README.md

metadata

title: SentimentScope
emoji: 🎯
colorFrom: indigo
colorTo: green
sdk: docker
app_port: 7860
pinned: false
short_description: Educational sentiment analysis + AI-text detection
models:
  - cardiffnlp/twitter-roberta-base-sentiment-latest
  - distilbert/distilbert-base-uncased-finetuned-sst-2-english
  - desklib/ai-text-detector-v1.01
  - fakespot-ai/roberta-base-ai-text-detection-v1
  - Oxidane/tmr-ai-text-detector

SentimentScope

An educational sentiment-analysis showcase: paste text (or upload a CSV) and see what a transformer classifier actually does — class probabilities, token attributions, and side-by-side model comparison.

• Analyze — 3-class sentiment (negative / neutral / positive) from RoBERTa fine-tuned on ~124M tweets, with per-class confidence bars.
• Explain — token-level attributions via Layer Integrated Gradients: which words pushed the model toward its prediction.
• Batch — CSV upload with aggregate charts.
• Compare — the same text through different models (3-class social-media RoBERTa vs binary SST-2 DistilBERT) to see domain and label-space mismatch.
• AI Detector — one paragraph run through three AI-text detectors at once (desklib / fakespot / oxidane), with a disagreement flag and a verbatim uncertainty warning: detector disagreement is the uncertainty signal.
• How it works — a plain-language walkthrough of the pipeline.

Public deployment limits

This free CPU Space is rate-limited (30 requests/min per IP) and serves an allowlist of five models — two sentiment models plus all three AI detectors. Clone the repo and run it locally (docker compose or the dev servers) for the full model registry.

Backend: FastAPI + PyTorch + transformers + captum. Frontend: React + Vite. The container serves both — the SPA via FastAPI StaticFiles, the API under /api/*, weights baked into the image so cold starts never re-download.