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| # 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. | |