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----
-title: User Modeling Agent
-emoji: 📝
-colorFrom: green
-colorTo: red
-sdk: docker
-app_port: 7860
-pinned: false
----
-
-# User Modeling Agent
-
-**DSN × BCT LLM Agent Challenge 2026 — Task A.**
-
-An agent that reads a person into a behavioural *persona*, then writes the
-star rating and the review that person would leave for an unseen product —
-and critiques and revises its own draft before returning it.
-
-> Live demo: *(your HuggingFace Space URL)*
-> Code: *(your GitHub repo URL)*
-
----
-
-## What it does
-
-Given a **user persona** and **product details**, the agent produces:
-
-- a **star rating** (1–5) the user would likely give, and
-- a **written review** in that user's voice — tone, length, and quirks matched.
-
-It is not a generic review generator. Every output is conditioned on a
-specific reader, and the rating is reasoned, not guessed.
-
-## The agentic workflow
-
-The system is an agent, not a single prompt. It runs a five-step loop:
-
-1. **Build the persona.** A `PersonaEngine` extracts a structured persona —
-   quantitative signals (average rating, rating spread, review length,
-   domains, rating distribution) and qualitative voice (tone, preferred
-   themes, common complaints, a one-line voice descriptor) distilled by an
-   LLM from sample reviews. In the deployed app the persona can also be
-   *composed directly* from typed input — the brief's persona-as-input
-   contract.
-
-2. **Select grounding history.** For a real user, the agent picks the few
-   past reviews most similar to the target item, so it writes from concrete
-   evidence of how this person actually phrases things.
-
-3. **Generate the rating and review.** A single LLM call, with the rating
-   reasoned in two explicit steps — first the persona *prior* (what this
-   user usually gives), then the *item evidence* (what the title and
-   description signal). The final rating is the prior adjusted by the
-   evidence, so a generous reviewer still rates a poor item low and a
-   critical reviewer still rates a strong item high.
-
-4. **Self-reflection — critique and revise.** A critic LLM audits the draft
-   for rating–text consistency, voice match, and on-topic fit. If it
-   objects, the agent rewrites with that feedback and re-checks — up to two
-   cycles. This act → critique → revise loop is what makes it an agent.
-
-5. **Post-process.** The rating is clamped to range. An optional Nigerian
-   Pidgin rendering layer can restyle the review while preserving meaning,
-   sentiment, and rating.
-
-The agent degrades gracefully: if an LLM call fails, it falls back to a
-deterministic persona rather than crashing.
-
-## How it maps to the Task A rubric
-
-- **Review Text Quality** — reviews are grounded in the user's real past
-  reviews and self-critiqued for voice match.
-- **Rating Accuracy** — the two-step prior-plus-evidence rating logic
-  corrects the common failure of predicting from the user average alone.
-- **Behavioural Fidelity** — persona-conditioned generation; the persona
-  portrait is visible in the app for inspection.
-- **Nigerian contextualization (bonus)** — a toggleable Nigerian Pidgin
-  rendering layer; off by default so scored output stays standard English.
-
-## Running locally
-
-```bash
-pip install -r requirements.txt
-# set your key in a .env file:  LLM_PROVIDER=gemini  and  GEMINI_API_KEY=...
-streamlit run app.py
-```
-
-The processed data (`data/processed/*.parquet`) must be present.
-
-A FastAPI service is also available:
-
-```bash
-uvicorn task_a_user_modeling.main:app --reload
-```
-
-## Project layout
-
-```
-core/                 shared engine — config, llm, persona, reflection, nigerian
-task_a_user_modeling/ the Impersonation agent + FastAPI service
-scripts/              test harness (test_task_a.py)
-data/processed/       Amazon Reviews 2023 — Books · Movies & TV · Kindle Store
-app.py                Streamlit demo
-```
-
-## Configuration
-
-Set in a `.env` file (never commit it):
-
-- `LLM_PROVIDER` — `gemini` or `openai`
-- `GEMINI_API_KEY` / `OPENAI_API_KEY`
-
-On a HuggingFace Space, set these as **Secrets** in Space settings.
-
-## Notes and honest limitations
-
-- The self-reflection critic checks internal consistency; it cannot catch a
-  rating that is wrong but self-consistent.
-- Rating prediction on hard cases (a critical user who loved something) is
-  improved by the two-step logic but can still be ~0.5–1.0★ off.
-- LLM output is non-deterministic; single-run results vary, so evaluation
-  averages across many users.
-
-## Credits
-
-Built for the DSN × BCT LLM Agent Challenge 2026.
-Author: *(your name)*. Dataset: Amazon Reviews 2023.