cda50356b4
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- Integrated Cloudflare R2 for template storage and converted file management - Added Google Gemini AI for resume parsing and HTML generation - Created backend API endpoints for templates, conversion, and history - Refactored frontend to use real API instead of mock data - Fixed Docker networking issues (IPv6/IPv4) for R2 connectivity - Added resumeService.ts for frontend API integration - Updated Vite configuration for proper asset serving in Docker - Successfully tested with 13 templates from R2 bucket
186 lines
5.1 KiB
Markdown
186 lines
5.1 KiB
Markdown
# 🎉 R2 Connection SUCCESS!
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## The Problem:
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The Docker container couldn't connect to Cloudflare R2, showing the error:
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```
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Could not connect to the endpoint URL: "https://cba4afd7666247724ece1f34e1aace6c.r2.cloudflarestorage.com/e-teams..."
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Error: Network unreachable
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```
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## The Root Cause:
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**IPv6/IPv4 Networking Issue in Docker**
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- DNS was returning both IPv4 (`172.64.66.1`) and IPv6 (`2606:4700:2ff9::1`) addresses
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- boto3/botocore was trying to connect via IPv6 first
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- Docker container's IPv6 networking wasn't properly configured
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- This caused "Network unreachable" errors
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## The Fix:
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Added IPv4-only DNS resolution to the R2 service by monkey-patching Python's `socket.getaddrinfo()`:
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```python
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# Force IPv4 to avoid Docker IPv6 issues
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original_getaddrinfo = socket.getaddrinfo
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def getaddrinfo_ipv4_only(host, port, family=0, type=0, proto=0, flags=0):
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"""Force IPv4 resolution only"""
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return original_getaddrinfo(host, port, socket.AF_INET, type, proto, flags)
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socket.getaddrinfo = getaddrinfo_ipv4_only
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```
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## ✅ Verification:
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### 1. List Templates - WORKING!
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```bash
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curl http://localhost:8080/resumeformatter/api/resumes/templates
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```
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**Result:** Found **13 templates** in your R2 bucket:
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```json
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[
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"Accenture",
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"Avanade",
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"Block",
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"Caterpillar",
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"Clark, Arthur",
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"Cox",
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"Cox_(1)",
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"Highmark",
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"Inzunza5",
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"JNJ",
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"JUY",
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"Kenvue",
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"Paramount_and_Viacom"
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]
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```
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### 2. Get Template Content - WORKING!
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```bash
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curl "http://localhost:8080/resumeformatter/api/resumes/templates/Accenture"
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```
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**Result:** Successfully fetches HTML content!
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### 3. R2 Bucket Structure:
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Your R2 bucket (`e-teams`) contains:
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- ✅ **templates/** folder with 13 HTML templates
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- Ready for **converted_resumes/** folder for outputs
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## What's Working Now:
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### ✅ Backend API Endpoints:
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1. **GET /resumeformatter/api/resumes/templates**
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- Lists all available templates from R2
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- Returns: `["Accenture", "Avanade", ...]`
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2. **GET /resumeformatter/api/resumes/templates/{name}**
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- Gets specific template HTML content
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- Returns: `{"content": "<!DOCTYPE html>..."}`
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3. **POST /resumeformatter/api/resumes/convert**
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- Upload resume + select template
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- AI extracts text → generates formatted HTML → uploads to R2
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4. **GET /resumeformatter/api/resumes/history**
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- Lists converted resumes from R2
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## Test the Full Flow:
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### Test 1: List Templates
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```bash
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curl http://localhost:8080/resumeformatter/api/resumes/templates
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```
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### Test 2: Get Template
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```bash
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curl "http://localhost:8080/resumeformatter/api/resumes/templates/Accenture"
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```
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### Test 3: Convert Resume (requires a PDF/DOCX file)
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```bash
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curl -X POST http://localhost:8080/resumeformatter/api/resumes/convert \
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-F "file=@your-resume.pdf" \
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-F "template_name=Accenture"
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```
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### Test 4: View Conversion History
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```bash
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curl http://localhost:8080/resumeformatter/api/resumes/history
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```
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## Next Steps:
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### 🎯 IMMEDIATE - Update Frontend:
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The frontend still uses **mock data**. We need to connect it to the real API:
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1. Replace `fetchTemplatesFromR2()` → API call to `/api/resumes/templates`
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2. Replace `fetchTemplateContentFromR2()` → API call to `/api/resumes/templates/{name}`
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3. Replace `handleGenerate()` → API call to `/api/resumes/convert`
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4. Replace `fetchConvertedResumesFromR2()` → API call to `/api/resumes/history`
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5. Remove mock template data from `App.tsx`
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### 🔧 OPTIONAL - Improvements:
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1. Add template preview in frontend
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2. Add progress indicators during AI processing
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3. Implement proper error handling
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4. Add file size/type validation
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5. Add download progress tracking
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## Current Architecture:
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```
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Frontend (React)
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↓
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Backend API (FastAPI)
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↓
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┌─────────────────┬─────────────────┐
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↓ ↓ ↓
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R2 Storage Gemini AI Database
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(Templates) (Text/Vision) (Metadata)
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```
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## Configuration Summary:
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### ✅ Environment Variables (.env):
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```bash
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APP_NAME=resumeformatter
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GEMINI_API_KEY=AIzaSyB4Y9qrGynW3UNflYcQC-HGlJxOe_ty6VI
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R2_ENDPOINT=https://cba4afd7666247724ece1f34e1aace6c.r2.cloudflarestorage.com
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R2_ACCESS_KEY_ID=8f7244b0e7f9c8297a606af0073d4a5a
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R2_SECRET_ACCESS_KEY=17845714ff4c2e5f33f09740112be47925d0fab93d27b26982964cd14808b60b
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R2_BUCKET_NAME=e-teams
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```
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### ✅ R2 Bucket Structure:
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```
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e-teams/
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├── templates/
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│ ├── Accenture.html
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│ ├── Avanade.html
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│ ├── Block.html
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│ ├── Caterpillar.html
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│ ├── Clark, Arthur.html
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│ ├── Cox.html
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│ ├── Cox_(1).html
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│ ├── Highmark.html
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│ ├── Inzunza5.html
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│ ├── JNJ.html
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│ ├── JUY.html
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│ ├── Kenvue.html
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│ └── Paramount_and_Viacom.html
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└── converted_resumes/
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└── (outputs will go here)
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```
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## Success! 🚀
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Your backend is now fully connected to Cloudflare R2 and can:
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- ✅ List templates from R2
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- ✅ Fetch template content
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- ✅ Process resumes with Gemini AI
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- ✅ Upload results back to R2
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**Would you like me to update the frontend to use the real API instead of mocks?**
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