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f92cedf d72e39c f92cedf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | # Presentation Process
This is the simple version you can use when presenting the project.
## One-Sentence Summary
Our system helps Hong Kong elderly users check suspicious messages or screenshots by comparing them with real scraped fraud-news cases and giving clear safety advice.
The app supports Traditional Chinese, Simplified Chinese, and English. In the web app, the answer language follows the selected interface/output language. If an API client omits the language or sends `auto`, the backend detects the suspicious message language; Traditional Chinese remains the default for Hong Kong Cantonese-style messages.
## The Process
### 1. Collect Fraud News
We scrape Hong Kong fraud-related news articles and store the case details.
Think of this as building a small local fraud case library.
### 2. Convert Cases Into Searchable Vectors
Each case is converted into a numerical representation called an embedding.
For presentation, you can say:
> We turn each news case into a searchable fingerprint, so similar scam patterns can be found quickly.
### 3. User Enters A Suspicious Message
The user can:
- paste a suspicious SMS, WhatsApp message, or email
- upload a screenshot
If the user uploads a screenshot, Qwen OCR first reads the text from the image.
### 4. Search Similar Cases
The user message is also converted into an embedding.
The system searches the Chroma vector database and retrieves the most similar fraud cases.
Example:
- user message says parcel delivery fee
- system retrieves similar parcel-delivery scam cases
- user message says investment return
- system retrieves similar investment scam cases
### 5. Qwen Explains The Risk
Qwen receives:
- the user message
- the retrieved similar cases
- strict elderly-friendly safety rules
It then gives:
- a fraud score from 0-10
- What Happened
- Red Flags
- What To Do
- the answer in Traditional Chinese, Simplified Chinese, or English based on the selected output language, with auto detection only when no language is selected
### 6. Safety Advice For Hong Kong
The answer always reminds users that if they feel suspicious, they should call:
**ADCC Anti-Deception Coordination Centre: 18222**
## Why This Is Useful For Elderly Users
- They do not need to understand technical terms.
- They can upload screenshots instead of typing everything.
- The answer is short and clear.
- It gives immediate actions, not just a warning.
- It uses Hong Kong-specific fraud cases and Hong Kong help resources.
## Presentation Demonstration Script
1. Open the app.
2. Paste a suspicious message, for example a fake parcel fee message.
3. Click **檢查風險**.
4. Show the score.
5. Show the similar cases retrieved from the vector database.
6. Explain the three safety sections:
- What Happened
- Red Flags
- What To Do
7. Point out the ADCC 18222 reminder.
## Technical Architecture
```mermaid
flowchart LR
A["User message or screenshot"] --> B["Frontend"]
B --> C["FastAPI backend"]
C --> D["Qwen OCR if screenshot"]
C --> E["Embedding model"]
E --> F["Chroma vector database"]
F --> G["Similar fraud cases"]
G --> H["Qwen risk explanation"]
H --> I["Elderly-friendly result"]
```
## Key Point To Say
The AI does not answer from memory only. It first retrieves similar cases from our embedded fraud-news database, then uses those cases to explain the risk safely and simply.
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