Document deployment and add demo GIF

.gitattributes

@@ -44,3 +44,4 @@ UM_Handbook/outputs/baseline2_rag_harness_agent/lora_adapter/tokenizer.json filt
 UM_Handbook/outputs/baseline2_rag_harness_agent/trainer_runs/checkpoint-1100/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 UM_Handbook/outputs/baseline2_rag_harness_agent/trainer_runs/checkpoint-1300/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 UM_Handbook/assets/tensortalk-rag-inspector.png filter=lfs diff=lfs merge=lfs -text
+UM_Handbook/assets/tensortalk-simulation.gif filter=lfs diff=lfs merge=lfs -text

README.md

@@ -30,9 +30,50 @@ tags:
 
 # TensorTalk
 
-**TensorTalk** is a Universiti Malaya Faculty of Computer Science and Information Technology handbook QA project built around **Qwen3-8B**, supervised fine-tuning, metadata-aware RAG, an official-source web helper, and a guarded harness for traceable answers.
+**TensorTalk** is a fully deployed Universiti Malaya Faculty of Computer Science and Information Technology handbook QA system built around **Qwen3-8B**, supervised fine-tuning, metadata-aware RAG, an official-source web helper, and a guarded harness for traceable answers.
 
-The repository is best read as a complete LLM engineering project, not only as a standalone checkpoint. It contains the handbook datasets, SFT/RAG/PPO notebooks, the Baseline 2 LoRA adapter artifacts, retrieval and generation evaluation outputs, and the TensorTalk UI demonstration.
+The project includes the research and training pipeline in this repository, a separately maintained model repository, and a complete Vercel-deployed frontend experience. The live application provides conversation history, handbook and official-web routing, semantic retrieval controls, answer traces, grounding status, and source-aware responses.
+
+## Live Deployment
+
+**Try TensorTalk:** [https://tensor-talk.vercel.app/](https://tensor-talk.vercel.app/)
+
+| Project Component | Link | Role |
+| --- | --- | --- |
+| Live web application | [tensor-talk.vercel.app](https://tensor-talk.vercel.app/) | Public Vercel deployment for interacting with TensorTalk. |
+| Frontend source code | [github.com/nfdlh/tensor-talk](https://github.com/nfdlh/tensor-talk) | Source repository for the deployed web interface. |
+| Model repository | [huggingface.co/nfdlh/tensortalk](https://huggingface.co/nfdlh/tensortalk) | Related TensorTalk model repository used by the deployed project. |
+| Training and research repository | [TensorCat/TensorTalk/UM_Handbook](https://huggingface.co/TensorCat/TensorTalk/tree/main/UM_Handbook) | SFT, RAG, agent-harness, PPO, datasets, adapters, and evaluation artifacts. |
+
+### Deployment Architecture
+
+```text
+User Browser
+    |
+    v
+Vercel Frontend
+https://tensor-talk.vercel.app/
+    |
+    +-- Conversation threads and responsive chat interface
+    +-- Semantic retrieval and routing controls
+    +-- Evidence, grounding, and tracing views
+    |
+    v
+TensorTalk Model + RAG / Agent Harness
+    |
+    +-- UM handbook knowledge base
+    +-- Metadata-aware dense retrieval
+    +-- Official UM / FSKTM web-source helper
+    +-- Evidence and answer-grounding checks
+```
+
+The frontend deployment turns the research notebooks and model artifacts into a complete user-facing application. It exposes the system's intermediate retrieval and validation states instead of presenting TensorTalk as a black-box chatbot.
+
+## Project Demonstration
+
+The following GIF is generated from the complete deployment walkthrough. The original HDR recording was brightness-normalized for readability and accelerated to keep the README demonstration practical.
+
+![TensorTalk full deployment demonstration](UM_Handbook/assets/tensortalk-simulation.gif)
 
 ## Project Screenshot
 
@@ -66,6 +107,7 @@ The project compares three stages:
 | Metadata-aware reranker | Uses scope, section, subsection, and keywords to reduce wrong-context answers. |
 | Official web helper | Searches constrained official UM/FSKTM-related sources when local handbook evidence is not enough. |
 | Harness engineering | Runs source guards, fake-URL guards, evidence checks, grounding checks, retry logic, and fallback rules. |
+| Vercel frontend | Provides the deployed conversation workspace, history, retrieval controls, and trace views. |
 | TensorTalk UI | Shows answers together with traceable RAG, web, and harness evidence panels. |
 
 ## Data Assets
@@ -99,6 +141,16 @@ The 521 handbook chunks are split into 58 general, 250 postgraduate, and 213 und
 
 The retrieval results show why the project moved beyond closed-book SFT. The model can speak in the right academic tone after fine-tuning, but RAG and harness checks make the answers more evidence-grounded and easier to audit.
 
+## End-to-End Project Flow
+
+1. Handbook PDFs are converted into structured Markdown.
+2. Source chunks and question-answer datasets are built with scope and source metadata.
+3. Qwen3-8B is adapted with SFT using LoRA / QLoRA.
+4. BGE embeddings and FAISS retrieve handbook evidence, followed by metadata-aware reranking.
+5. The agent harness validates sources, rejects unsupported evidence, retries weak retrieval, and checks answer grounding.
+6. Rule-reward PPO experiments further shape response behavior.
+7. The Vercel frontend exposes the complete workflow through an interactive deployed experience.
+
 ## Repository Map
 
 ```text
@@ -157,6 +209,8 @@ model.eval()
 
 For the full TensorTalk behavior shown in the screenshot, use the adapter together with the RAG knowledge base, FAISS retriever, official-source web helper, and harness checks from the notebooks. The model weights alone do not include the live retrieval index or web-agent runtime.
 
+For an immediate end-to-end demonstration, use the [deployed TensorTalk web application](https://tensor-talk.vercel.app/).
+
 ## Intended Use
 
 TensorTalk is intended for research, education, and demonstration of: