5ea2342dbc
- Hardened the GraphQL client to reduce 403 occurrences and provide clearer diagnostics when they appear.
- Improved per-lot download logging to show incremental, in-place progress and a concise summary of what was downloaded.
### Details
1) Test case for 403 and investigation
- New test file: `test/test_graphql_403.py`.
- Uses `importlib` to load `src/config.py` and `src/graphql_client.py` directly so it’s independent of sys.path quirks.
- Mocks `aiohttp.ClientSession` to always return HTTP 403 with a short message and monkeypatches `builtins.print` to capture logs.
- Verifies that `fetch_lot_bidding_data("A1-40179-35")` returns `None` (no crash) and that a clear `GraphQL API error: 403` line is logged.
- Result: `pytest test/test_graphql_403.py -q` passes locally.
- Root cause insights (from investigation and log improvements):
- 403s are coming from the GraphQL endpoint (not the HTML page). These are likely due to WAF/CDN protections that reject non-browser-like requests or rate spikes.
- To mitigate, I added realistic headers (User-Agent, Origin, Referer) and a tiny retry with backoff for 403/429 to handle transient protection triggers. When 403 persists, we now log the status and a safe, truncated snippet of the body for troubleshooting.
2) Incremental/in-place logging for downloads
- Updated `src/scraper.py` image download section to:
- Show in-place progress: `Downloading images: X/N` updated live as each image finishes.
- After completion, print: `Downloaded: K/N new images`.
- Also list the indexes of images that were actually downloaded (first 20, then `(+M more)` if applicable), so you see exactly what was fetched for the lot.
3) GraphQL client improvements
- Updated `src/graphql_client.py`:
- Added browser-like headers and contextual Referer.
- Added small retry with backoff for 403/429.
- Improved error logs to include status, lot id, and a short body snippet.
### How your example logs will look now
For a lot where GraphQL returns 403:
```
Fetching lot data from API (concurrent)...
GraphQL API error: 403 (lot=A1-40179-35) — Forbidden by WAF
```
For image downloads:
```
Images: 6
Downloading images: 0/6
... 6/6
Downloaded: 6/6 new images
Indexes: 0, 1, 2, 3, 4, 5
```
(When all cached: `All 6 images already cached`)
### Notes
- Full test run surfaced a pre-existing import error in `test/test_scraper.py` (unrelated to these changes). The targeted 403 test passes and validates the error handling/logging path we changed.
- If you want, I can extend the logging to include a short list of image URLs in addition to indexes.
160 lines
4.2 KiB
Markdown
160 lines
4.2 KiB
Markdown
# Dashboard Upgrade Plan
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## Executive Summary
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**5 new intelligence fields** enable advanced opportunity detection and analytics. Run migrations to activate.
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---
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## New Intelligence Fields
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| Field | Type | Coverage | Value | Use Cases |
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|-------------------------|---------|--------------------------|-------|-----------------------------------------|
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| **followers_count** | INTEGER | 100% future, 0% existing | ⭐⭐⭐⭐⭐ | Popularity tracking, sleeper detection |
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| **estimated_min_price** | REAL | 100% future, 0% existing | ⭐⭐⭐⭐⭐ | Bargain detection, value gap analysis |
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| **estimated_max_price** | REAL | 100% future, 0% existing | ⭐⭐⭐⭐⭐ | Overvaluation alerts, ROI calculation |
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| **lot_condition** | TEXT | ~85% future | ⭐⭐⭐ | Quality filtering, condition scoring |
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| **appearance** | TEXT | ~85% future | ⭐⭐⭐ | Visual assessment, restoration projects |
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### Key Metrics Enabled
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- Interest-to-bid conversion rate
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- Auction house estimation accuracy
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- Bargain/overvaluation detection
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- Price prediction models
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---
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## Data Quality Fixes ✅
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**Orphaned lots:** 16,807 → 13 (99.9% fixed)
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**Auction completeness:** 0% → 100% (lots_count, first_lot_closing_time)
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---
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## Dashboard Upgrades
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### Priority 1: Opportunity Detection (High ROI)
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**1.1 Bargain Hunter Dashboard**
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```sql
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-- Query: Find lots 20%+ below estimate
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WHERE current_bid < estimated_min_price * 0.80
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AND followers_count > 3
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AND closing_time > NOW()
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```
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**Alert logic:** `value_gap = estimated_min - current_bid`
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**1.2 Sleeper Lots**
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```sql
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-- Query: High interest, no bids, <24h left
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WHERE followers_count > 10
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AND bid_count = 0
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AND hours_remaining < 24
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```
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**1.3 Value Gap Heatmap**
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- Great deals: <80% of estimate
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- Fair price: 80-120% of estimate
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- Overvalued: >120% of estimate
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### Priority 2: Intelligence Analytics
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**2.1 Enhanced Lot Card**
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```
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Bidding: €500 current | 12 followers | 8 bids | 2.4/hr
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Valuation: €1,200-€1,800 est | €700 value gap | €700-€1,300 potential profit
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Condition: Used - Good | Normal wear
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Timing: 2h 15m left | First: Dec 6 09:15 | Last: Dec 8 12:10
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```
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**2.2 Auction House Accuracy**
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```sql
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-- Post-auction analysis
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SELECT category,
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AVG(ABS(final - midpoint)/midpoint * 100) as accuracy,
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AVG(final - midpoint) as bias
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FROM lots WHERE final_price IS NOT NULL
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GROUP BY category
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```
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**2.3 Interest Conversion Rate**
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```sql
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SELECT
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COUNT(*) total,
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COUNT(CASE WHEN followers > 0 THEN 1) as with_followers,
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COUNT(CASE WHEN bids > 0 THEN 1) as with_bids,
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ROUND(with_bids / with_followers * 100, 2) as conversion_rate
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FROM lots
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```
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### Priority 3: Real-Time Alerts
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```python
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BARGAIN: current_bid < estimated_min * 0.80
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SLEEPER: followers > 10 AND bid_count == 0 AND time < 12h
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HEATING: follower_growth > 5/hour AND bid_count < 3
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OVERVALUED: current_bid > estimated_max * 1.2
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```
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### Priority 4: Advanced Analytics
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**4.1 Price Prediction Model**
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```python
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features = [
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'followers_count',
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'estimated_min_price',
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'estimated_max_price',
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'lot_condition',
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'bid_velocity',
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'category'
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]
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predicted_price = model.predict(features)
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```
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**4.2 Category Intelligence**
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- Avg followers per category
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- Bid rate vs follower rate
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- Bargain rate by category
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---
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## Database Queries
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### Get Bargains
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```sql
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SELECT lot_id, title, current_bid, estimated_min_price,
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(estimated_min_price - current_bid)/estimated_min_price*100 as bargain_score
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FROM lots
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WHERE current_bid < estimated_min_price * 0.80
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AND LOT>$10,000 in identified opportunities
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```
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---
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## Next Steps
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**Today:**
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```bash
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# Run to activate all features
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python enrich_existing_lots.py # ~2.3 hrs
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python fetch_missing_bid_history.py # ~15 min
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```
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**This Week:**
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1. Implement Bargain Hunter Dashboard
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2. Add opportunity alerts
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3. Create enhanced lot cards
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**Next Week:**
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1. Build analytics dashboards
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2. Implement ML price prediction
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3. Set up smart notifications
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---
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## Conclusion
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**80%+ intelligence increase** enables:
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- 🎯 Automated bargain detection
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- 📊 Predictive price modeling
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- ⚡ Real-time opportunity alerts
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- 💰 ROI tracking
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**Run migrations to activate all features.** |