# Dream QA / 梦境问答台 PRD

Last updated: 2026-06-08

## 1. Executive Summary

### Problem Statement

Users who wake up from vivid dreams may feel unsettled, curious, or mentally cluttered. They often do not need a definitive "meaning"; they need a short, safe conversation that helps them identify what question the dream left behind and what tiny thing they can do today.

### Proposed Solution

Dream QA is a Gradio app that accepts voice, image, and text dream fragments, normalizes them into a structured dream intake, asks gentle follow-up questions, drafts a grounded interpretation, and ends with one `今日小 Tips`.

The repository and Space may continue to use `Dream Customs` as the deployed project name, but the product experience should no longer be built around permits, contraband, or sealed pact cards.

### Success Criteria

- A user can complete the core text-only Q&A flow in <= 90 seconds on the hosted demo.
- At least one follow-up question feels related to the dream in >= 90% of demo runs.
- Final output references at least one concrete dream detail.
- Final output gives exactly one primary today tip.
- Ordinary cases contain no diagnostic, prophetic, or frightening language.
- Distress-edge cases show a support note.
- The app runs with total model parameters <= 32B and is packaged for Gradio/Hugging Face Space.

## 2. User Experience & Functionality

### User Personas

1. Primary persona: vivid dreamer.
   - Recently sleeps poorly or wakes with strong dream fragments.
   - Wants a small morning reflection, not therapy or fortune-telling.

2. Secondary persona: playful AI explorer.
   - Wants a strange AI experience worth showing a friend.
   - Cares about creativity and shareable outputs.

3. Demo viewer / judge.
   - Needs to understand the interaction in under one minute.
   - Looks for clear small-model fit and Gradio polish.

### User Stories

#### Story 1: Text Dream Q&A

As a vivid dreamer, I want to type my dream so that the app can help me figure out what I am actually wondering about.

Acceptance criteria:

- User can submit text-only input.
- App creates a valid dream intake object.
- App asks at least one relevant follow-up question.
- User can answer or skip.
- Final output includes a grounded today tip.

#### Story 2: Image-Assisted Dream Clues

As a user who remembers images better than words, I want to upload a sketch or bedside note so that the system can use visual dream clues.

Acceptance criteria:

- User can upload one image.
- MiniCPM-V-4.6 extracts visible clues when content exists.
- Visual clues are included in the dream intake and final interpretation.
- If image extraction fails, text-only flow still works.

#### Story 3: Voice Dream Capture

As a half-awake user, I want to speak my dream so that I do not need to type immediately after waking.

Acceptance criteria:

- User can record or upload an audio clip.
- ASR adapter returns a transcript.
- Transcript is visible/editable or transparently included in the dream intake.
- If ASR fails, user receives a concise fallback message and can type manually.

#### Story 4: Follow-Up Question Flow

As a user, I want the AI to ask me a few questions so that the final interpretation feels connected to my dream rather than generic.

Acceptance criteria:

- App generates 1-3 follow-up questions.
- User can answer all, answer some, skip, or ask for another angle.
- Skip path still generates a final tip.
- Questions avoid clinical or diagnostic phrasing.

#### Story 5: Today Tip Card

As a user, I want a clear final result so that I leave with one small thing for today.

Acceptance criteria:

- Result includes dream summary, main question, interpretation, today tip, optional tiny action, optional caring note, and safety note when needed.
- Result is readable on mobile and desktop.
- Result avoids medical, prophetic, or deterministic framing.
- Today tip references a concrete dream anchor.

### Non-Goals

- Medical diagnosis or therapy.
- Dream prophecy or symbolic certainty.
- Sleep-stage tracking.
- Long-term journaling database.
- Account creation.
- Voice output in MVP.
- Social feed or leaderboard.
- Fine-tuning during the hackathon MVP.

## 3. AI System Requirements

### Tool Requirements

- MiniCPM-V-4.6 for image-to-text visual clue extraction.
- MiniCPM5-1B for text reasoning, follow-up questions, interpretation, and Today Tip generation.
- A small ASR adapter for voice-to-text. This adapter is not the core AI experience.
- Gradio for UI.
- Optional HTML/CSS result renderer.

### Prompt Requirements

Prompts must:

- Ask for JSON-compatible output.
- Keep the tone playful, gentle, and non-clinical.
- Avoid strong claims about dream meaning.
- Include a safety boundary for severe distress.
- Keep final advice small enough to execute today.
- Require at least one dream anchor in the final tip.

### Evaluation Strategy

Create an eval set with at least 12 dream inputs:

- 4 text-only dreams.
- 3 image-assisted dreams.
- 3 voice-transcript dreams.
- 2 distress-edge cases.

Measure:

- Schema validity.
- Follow-up relevance.
- Tone safety.
- Dream-anchor grounding.
- Practicality of today tip.
- Ability to recover from missing modality.

Pass thresholds:

- Schema validity: >= 90%.
- No diagnostic language in ordinary cases: 100%.
- Safety note appears in distress-edge cases: >= 90%.
- At least one concrete dream anchor in final result: >= 90%.
- Human usefulness/delight rating on 5 demo cases: average >= 4/5.

## 4. Technical Specifications

### Architecture Overview

```text
Gradio inputs
  -> text input
  -> image upload -> MiniCPM-V-4.6 visual clue extractor
  -> audio upload/record -> ASR adapter
  -> DreamQuestionIntake builder
  -> MiniCPM5-1B summary/main-question prompt
  -> follow-up question flow
  -> user answer/skip/another-angle
  -> MiniCPM5-1B interpretation prompt
  -> TodayTipCard prompt
  -> HTML result renderer
```

### Core Components

- `app.py`: thin Gradio entrypoint.
- `dream_customs/schema.py`: Pydantic models for dream intake, Q&A state, and Today Tip output.
- `dream_customs/models.py`: model/client loading and inference wrappers.
- `dream_customs/prompts.py`: prompt templates.
- `dream_customs/pipeline.py`: orchestration logic.
- `dream_customs/safety.py`: lightweight safety checks and copy.
- `dream_customs/render.py`: HTML result rendering.
- `dream_customs/ui/`: Gradio UI shell, copy, actions, and styles.
- `tests/`: schema, prompt, safety, pipeline, render, and UI action unit tests with mocked model outputs.

### Integration Points

- Hugging Face Space runtime.
- Hugging Face model downloads or private Modal endpoints.
- Gradio audio, image, and text components.
- Optional model cache in Space environment.

### Security & Privacy

- Do not persist user dreams by default.
- Do not log raw dream text or uploaded images in production demo logs.
- Provide visible copy: "This is a playful reflection tool, not medical advice."
- Do not require account login.

## 5. Risks & Roadmap

### Phased Rollout

#### MVP

- Text, image, and voice input.
- Dream intake normalization.
- MiniCPM-V-4.6 image clue extraction.
- MiniCPM5-1B follow-up and final Today Tip.
- HTML output card.
- Safety boundary.

#### v1.1

- Better example gallery.
- Optional bilingual output.
- More robust eval harness.
- Better result export.

#### v2.0

- Voice output through VoxCPM2.
- Local dream history stored only in browser/session.
- Multiple question-guide styles.

### Technical Risks

- MiniCPM-V-4.6 may be heavy for Space hardware if loaded locally.
- MiniCPM5-1B output may fail strict JSON without repair.
- ASR adapter may complicate dependencies.
- Audio input on mobile browsers may behave inconsistently.
- Final tips may become generic unless prompts and repair logic enforce dream anchors.