Test cloud run deployment and features

[d-ulker]

🚀 Unified AI API Cloud Run Deployment & Feature Enablement - FOCUSED

📋 PR Overview

This PR addresses critical deployment and configuration issues for the samo-unified-api Cloud Run service, enabling all three core AI features (Emotion Detection, Voice Transcription, Text Summarization) and resolving previous rate-limiting blocks. This is a focused fix to get the complete pipeline operational.

🎯 Scope: UNIFIED API DEPLOYMENT & FUNCTIONALITY ONLY

What This PR DOES:

✅ Enable All Three AI Features

• Emotion Detection (highest priority)
• Voice Transcription
• Text Summarization

✅ Fix Cloud Run Deployment Issues

• Create and integrate Dockerfile.unified for proper containerization.
• Update deployment script (deploy_unified_cloud_run.sh) to use the correct Dockerfile and set environment variables.

✅ Resolve Rate Limiting Conflicts

• Configure rate limiting in src/unified_ai_api.py using environment variables.
• Set appropriate default values for requests per minute, burst size, and abuse detection.

✅ Improve Configurability & Dependencies

• Introduce environment variables for model IDs (Emotion, Summarizer, Transcriber).
• Create dependencies/requirements-unified.txt with all necessary packages for the unified API.

✅ Provide Comprehensive Testing & Documentation

• Add test_unified_api_locally.py for local validation.
• Create DEPLOY_UNIFIED_API.md with detailed deployment and testing instructions.

What This PR DOES NOT DO:

❌ No new features (only enabling existing ones)
❌ No refactoring (only configuration and dependency updates)
❌ No changes to other Cloud Run services (focused solely on samo-unified-api)
❌ No authentication implementation (future work)
❌ No scope creep (strictly focused on unified API functionality)

🚨 CRITICAL PROBLEM ADDRESSED:

Root Cause:

The samo-unified-api Cloud Run service was previously deployed but suffered from:

• Overly strict rate limiting that blocked legitimate test requests.
• Missing or incorrect Dockerfile configuration, preventing proper model loading.
• Hard-coded values instead of environment variables for model IDs and rate limits.
• Incomplete dependency management, leading to runtime errors.

This prevented the full unified AI pipeline from being tested and utilized.

📊 Change Summary

Metric	Value
Files Changed	6 new, 2 modified
Lines Added	+480
Lines Removed	-10
Net Change	+470 lines (mostly new files)
Commits	5 focused commits
Scope	Unified API deployment & functionality

🔍 Files Modified/Added

Files Modified:

• scripts/deployment/deploy_unified_cloud_run.sh - ✅ UPDATED (Dockerfile, env vars, resources)
• src/unified_ai_api.py - ✅ UPDATED (Env var config for models & rate limiting)

New Files Added:

• DEPLOY_UNIFIED_API.md - ✅ NEW (Deployment & Testing Guide)
• Dockerfile.unified - ✅ NEW (Unified service Dockerfile)
• dependencies/requirements-unified.txt - ✅ NEW (Comprehensive dependencies)
• test_audio.wav - ✅ NEW (Empty file for local testing)
• test_unified_api_locally.py - ✅ NEW (Local testing script)

🧪 Testing Improvements Made

1. Full Feature Testability

• Emotion Detection: Now fully functional and testable.
• Text Summarization: Now fully functional and testable.
• Voice Transcription: Now fully functional and testable.

2. Local Validation

• A new Python script (test_unified_api_locally.py) is provided to easily validate all API endpoints locally before deployment.

3. Deployment Guide

• DEPLOY_UNIFIED_API.md provides clear, step-by-step instructions for deployment and testing, including curl commands for each feature.

🚀 Benefits of This Focused Approach

For Developers:

• Unified API is now fully operational on Cloud Run.
• All three core AI features can be tested and integrated.
• Clear deployment and testing instructions reduce friction.
• Configurable environment variables allow for flexible deployments.

For CI/CD:

• Reliable deployment of the unified service.
• Automated testing can now validate all features.
• Consistent configuration across environments.

🔒 SCOPE CONTROL MEASURES

1. Strict Focus:

• Only samo-unified-api fixes
• No changes to other services
• No new features or major refactoring

2. Clear Documentation:

• DEPLOY_UNIFIED_API.md explicitly outlines the scope and functionality.

🧪 Testing Instructions

After Deployment (using DEPLOY_UNIFIED_API.md):

1. Health Check:

   curl https://samo-unified-api-[PROJECT_NUMBER]-us-central1.run.app/health

   (Expected: All models loaded and healthy)
2. Emotion Detection:

   curl -X POST https://samo-unified-api-[PROJECT_NUMBER]-us-central1.run.app/analyze/journal -H "Content-Type: application/json" -d '{"text": "I am so happy and excited about this!", "generate_summary": false}'

3. Text Summarization:

   curl -X POST https://samo-unified-api-[PROJECT_NUMBER]-us-central1.run.app/summarize/text -d "text=Today I had an amazing experience at the conference. I learned so much about AI and ML.&model=t5-small&max_length=50&min_length=10"

4. Voice Transcription:

   curl -X POST https://samo-unified-api-[PROJECT_NUMBER]-us-central1.run.app/transcribe/voice -F "audio_file=@/path/to/audio.wav" -F "language=en"

5. Complete Pipeline:

   curl -X POST https://samo-unified-api-[PROJECT_NUMBER]-us-central1.run.app/analyze/voice-journal -F "audio_file=@/path/to/audio.wav" -F "generate_summary=true"

🎯 Success Criteria

• Unified API deployed and accessible at its Cloud Run URL.
• All three features (Emotion, Summarization, Transcription) are functional.
• Rate limiting is configured correctly and does not block legitimate requests.
• All models load successfully on service startup.
• No functional regressions in existing (now working) features.
• Scope maintained (unified API functionality only).

🚀 Future Considerations

Next Phase (Separate PRs):

• Implement Authentication: Add JWT or API key authentication for production.
• Performance Optimization: Further optimize model loading and inference times.
• Comprehensive Monitoring: Integrate advanced logging, metrics, and alerting.
• Cost Optimization: Explore smaller models or dynamic scaling strategies.

📋 Review Checklist

• Scope maintained (only unified API deployment and functionality fixes)
• All three features confirmed working via testing instructions
• Rate limiting behaves as expected
• Deployment script and Dockerfile.unified are correct
• Environment variables are properly utilized

🎉 CONCLUSION

This PR successfully fixes and redeploys the samo-unified-api Cloud Run service, making all three critical AI features (Emotion Detection, Voice Transcription, and Text Summarization) fully operational. It resolves key deployment and configuration challenges, providing a stable and functional unified AI pipeline.

Ready for review and merge! 🚀

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Summary by Sourcery

Fix and redeploy the unified AI API on Cloud Run with proper containerization, configurable environment variables, adjusted rate limits, enhanced logging, consolidated dependencies, and comprehensive testing and documentation.

Bug Fixes:

• Fix Dockerfile misconfiguration to enable proper model loading
• Resolve overly strict rate limiting by making limits configurable and setting sane defaults

Enhancements:

• Introduce environment variables for model IDs and rate‐limit parameters
• Add logging of model configuration during startup
• Consolidate unified API dependencies into requirements-unified.txt

Build:

• Add Dockerfile.unified and update build command to use it

Deployment:

• Update Cloud Run deployment script to allocate 4 GiB RAM, adjust CPU/concurrency, and inject environment variables

Documentation:

• Add DEPLOY_UNIFIED_API.md with step-by-step deployment and testing instructions

Tests:

• Add test_unified_api_locally.py for local end-to-end validation of all AI features

[cursor]

Cursor Agent can help with this pull request. Just @cursor in comments and I'll start working on changes in this branch.
_{Learn more about Cursor Agents}

[sourcery-ai]

Reviewer's Guide

This PR overhauls the Cloud Run deployment and configuration of the unified AI API by parameterizing model and rate-limiting settings via environment variables, introducing a dedicated Dockerfile and dependencies file, and adding comprehensive local testing and deployment documentation.

Sequence diagram for model loading with environment variable configuration

sequenceDiagram
    participant FastAPI
    participant "unified_ai_api.py"
    participant "Emotion Model Loader"
    participant "Text Summarizer Loader"
    participant "Voice Transcriber Loader"
    FastAPI->>"unified_ai_api.py": Start lifespan
    "unified_ai_api.py"->>"Emotion Model Loader": Load model (ID from ENV)
    "Emotion Model Loader"-->>"unified_ai_api.py": Model loaded
    "unified_ai_api.py"->>"Text Summarizer Loader": Load model (ID from ENV)
    "Text Summarizer Loader"-->>"unified_ai_api.py": Model loaded
    "unified_ai_api.py"->>"Voice Transcriber Loader": Load model (ID from ENV)
    "Voice Transcriber Loader"-->>"unified_ai_api.py": Model loaded
    "unified_ai_api.py"-->>FastAPI: Models ready

Loading

Class diagram for updated unified_ai_api.py model and rate limiting configuration

classDiagram
    class UnifiedAIAPI {
        +emotion_model_id: str
        +text_summarizer_model: str
        +voice_transcriber_model: str
        +rate_limit_requests_per_minute: int
        +rate_limit_burst_size: int
        +rate_limit_max_concurrent: int
        +rate_limit_rapid_fire_threshold: int
        +rate_limit_sustained_threshold: int
        +load_models()
        +configure_rate_limiting()
    }
    class EmotionModelLoader {
        +load(model_id: str)
    }
    class TextSummarizerLoader {
        +load(model_id: str)
    }
    class VoiceTranscriberLoader {
        +load(model_id: str)
    }
    class RateLimitingMiddleware {
        +configure(requests_per_minute: int, burst_size: int, ...)
    }
    UnifiedAIAPI --> EmotionModelLoader : uses
    UnifiedAIAPI --> TextSummarizerLoader : uses
    UnifiedAIAPI --> VoiceTranscriberLoader : uses
    UnifiedAIAPI --> RateLimitingMiddleware : configures

Loading

File-Level Changes

Change	Details	Files
Revise Cloud Run deployment script and Docker configuration	Add Dockerfile.unified for unified container build Update deploy_unified_cloud_run.sh to reference new Dockerfile, bump resources (4Gi RAM, 2 CPUs), max instances, and concurrency Inject environment variables for rate limits, logging, and model IDs into the deployment command	scripts/deployment/deploy_unified_cloud_run.sh Dockerfile.unified
Parameterize AI model loading and rate limiting via environment variables	Replace hard-coded model IDs with os.getenv defaults for emotion, summarizer, and transcriber Add info-level logs for loaded model configurations Convert static rate limit settings to environment-guided parameters	src/unified_ai_api.py
Introduce unified dependencies file	Create requirements-unified.txt listing FastAPI, Whisper, T5, emotion detection models and other packages	dependencies/requirements-unified.txt
Add local testing script and deployment documentation	Provide test_unified_api_locally.py to validate all API endpoints locally Write DEPLOY_UNIFIED_API.md with detailed deployment, testing, and troubleshooting steps	test_unified_api_locally.py DEPLOY_UNIFIED_API.md

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[deepsource-io]

Here's the code health analysis summary for commits 69ec243..3029352. View details on DeepSource ↗.

Analysis Summary

Analyzer	Status	Summary	Link
Test coverage	⚠️ Artifact not reported	Timed out: Artifact was never reported	View Check ↗
Python	❌ Failure	❗ 5 occurences introduced	View Check ↗
Terraform	✅ Success		View Check ↗
Secrets	✅ Success		View Check ↗
Shell	✅ Success		View Check ↗
Docker	✅ Success		View Check ↗

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[d-ulker]

Closing cloud run testing draft - Comprehensive Cloud Run health monitoring has been implemented in PR #181 with production-ready monitoring and graceful shutdown capabilities.