README.md

metadata

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 (vector store) + Sentence Transformers (embeddings)
• Orchestration: LangChain text splitting
• News Data: NewsAPI (free tier)
• Generation: Google Gemini Flash (free tier)
• UI: Gradio

Setup

Running Locally

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 — free tier, 100 requests/day, last 30 days of articles
• Google Gemini: 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.

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Built by Sammie Wong