Spaces:

JanviMl
/

QA_VAM

Paused

Navigate to Hugging Face Spaces
Click "Create new Space"
Fill in the details:
- Space name: stlc-ai-demo (or your preferred name)
- License: MIT
- SDK: Select "Gradio"
- Visibility: "Public" (recommended for demo)
- Hardware: "CPU basic" (sufficient for this demo)

1.2 Initialize Repository

# Clone your new space repository
git clone https://huggingface.co/spaces/YOUR_USERNAME/stlc-ai-demo
cd stlc-ai-demo

# Verify you're in the correct directory
ls -la
# Should show .git folder and possibly README.md

🗂️ Step 2: Upload Project Files

2.1 Copy All Project Files

Copy the following files to your space directory:

# Copy main application files
cp /path/to/your/project/app.py .
cp /path/to/your/project/utils.py .
cp /path/to/your/project/requirements.txt .
cp /path/to/your/project/README.md .

# Copy configuration and data files
cp /path/to/your/project/prompts.yaml .
cp /path/to/your/project/dummy_user_stories.json .
cp /path/to/your/project/test_log_samples.json .

# Optional: Environment file template
cp /path/to/your/project/.env.example .

2.2 Verify File Structure

ls -la
# Should show:
# app.py
# utils.py
# requirements.txt
# README.md
# prompts.yaml
# dummy_user_stories.json
# test_log_samples.json
# .git/

2.3 Test Files Locally (Optional)

# Quick test to ensure everything works
python app.py
# Should launch Gradio interface on localhost:7860

📝 Step 3: Configure Space Settings

3.1 Create/Update README.md

Ensure your README.md has the proper front matter for Hugging Face:

---
title: STLC-AI Demo
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.8.0
app_file: app.py
pinned: false
license: mit
tags:
  - artificial-intelligence
  - testing
  - insurance
  - automation
  - qa
  - gradio
  - demo
---

3.2 Optimize requirements.txt

Ensure your requirements.txt includes version constraints:

gradio>=4.8.0,<5.0.0
openai>=1.3.0,<2.0.0
pyyaml>=6.0,<7.0
python-dotenv>=1.0.0,<2.0.0
pytest>=7.4.0,<8.0.0
requests>=2.31.0,<3.0.0
pandas>=2.0.0,<3.0.0
numpy>=1.24.0,<2.0.0
jinja2>=3.1.0,<4.0.0

🔐 Step 4: Environment Variables (Optional)

4.1 Set up OpenAI API (Production Mode)

If you want to use real OpenAI API instead of mock responses:

Go to your Space settings on Hugging Face
Navigate to "Repository secrets"
Add new secret:
- Name: OPENAI_API_KEY
- Value: Your OpenAI API key
- Visibility: Keep it private

4.2 Update Code for Production

Modify utils.py to use real API when available:

import os
from openai import OpenAI

# Check if running in production with API key
if os.getenv("OPENAI_API_KEY"):
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    USE_REAL_API = True
else:
    USE_REAL_API = False

def simulate_llm_call(prompt: str, context: Dict[str, Any] = None) -> str:
    if USE_REAL_API:
        # Use real OpenAI API
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=1500,
            temperature=0.7
        )
        return response.choices[0].message.content
    else:
        # Use mock responses for demo
        return generate_mock_response(prompt, context)

📤 Step 5: Deploy to Hugging Face

5.1 Commit and Push Files

# Add all files to git
git add .

# Commit with descriptive message
git commit -m "Initial deployment: STLC-AI GenAI Test Automation Demo

- Complete Gradio application with interactive UI
- 12 sample insurance domain user stories
- AI-powered BDD generation and test script creation
- Realistic test execution simulation
- Intelligent defect analysis and reporting
- Export functionality for test results
- Comprehensive documentation and examples"

# Push to Hugging Face
git push origin main

5.2 Monitor Deployment

Go to your Space URL: https://huggingface.co/spaces/YOUR_USERNAME/stlc-ai-demo
Watch the "Building" status in the top banner
Check the "Logs" tab for any build issues
Wait for "Running" status (usually 2-5 minutes)

5.3 Troubleshoot Common Issues

Build Failed - Dependencies

# Check logs for specific package conflicts
# Update requirements.txt with compatible versions
# Common fixes:
gradio==4.8.0  # Pin specific version
numpy<2.0.0    # Avoid v2.0 compatibility issues

Build Failed - File Not Found

# Ensure all files are committed
git status
git add missing_file.py
git commit -m "Add missing file"
git push origin main

Runtime Error - Import Issues

# Check app.py imports
# Ensure all modules are in requirements.txt
# Verify file paths are relative

✅ Step 6: Post-Deployment Verification

6.1 Test Core Functionality

Open your Space URL
Select a user story from the dropdown
Click "Start Test Lifecycle"
Verify each stage processes correctly:
- ✅ BDD scenario generation
- ✅ Test script creation
- ✅ Execution simulation
- ✅ Defect summary (for failures)
Test export functionality

6.2 Performance Check

Response time: Should be <10 seconds per stage
UI responsiveness: All components should load quickly
Mobile compatibility: Test on mobile device
Error handling: Try invalid inputs

6.3 Update Space Description

Go to your Space settings
Update the description:

🤖 AI-powered Software Test Life Cycle automation demo for insurance systems. 

Features:
• Convert user stories to BDD scenarios
• Generate Python test scripts automatically  
• Simulate test execution with realistic results
• Provide intelligent defect analysis
• Export comprehensive test documentation

Perfect demonstration of GenAI in QA automation!

📊 Step 7: Space Optimization

7.1 Performance Optimization

# In app.py, optimize for Spaces environment
demo = gr.Blocks(
    theme=gr.themes.Soft(),
    title="STLC-AI: GenAI Test Automation",
    css="Custom CSS here...",
    analytics_enabled=False,  # Disable for faster loading
    show_error=True          # Show helpful errors
)

# Optimize launch parameters
if __name__ == "__main__":
    demo.launch(
        share=True,
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True,
        quiet=False
    )

7.2 Add Analytics (Optional)

# Track usage with simple analytics
import time
from datetime import datetime

def log_usage(action, user_story_id=None):
    timestamp = datetime.now().isoformat()
    print(f"[ANALYTICS] {timestamp}: {action} - Story: {user_story_id}")

7.3 SEO and Discoverability

Update your Space's metadata:

Add relevant tags: artificial-intelligence, testing, automation
Create engaging thumbnail: Upload a custom image
Write compelling description: Highlight key benefits
Pin to profile: Make it easily discoverable

🎉 Step 8: Share Your Demo

8.1 Test the Public URL

Share with colleagues: https://huggingface.co/spaces/YOUR_USERNAME/stlc-ai-demo
Test from different devices and networks
Verify all functionality works in production

8.2 Create Marketing Materials

## 🚀 Just Launched: STLC-AI Demo!

Experience the future of QA automation with AI:
• Upload insurance user stories
• Watch AI generate BDD scenarios
• Get complete Python test scripts
• See intelligent defect analysis

Try it live: [Your Space URL]

8.3 Gather Feedback

Add feedback mechanism in your app
Monitor Space analytics and usage
Iterate based on user feedback

🔧 Maintenance & Updates

Update Process

# Pull latest changes
git pull origin main

# Make your updates
# ... edit files ...

# Test locally
python app.py

# Deploy updates
git add .
git commit -m "Update: [description of changes]"
git push origin main

Monitoring

Check Space status regularly
Monitor build logs for warnings
Track usage analytics
Update dependencies periodically

🆘 Troubleshooting Guide

Common Issues and Solutions

Space Not Loading

# Check build logs in HF interface
# Common issues:
# 1. Requirements conflicts
# 2. File path errors
# 3. Port binding issues

Slow Performance

# Optimize large data files
# Reduce model complexity
# Cache common responses
# Upgrade to better hardware tier

API Quotas Exceeded

# Implement rate limiting
# Add error handling for API failures
# Fall back to mock responses

🎯 Success Checklist

Space builds successfully
All UI components load properly
User story selection works
BDD generation displays correctly
Test script generation works
Execution simulation runs
Defect summaries appear for failures
Export functionality works
Mobile responsiveness verified
Error handling tested
Performance acceptable (<10s response)
Documentation complete
Space description updated
Tags and metadata configured

🎉 Congratulations! Your STLC-AI demo is now live and ready to showcase the power of GenAI in test automation!