI Generator** - Business analytics
    
    🔗 **GitHub:** [AlBaraa-1/CleanEye-Hackathon](https://github.com/AlBaraa-1/CleanEye-Hackathon)
    
    🏆 Built for HuggingFace Gradio x BuildWithMCP Hackathon
    """)


# ============================================================================
# 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
    )