Document runtime error fix and remediation

Added Runtime Error Diagnostic & Remediation section documenting the ModuleNotFoundError for transformers library, root cause analysis, and the fix applied to requirements.txt. Includes best practices for dependency management going forward.

README.md

@@ -7,42 +7,83 @@ sdk: gradio
 pinned: false
 short_description: A simple Gradio app
 ---
+
 # StrategyStack Recovery, Enrichment & Outreach Platform
+
 **Modular pipeline for automated asset enrichment, engagement, analytics, and agent/handoff orchestration—including API, UI, test logic, upgrade/migration, and troubleshooting.**
 
 ---
+
+## Runtime Error Diagnostic & Remediation (Nov 2025)
+
+### Problem Identified
+The Space experienced a `ModuleNotFoundError: No module named 'transformers'` runtime error at startup. The error traceback showed:
+```
+File "/app/app.py", line 2, in <module>
+    from lead_scoring_agent import LeadScoringAgent
+File "/app/lead_scoring_agent.py", line 13, in <module>
+    from transformers import pipeline
+ModuleNotFoundError: No module named 'transformers'
+```
+
+### Root Cause
+The `lead_scoring_agent.py` module imports `from transformers import pipeline` (line 13), which is required for HuggingFace model-based lead scoring functionality. However, the `transformers` library dependency was commented out in `requirements.txt` (line 16: `# transformers>=4.35.0  # Uncomment if using HuggingFace models`).
+
+### Remediation
+**Action taken:** Uncommented the `transformers>=4.35.0` dependency in `requirements.txt` (commit 2812a465d20a511d453648b52e546bbba1d87532).
+
+**Explanation:** The `transformers` library is essential for the lead scoring agent's NLP-based analysis capabilities. With the dependency now active, the Space container can install the required package during build, resolving the import error.
+
+**Verification:** After the fix, the Space should rebuild automatically and transition from "Runtime error" to "Running" state. Monitor build logs to confirm successful installation of transformers and dependencies.
+
+### Best Practices Going Forward
+- **Dependency Audit:** When adding new agent modules that import external libraries, ensure all dependencies are declared in `requirements.txt`.
+- **Import Testing:** Run local imports or CI checks before deploying to catch missing dependencies early.
+- **Documentation Alignment:** Keep inline comments in `requirements.txt` synchronized with actual code imports to prevent confusion.
+
+---
+
 ## Pipeline Workflow
+
 1. **Input/Asset Ingestion** (`activity_ingestion.py`):
    - Receives assets or lead lists (CSV, API, UI submission).
    - Validates schema (see `schema_registry.py`).
    - Logs activity (`activity_log.csv`).
+
 2. **Automated Enrichment** (`recovery_enrichment_agent.py`):
    - Fills gaps: persona, recent posts, complaints, engagement metrics.
    - Invokes external APIs as needed or generates synthetic context.
    - Handoff to validation agent if enrichment uncertain.
+
 3. **Lead Scoring** (`lead_scoring_agent.py`) - NEW:
    - Scores lead profiles based on firmographic, behavioral, and engagement signals.
    - Enriches profiles with scoring metadata and rationale.
    - Integrated into validation workflow at action start for each lead review.
+
 4. **Human/Machine Validation** (`human_validation_agent.py`):
    - Receives enriched assets where automated logic lacks confidence.
    - **NEW:** Scores each lead profile at action start using `lead_scoring_agent`.
    - **NEW:** Passes scoring results and enrichment data to downstream handlers.
    - Interactive review and correction (UI or API endpoint).
    - Returns enriched/validated/scored assets to pipeline.
+
 5. **Outreach/Engagement** (`outreach_agent.py`):
    - Selects template, personalizes using context fields and scoring signals.
    - Delivers via pluggable channels: email, LinkedIn, SMS, browser automation.
    - Supports automated social actions and feedback.
+
 6. **Analytics & Reporting** (`analytics_agent.py`):
    - Logs all asset/channel outcomes, engagement stats, and scoring performance.
    - Computes dashboards, actionable feedback, and improvement triggers.
+
 7. **Research/Search Integration** (`research_agent.py`, `query_search_agent.py`):
    - Supplements enrichment with web or corpus search, updates context/data.
    - Used by outreach for deep personalization or compliance needs.
 
 ---
+
 ## API Endpoints
+
 - `/ingest` (**POST**): Upload asset batch (CSV/JSON), triggers validation & enrichment.
 - `/validate` (**POST**): Human/machine validation of asset content with lead scoring.
 - `/score` (**POST**) - NEW: Score lead profiles using lead_scoring_agent; returns scoring metadata.
@@ -52,7 +93,9 @@ short_description: A simple Gradio app
 - **Authentication/Authorization**: All endpoints respect granular role controls, see config in `docs/configs/`.
 
 ---
+
 ## UI Dashboard Features
+
 - Asset ingestion/upload table with schema/state indicators and scoring status.
 - Visualization of validation/enrichment status, gaps, overrides (human/manual call-outs), and lead scores.
 - Outreach composer: preview, edit, and dispatch personalized messages across channels using scoring signals.
@@ -60,7 +103,9 @@ short_description: A simple Gradio app
 - Agent handoff log: view/trace enrichment, validation, scoring, and outreach chain per asset.
 
 ---
+
 ## Lead Scoring Integration (NEW)
+
 ### Overview
 The `lead_scoring_agent` module scores lead profiles based on multiple signals and enriches them with contextual metadata. The `human_validation_agent` integrates lead scoring at the point of action start—each lead review now includes:
 - Score result (numeric or categorical)
@@ -111,21 +156,27 @@ print(results)  # All results include scoring metadata
 ```
 
 ---
+
 ## Test Case Logic
+
 - Test scripts: `test_schema_tools.py`, `test_validation.py` (unit tests for all schema/enrichment/validation logic).
 - **NEW**: Test lead scoring integration in `test_validation.py`; verify scoring metadata in reviews.
 - CI-friendly: All test scripts runnable standalone or via `pytest`, support fixtures for human/machine edge cases.
 - Example cases cover incomplete asset, fuzzy enrichment, validation override, lead scoring, pipeline/integration chain.
 
 ---
+
 ## Agent & Asset Wiring
+
 - Each module is pluggable: you can sub in new enrichment, validation, scoring, or channel handler without altering others.
 - **NEW**: Lead scoring agent decoupled from validation—can be swapped or augmented independently.
 - Agent handoff: all context (original, intermediate states, **scoring metadata**) passes via in-memory or file-based serialization; reference logic in `enrichment_handoff_simulation.py` (`docs/`).
 - Asset state maintained through pipeline, logging all hops including scoring. Schemas upgradable (see `SCHEMA_VERSIONING.md`).
 
 ---
+
 ## Upgrade & Migration Guide
+
 - **Schema Versioning**: Asset, enrichment, and analytics schema tracked with incremental migrations (`SCHEMA_VERSIONING.md`).
   - **NEW**: `LeadReview` schema v2 includes `scoring_result` and `enrichment_data` fields (backward compatible).
 - **Lead Scoring Integration**: Ensure `lead_scoring_agent.py` is available in the same namespace; import validated on `HumanValidationAgent` instantiation.
@@ -133,7 +184,9 @@ print(results)  # All results include scoring metadata
 - Migration scripts: Provided for legacy log/asset conversion.
 
 ---
+
 ## Best Practices: Setup, Usage, Troubleshooting
+
 - **Setup**: `pip install -r requirements.txt`; run `app.py` to launch UI and API.
 - **Usage**: Ingest assets via UI or API, follow pipeline in dashboard, review and score leads, manually validate flagged inputs.
 - **Troubleshooting**:
@@ -145,7 +198,9 @@ print(results)  # All results include scoring metadata
   - Update configs/schemas via `docs/configs/` for custom needs.
 
 ---
+
 ## Module, Config, Script, and Logic Reference
+
 - **Core Modules**: 
   - `activity_ingestion.py`, `outreach_agent.py`, `recovery_enrichment_agent.py`, `analytics_agent.py`, `research_agent.py`, `human_validation_agent.py`, `query_search_agent.py`
   - **NEW**: `lead_scoring_agent.py` (lead profile scoring and enrichment)
@@ -163,4 +218,5 @@ print(results)  # All results include scoring metadata
   - **NEW**: Scoring metadata passed through handoff chain
 
 ---
+
 For further explanation, workflows, and custom case studies—see full documentation and module-level docstrings.