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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Complete Process Flow - BMS AI Assistant</title>
<style>
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box-sizing: border-box;
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body {
font-family: Arial, sans-serif;
background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
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.container {
max-width: 1200px;
margin: 0 auto;
background: white;
border-radius: 10px;
box-shadow: 0 10px 40px rgba(0, 0, 0, 0.1);
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.header {
background: linear-gradient(135deg, #D01010 0%, #A00C0C 100%);
color: white;
padding: 30px;
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border-radius: 10px 10px 0 0;
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.header h1 {
font-size: 2em;
margin-bottom: 10px;
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.content {
padding: 30px;
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.flow-step {
background: #f8f9fa;
border-left: 5px solid #D01010;
padding: 20px;
margin: 15px 0;
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.flow-step h3 {
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display: flex;
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gap: 10px;
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.step-num {
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justify-content: center;
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font-size: 0.9em;
overflow-x: auto;
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.branch {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 10px;
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.branch-item h4 {
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</style>
</head>
<body>
<div class="container">
<div class="header">
<h1>🔄 Complete Technical Process Flow</h1>
<p>BMS AI Assistant - User to System to User</p>
</div>
<div class="content">
<div class="flow-step">
<h3><span class="step-num">1</span> User Input</h3>
<p><strong>Component:</strong> Frontend UI (index.html)</p>
<p><strong>Action:</strong> User types query in chat interface</p>
<p><strong>Example:</strong> "What is the forecast for BMS0015 next month?"</p>
<div class="code">userInput.addEventListener('keypress', (e) => {
if (e.key === 'Enter') sendMessage();
});</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">2</span> Client Processing</h3>
<p><strong>Component:</strong> JavaScript</p>
<p><strong>Action:</strong> Validate input, display user message, prepare API call</p>
<div class="code">const text = userInput.value.trim();
addMessage(text, 'user');</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">3</span> HTTP POST Request</h3>
<p><strong>Endpoint:</strong> /api/chat</p>
<p><strong>Method:</strong> POST</p>
<p><strong>Payload:</strong> {"message": "user query"}</p>
<div class="code">fetch('/api/chat', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({message: text})
});</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">4</span> Server Receives Request</h3>
<p><strong>Component:</strong> FastAPI (main.py)</p>
<p><strong>Server:</strong> Uvicorn ASGI</p>
<div class="code">@app.post("/api/chat")
async def chat(request: ChatRequest):
message = request.message</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">5</span> Intent Parsing</h3>
<p><strong>Component:</strong> intent_parser.py</p>
<p><strong>Method:</strong> Regex pattern matching</p>
<p><strong>Extracts:</strong> Intent, Item code, Quantity, Location, Horizon</p>
<div class="code">parsed = parser.parse(message)
# Returns: {
# "intent": "demand_forecast",
# "item_code": "BMS0015",
# "horizon_days": 30
# }</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">6</span> Intent Routing</h3>
<p><strong>Decision Point:</strong> Route to appropriate handler</p>
<div class="branch">
<div class="branch-item">
<h4>Forecast</h4>
<p>forecasting.py</p>
</div>
<div class="branch-item">
<h4>Item Details</h4>
<p>data_loader.py</p>
</div>
<div class="branch-item">
<h4>Inventory</h4>
<p>data_loader.py</p>
</div>
<div class="branch-item">
<h4>Supplier</h4>
<p>data_loader.py</p>
</div>
<div class="branch-item">
<h4>Requisition</h4>
<p>data_loader.py</p>
</div>
<div class="branch-item">
<h4>Status</h4>
<p>data_loader.py</p>
</div>
<div class="branch-item">
<h4>PDF</h4>
<p>pdf_generator.py</p>
</div>
<div class="branch-item">
<h4>Chat</h4>
<p>llm_engine.py</p>
</div>
</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">7</span> Business Logic Execution</h3>
<p><strong>Example:</strong> Demand Forecast</p>
<p>1. Load demand_history.csv<br>
2. Filter by item_code<br>
3. Fit ARIMA model<br>
4. Generate forecast<br>
5. Format as JSON</p>
<div class="code">forecast_data = forecast_demand(item_code, horizon)
# Returns: [{"date": "2025-01-01", "qty": 150}, ...]</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">8</span> Data Retrieval</h3>
<p><strong>Component:</strong> data_loader.py</p>
<p><strong>Sources:</strong> items.csv, inventory.csv, suppliers.csv, etc.</p>
<p><strong>Technology:</strong> Pandas DataFrames</p>
<div class="code">item = loader.items_df[loader.items_df['item_code'] == 'BMS0015']</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">9</span> LLM Processing (if needed)</h3>
<p><strong>Component:</strong> llm_engine.py</p>
<p><strong>Trigger:</strong> Only for general_chat intent</p>
<p><strong>Model:</strong> TinyLlama 1.1B</p>
<p><strong>Time:</strong> 5-15 seconds</p>
<div class="code">response = llm.generate_response(query)
# Uses company_context.txt for RAG</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">10</span> Response Formatting</h3>
<p><strong>Component:</strong> main.py</p>
<p><strong>Format:</strong> JSON</p>
<div class="code">return {
"intent": "demand_forecast",
"answer": "Forecast for BMS0015...",
"forecast": [{"date": "2025-01-01", "qty": 150}]
}</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">11</span> HTTP Response</h3>
<p><strong>Status:</strong> 200 OK</p>
<p><strong>Content-Type:</strong> application/json</p>
<p><strong>Sent to:</strong> Client browser</p>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">12</span> Client Receives Response</h3>
<p><strong>Component:</strong> JavaScript Fetch API</p>
<p><strong>Action:</strong> Parse JSON response</p>
<div class="code">const data = await response.json();
let botHtml = data.answer;</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">13</span> UI Rendering</h3>
<p><strong>Component:</strong> JavaScript DOM manipulation</p>
<p><strong>Actions:</strong></p>
<p>1. Create message bubble<br>
2. Add bot icon<br>
3. Render forecast table (if present)<br>
4. Add download button (if PDF)<br>
5. Scroll to bottom</p>
<div class="code">addMessage(botHtml, 'bot', true);
if (data.forecast) {
botHtml += renderForecastTable(data.forecast);
}</div>
</div>
<div class="arrow">↓</div>
<div class="flow-step">
<h3><span class="step-num">14</span> User Sees Response</h3>
<p><strong>Display:</strong> Chat bubble with bot icon</p>
<p><strong>Features:</strong> Formatted text, tables, download links</p>
<p><strong>Animation:</strong> Fade-in effect</p>
<p><strong>Interaction:</strong> User can copy text, download PDFs, ask follow-up</p>
</div>
<div style="background: #e8f5e9; padding: 20px; border-radius: 5px; margin-top: 30px;">
<h3 style="color: #2e7d32; margin-bottom: 10px;">✅ Flow Complete</h3>
<p><strong>Total Steps:</strong> 14</p>
<p><strong>Average Time:</strong> 2-15 seconds (depending on intent)</p>
<p><strong>Technologies Used:</strong> HTML/JS, FastAPI, Pandas, ARIMA, TinyLlama</p>
</div>
</div>
</div>
</body>
</html> |