TESTING_GUIDE.md

# 🚀 Quick Start Guide - Testing ML Features

## Installation & Setup

### 1. Install Dependencies
```bash
cd SkillSync
pip install -r requirements.txt
```

**Note:** First-time setup will download ~750MB of ML models automatically.

### 2. Run the Application
```bash
python app.py
```

The app will start on `http://localhost:7860` (or port 5000)

---

## 🧪 Testing the New Features

### Feature 1: AI Resume Scorer 🤖

1. **Login as Intern:**
   - Email: `alice.smith@example.com`
   - Password: `password`

2. **Navigate:** Dashboard → "🤖 AI Resume Scorer"

3. **Test Cases:**
   - **Without Job Description:** Get baseline score
   - **With Job Description:** Paste a job posting to get targeted score

**Expected Output:**
- Total score (0-100%)
- Grade (A+ to D)
- Breakdown: Completeness, Skills Depth, Experience Quality, Job Match
- Recommendations for improvement
- Skills count (technical & soft)

---

### Feature 2: Success Predictor 🎯

1. **From Dashboard:** Find any internship card

2. **Click:** "🎯 Predict Success" button

3. **Review:**
   - Success probability (0-100%)
   - Prediction (Likely/Unlikely)
   - Confidence level
   - Personalized recommendations

**Test Different Scenarios:**
- High match (>75% similarity) → High success probability
- Medium match (50-75%) → Medium success probability
- Low match (<50%) → Low success probability

---

### Feature 3: Learning Path Generator 🎓

1. **Navigate:** Dashboard → "🎓 Learning Path"

2. **Test Two Ways:**

   **Option A: Target Role**
   ```
   Target Role: "Full Stack Developer"
   Submit
   ```

   **Option B: Target Internship**
   ```
   Select an internship from dropdown
   Submit
   ```

**Expected Output:**
- Missing skills categorized (Beginner/Intermediate/Advanced)
- Course recommendations with links to:
  - Coursera
  - Udemy
  - YouTube
  - Official Docs
- Estimated time per skill
- Learning tips

---

### Feature 4: AI Career Chatbot 💬

1. **Navigate:** Dashboard → "💬 AI Career Chat"

2. **Test Questions:**
   ```
   "How do I write a good resume?"
   "How do I prepare for interviews?"
   "What skills should I learn?"
   "How do I negotiate salary?"
   "How do I plan my career?"
   ```

3. **Or Use Quick Buttons:** Click any pre-defined question

**Expected Output:**
- Detailed, contextual responses
- Bullet points with actionable advice
- Links to platform features

---

### Feature 5: Enhanced ATS Insights 📈

1. **Navigate:** Dashboard → "ATS Insights"

2. **Paste Job Description:**
   ```
   We are looking for a Python developer with experience in 
   machine learning, data analysis, and cloud computing. 
   Candidates should have strong problem-solving skills and 
   experience with TensorFlow or PyTorch.
   ```

3. **Submit**

**Expected Output:**
- Keyword Match Score (traditional)
- Semantic Match Score (NEW - AI-powered)
- Missing keywords
- Improvement tips

**Compare:**
- Without ML: Only keyword matching
- With ML: Deep semantic understanding

---

### Feature 6: Enhanced Mock Interview 🎤

1. **Navigate:** Dashboard → "Mock Interview"

2. **Select Question:** "Describe a challenge you faced"

3. **Provide Response:**
   ```
   In my previous internship, I faced a situation where our 
   database queries were extremely slow. My task was to optimize 
   them. I analyzed the query patterns, added proper indexes, 
   and implemented caching. As a result, we reduced query time 
   by 60% and improved application performance significantly.
   ```

4. **Submit**

**Expected Output:**
- Score (0-100%)
- Grade
- Detailed metrics:
  - Word count
  - Readability score
  - Sentiment analysis
  - STAR method detection
  - Technical terms found
- Specific feedback

---

### Feature 7: Semantic Matching in Dashboard 🎯

**Automatic Feature** - No separate testing needed

**How to Observe:**

1. **Login as different interns:**
   - Alice (Python, ML) → Matches ML internships
   - Bob (JavaScript, React) → Matches frontend internships

2. **Compare Scores:**
   - Old: Exact keyword match only
   - New: Semantic understanding

**Example:**
```
User has: "machine learning experience"
Job requires: "AI and neural networks"

Old Match: 0% (no common keywords)
New Match: 85% (semantically similar)
```

---

## 🧪 Advanced Testing

### Test ML Model Loading

```bash
# Check logs
cat /tmp/logs/app.log | grep "ML"
```

**Expected:**
```
[INFO] Advanced ML features loaded successfully
[INFO] Semantic model loaded successfully
[INFO] Sentiment analyzer loaded successfully
[INFO] NER model loaded successfully
```

### Test Fallback Mode

1. **Temporarily break ML:**
   - Rename `ml_utils.py` to `ml_utils_backup.py`
   - Restart app

2. **Verify:**
   - Dashboard still works
   - Basic matching still functions
   - Warning in logs: "ML features not available"

3. **Restore:**
   - Rename back to `ml_utils.py`
   - Restart app

---

## 📊 Performance Testing

### Model Loading Time
```python
import time
from ml_utils import get_semantic_model

start = time.time()
model = get_semantic_model()
print(f"Load time: {time.time() - start:.2f}s")
```

**Expected:** 2-5 seconds on first load, <0.1s on subsequent calls

### Semantic Similarity Speed
```python
from ml_utils import semantic_similarity

text1 = "Python programming and machine learning"
text2 = "Software development with AI"

start = time.time()
score = semantic_similarity(text1, text2)
print(f"Similarity: {score:.3f}, Time: {(time.time() - start)*1000:.1f}ms")
```

**Expected:** <100ms per comparison

---

## 🐛 Troubleshooting

### Issue: Models not downloading

**Solution:**
```bash
export TRANSFORMERS_CACHE=/tmp/hf_cache
mkdir -p /tmp/hf_cache
```

### Issue: Out of memory

**Symptoms:** App crashes when loading models

**Solution:**
- Ensure at least 2GB free RAM
- Close other applications
- Use CPU instead of GPU (already configured)

### Issue: Slow performance

**Check:**
```bash
# Verify models are cached
ls -lh /tmp/hf_cache
```

**Should see:** ~750MB of cached models

### Issue: "ML features not available"

**Debug:**
```python
python -c "from ml_utils import ML_FEATURES_ENABLED; print(ML_FEATURES_ENABLED)"
```

**If False:**
- Check `pip install -r requirements.txt` completed
- Check logs for import errors
- Verify all dependencies installed

---

## 🎯 Success Criteria

### ✅ All Features Working When:

1. **Dashboard loads** with ML-powered buttons visible
2. **AI Resume Scorer** returns scores with recommendations
3. **Success Predictor** shows probability for each internship
4. **Learning Path** generates personalized courses
5. **AI Chatbot** provides contextual responses
6. **ATS Insights** shows both keyword and semantic scores
7. **Mock Interview** provides detailed NLP analysis
8. **Semantic matching** shows improved similarity scores

---

## 📝 Test User Accounts

### Interns
```
Alice Smith
Email: alice.smith@example.com
Password: password
Skills: Python, Java, SQL, TensorFlow
```

```
Bob Johnson
Email: bob.johnson@example.com  
Password: password
Skills: JavaScript, React, Node.js
```

### Recruiters
```
Emma Wilson (TechCorp)
Email: emma.wilson@techcorp.com
Password: password
```

### Admin
```
Admin User
Email: admin@example.com
Password: password
```

---

## 🚀 Next Steps

After testing all features:

1. **Customize ML Models:**
   - Train on your own data
   - Fine-tune for your domain

2. **Add More Features:**
   - Speech-to-text interview
   - Video interview analysis
   - Collaborative filtering

3. **Scale:**
   - Use GPU for faster inference
   - Cache embeddings in database
   - Load balance multiple instances

4. **Monitor:**
   - Track model performance
   - Log user interactions
   - A/B test ML vs traditional

---

## 📧 Support

If you encounter issues:
1. Check `/tmp/logs/app.log`
2. Verify requirements.txt installed
3. Ensure internet connection for model downloads
4. Check disk space (~1GB free needed)

---

**Happy Testing! 🎉**

All ML features are production-ready and thoroughly tested.