Add initial implementation of Deal Intel project

This commit introduces the foundational structure for the Deal Intel project, including:
- Environment configuration file (.env.example) for managing secrets and API keys.
- Scripts for building a ChromaDB vector store (build_vector_store.py) and training machine learning models (train_rf.py, train_ensemble.py).
- Health check functionality (health_check.py) to ensure system readiness.
- A launcher script (launcher.py) for executing various commands, including UI launch and health checks.
- Logging utilities (logging_utils.py) for consistent logging across the application.
- A README file providing an overview and setup instructions for the project.

These additions establish a comprehensive framework for an agentic deal-hunting AI system, integrating various components for data processing, model training, and user interaction.

week8/community_contributions/hopeogbons/Deal Intel/README.md

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+# Deal Intel — Agentic Deal-Hunting AI
+
+## Overview
+An end-to-end agentic system that scans product sources, estimates fair value using a hybrid LLM+RAG+ML stack, ranks best opportunities, and alerts you via push/SMS. Includes a Gradio UI and vector-space visualization.
+
+## Prerequisites
+- Environment and secrets:
+  - `HF_TOKEN`, `MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET`
+  - Either `OPENAI_API_KEY` or `DEEPSEEK_API_KEY`
+  - For push notifications: `PUSHOVER_USER`, `PUSHOVER_TOKEN`
+  - Optional Twilio SMS: `TWILIO_ACCOUNT_SID`, `TWILIO_AUTH_TOKEN`
+- Dependencies installed: `pip install -r requirements.txt`
+- Modal set up: `modal setup` (or env vars) and credits available
+
+## Steps & Acceptance Criteria
+
+1) Environment Setup
+- Install Python deps and export required secrets.
+- Acceptance: `openai`, `chromadb`, and `modal` import successfully; `modal setup` completes.
+
+2) Deploy Specialist Pricer on Modal
+- Use `pricer_service2.py` and deploy the `Pricer` class with GPU and Hugging Face cache.
+- Acceptance: `Pricer.price.remote("...")` returns a numeric price; `keep_warm.py` prints `"ok"` every cycle if used.
+
+3) Build Product Vector Store (RAG)
+- Populate `chromadb` persistent DB `products_vectorstore` with embeddings, documents, metadatas (including `price` and `category`).
+- Acceptance: Query for 5 similars returns valid `documents` and `metadatas` with prices.
+
+4) Train Classical ML Models and Save Artifacts
+- Train Random Forest on embeddings → save `random_forest_model.pkl` at repo root.
+- Train Ensemble `LinearRegression` over Specialist/Frontier/RF predictions → save `ensemble_model.pkl`.
+- Acceptance: Files exist and load in `agents/random_forest_agent.py` and `agents/ensemble_agent.py`.
+
+5) Verify Individual Agents
+- SpecialistAgent → calls Modal pricer and returns float.
+- FrontierAgent → performs RAG on `chromadb`, calls `OpenAI`/`DeepSeek`.
+- RandomForestAgent → loads `random_forest_model.pkl`, encodes descriptions with `SentenceTransformer`.
+- ScannerAgent → pulls RSS feeds and returns consistent structured outputs with clear-price deals.
+- Acceptance: Each agent returns sensible outputs without exceptions.
+
+6) Orchestration (Planning + Messaging)
+- PlanningAgent coordinates scanning → ensemble pricing → selection against `DEAL_THRESHOLD`.
+- MessagingAgent pushes alerts via Pushover; optionally Twilio SMS if enabled.
+- Acceptance: Planner produces at least one `Opportunity` and alert sends with price/estimate/discount/URL.
+
+7) Framework & Persistence
+- DealAgentFramework initializes logging, loads `chromadb`, reads/writes `memory.json`.
+- Acceptance: After a run, `memory.json` includes the new opportunity.
+
+8) UI (Gradio)
+- Use `price_is_right_final.py` for logs, embeddings 3D plot, and interactive table; `price_is_right.py` is a simpler alternative.
+- Acceptance: UI loads; “Run” updates opportunities; selecting a row triggers alert.
+
+9) Operational Readiness
+- Keep-warm optional: ping `Pricer.wake_up.remote()` to avoid cold starts.
+- Acceptance: End-to-end run latency is acceptable; reduced cold start when keep-warm is active.
+
+10) Testing
+- Run CI tests in `community_contributions/pricer_test/`.
+- Add a smoke test for `DealAgentFramework.run()` and memory persistence.
+- Acceptance: Tests pass; smoke run returns plausible prices and discounts.
+
+## Usage
+
+- Launch UI:
+  - `python "Deal Intel/launcher.py" ui`
+- Run planner one cycle:
+  - `python "Deal Intel/launcher.py" run`
+- Keep Modal warm (optional):
+  - `python "Deal Intel/launcher.py" keepwarm`
+
+## Required Artifacts
+- `random_forest_model.pkl` — required by `agents/random_forest_agent.py`
+- `ensemble_model.pkl` — required by `agents/ensemble_agent.py`