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| title: Trend Longevity Analyser | |
| emoji: 📊 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| app_file: app.py | |
| pinned: true | |
| # 📊 Trend Longevity Analyser | |
| A **RAG-powered trend intelligence tool** that classifies whether any topic is *Early, Rising, at Peak, Declining, or Fading* — using live news data. | |
| ## How It Works | |
| This tool implements a full 6-step RAG (Retrieval-Augmented Generation) pipeline: | |
| | Step | What Happens | | |
| |------|-------------| | |
| | **1. Fetch** | NewsAPI pulls up to 50 recent articles on your topic | | |
| | **2. Chunk** | LangChain splits articles into overlapping 400-character chunks | | |
| | **3. Embed** | `all-MiniLM-L6-v2` encodes each chunk into a 384-dim vector | | |
| | **4. Store** | ChromaDB holds all vectors in an in-memory collection | | |
| | **5. Retrieve** | Semantic search returns the 10 most relevant chunks | | |
| | **6. Generate** | Gemini analyses the retrieved context and returns a structured trend signal | | |
| ## Tech Stack | |
| - **Retrieval:** [ChromaDB](https://www.trychroma.com) (vector store) + [Sentence Transformers](https://sbert.net) (embeddings) | |
| - **Orchestration:** [LangChain](https://langchain.com) text splitting | |
| - **News Data:** [NewsAPI](https://newsapi.org) (free tier) | |
| - **Generation:** [Google Gemini Flash](https://aistudio.google.com) (free tier) | |
| - **UI:** [Gradio](https://gradio.app) | |
| ## Setup | |
| ### Running Locally | |
| ```bash | |
| git clone https://huggingface.co/spaces/lization/trend-longevity-analyser | |
| cd trend-longevity-analyser | |
| pip install -r requirements.txt | |
| export NEWSAPI_KEY=your_key_here | |
| export ANTHROPIC_API_KEY=your_key_here | |
| python app.py | |
| ``` | |
| ### HF Space Secrets | |
| Set `NEWSAPI_KEY` and `GEMINI_API_KEY` as Space Secrets under **Settings → Variables and Secrets**. If not set, users can enter keys directly in the UI. | |
| ## Get Your Free API Keys | |
| - **NewsAPI:** [newsapi.org/register](https://newsapi.org/register) — free tier, 100 requests/day, last 30 days of articles | |
| - **Google Gemini:** [aistudio.google.com](https://aistudio.google.com) — free tier, 1,500 requests/day, no credit card needed | |
| ## Why RAG (Not Just a Prompt)? | |
| Instead of passing all 50 articles to Claude (expensive, noisy, hits context limits), the RAG approach: | |
| 1. Embeds every chunk as a vector | |
| 2. Retrieves only the **10 most semantically relevant chunks** via cosine similarity | |
| 3. Passes those to Claude for analysis | |
| This gives more focused, accurate, and cost-efficient results — and is directly analogous to production social listening systems used in enterprise contexts. | |
| --- | |
| Built by [Sammie Wong](https://linkedin.com/in/sammie-wong) | |