New README

README.md

@@ -1,9 +1,348 @@
----
-title: Testing-Huggingface
-emoji: 🧪
-license: mit
----
+**PROJECT OVERVIEW**
+
+
+
+**Project Name:** AI-Powered Property Recommendation via Contact Request \& CRM\*\*
+
+
+
+**Overview**
+
+
+
+This project builds an AI-driven recommendation system that responds to user property requests submitted through the \*\*Contact Us\*\* form on a real estate website.
+
+
+
+When a user submits a request specifying budget, location, property type, and preferences, the request is:
+
+
+
+1\. Stored in the **CRM** as a lead
+
+2\. Processed by the backend
+
+3\. Passed into a vector search engine
+
+4\. Used to retrieve the most relevant property listings
+
+5\. Returned to the user via the **website chat interface** as recommended property links
+
+
+
+This system improves lead response speed, relevance, and conversion by providing instant, intelligent property suggestions
+
+
+
+**MVP(Minimal Viable Product) DEFINITION**
+
+
+
+**MVP Goal**
+
+
+
+Deliver **instant property recommendations** based on user specifications from the Contact Us form, while logging the request in the CRM
+
+
+
+**MVP Scope (Phase 1)**
+
+
+
+User Flow
+
+
+
+1\. User clicks **Contact Us**
+
+2\. User submits:
+
+
+
+   \* property type
+
+   \* location preference
+
+   \* budget
+
+   \* specifications
+
+
+
+3\. Request is:
+
+   saved in CRM
+
+   sent to recommendation engine
+
+
+
+4\. User receives:
+
+   5–10 recommended property links in chat
+
+
+
+ **Components**
+
+* Contact Us form
+* Backend API to receive lead
+* CRM lead ingestion
+* Vector search–based recommendation engine
+* Chat response with property cards or links
+
+
+
+**Recommendation Logic (MVP)**
+
+* **Hard filters:** budget, listing type, location
+* **Vector similarity:** semantic matching of property descriptions
+* **Re-ranking:** closeness to budget + preference match
+
+
+
+
+
+**Out of Scope (For MVP)**
+
+* Personalized long-term user profiles
+* Payment or booking
+* Mobile app
+* Analytics dashboard
+* Agent assignment logic
+
+
+
+
+
+**MVP Success Criteria**
+
+* Recommendation response time < 2 seconds
+* At least 70% relevance in manual review
+* Works with ≥ 1,000 property listings
+* CRM successfully stores every request
+
+
+
+
+
+**ROLE ASSIGNMENT (TEAM OF 4–5)**
+
+
+
+**AI / ML Lead**
+
+Responsibilities:
+
+* Define recommendation strategy
+* Design embedding \& vector search pipeline
+* Build Colab notebooks
+* Define input/output schemas
+* Validate recommendation quality
+
+
+
+Ownership:
+
+
+
+* Vector index
+* Query logic
+* Recommendation relevance
+
+
+
+**Backend Engineer**
+
+Responsibilities:
+
+* Contact Us API endpoint
+* CRM ingestion
+* Recommendation service orchestration
+* Chat response handling
+
+
+
+Ownership:
+
+
+
+* API contracts
+* Service reliability
+
+
+
+**Frontend Engineer**
+
+Responsibilities:
+
+* Contact Us form UI
+* Chat interface UI
+* Rendering recommendation cards/links
+
+
+
+Ownership:
+
+
+
+* User experience
+* API integration
+
+
+
+
+
+**Data / Infra Engineer (Optional but valuable)**
+
+
+
+Responsibilities:
+
+
+
+* Property dataset preparation
+* Data cleaning \& normalization
+* Storage \& deployment setup
+
+
+
+Ownership:
+
+
+
+* Data quality
+* Environment stability
+
+
+
+
+
+**Leadership rule:**
+
+You own decision clarity, not all the code.
+
+
+
+
+
+**DATASET PREPARATION (YOUR FIRST TECH TASK)**
+
+
+
+**Required Property Dataset Fields**
+
+Each property listing **must** include:
+
+
+
+| Field         | Description                    |
+
+| ------------- | ------------------------------ |
+
+| property\_id   | Unique identifier              |
+
+| title         | Short property title           |
+
+| description   | Full description               |
+
+| listing\_type  | sale / rent / shortlet         |
+
+| property\_type | apartment / duplex / land      |
+
+| price         | Numeric value                  |
+
+| location      | City / area                    |
+
+| bedrooms      | Integer                        |
+
+| bathrooms     | Integer                        |
+
+| amenities     | List (parking, security, etc.) |
+
+| url           | Public listing link            |
+
+| status        | available / unavailable        |
+
+
+
+
+
+**Fields Used for Vector Embeddings**
+
+
+
+These fields are **combined into one semantic text**:
+
+
+
+* title
+* description
+* amenities
+* location
+* property\_type
+* listing\_type
+
+
+
+Example combined text:
+
+
+
+> “3 bedroom furnished apartment in Lekki Phase 1 with parking, 24-hour security, close to the beach.”
+
+
+
+
+
+Fields Used for Filtering (NOT embeddings)
+
+
+
+* price
+* listing\_type
+* location
+* bedrooms (if strict)
+
+
+
+**Dataset Sources**
+
+* Existing company database
+* Export from CMS
+* Scraped listings (if approved)
+* Mock dataset (for early MVP)
+
+
+
+**Output of Dataset Preparation**
+
+* Cleaned property table
+* Text field for embeddings
+* Metadata table for filters
+* Ready for FAISS indexing
+
+
+
+
+
+**CRM INTEGRATION (MVP VIEW)**
+
+CRM Receives:
+
+* User contact info
+* Property request specs
+* Timestamp
+* Recommendation IDs returned
+
+
+
+Why this matters:
+
+* Agents can see what was recommended
+* Follow-up becomes context-aware
+* Better conversion tracking
+
 
-# Testing-Huggingface
 
-This repository is for testing Hugging Face Git push and pull workflows.