📡 ReputationRadar

Real-time brand intelligence with human-readable insights.

ReputationRadar is a Streamlit dashboard that unifies Reddit, Twitter/X, and Trustpilot chatter, classifies sentiment with OpenAI (or VADER fallback), and delivers exportable executive summaries. It ships with modular services, caching, retry-aware scrapers, demo data, and pytest coverage—ready for production hardening or internal deployment.

Demo
Feature Highlights
Architecture Overview
Quick Start
Configuration & Credentials
Running Tests
Working Without API Keys
Exports & Deliverables
Troubleshooting
Legal & Compliance

Demo

The video demo of the app can be found at:- https://drive.google.com/file/d/1XZ09NOht1H5LCJEbOrAldny2L5SV1DeT/view?usp=sharing

Feature Highlights

Adaptive Ingestion – Toggle Reddit, Twitter/X, and Trustpilot independently; backoff, caching, and polite scraping keep providers happy.
Smart Sentiment – Batch OpenAI classification with rationale-aware prompts and auto-fallback to VADER when credentials are missing.
Actionable Summaries – Executive brief card (highlights, risks, tone, actions) plus refreshed PDF layout that respects margins and typography.
Interactive Insights – Plotly visuals, per-source filtering, and a lean “Representative Mentions” link list to avoid content overload.
Export Suite – CSV, Excel (auto-sized columns), and polished PDF snapshots for stakeholder handoffs.
Robust Foundation – Structured logging, reusable UI components, pytest suites, Dockerfile, and Makefile for frictionless iteration.

Architecture Overview

community-contributions/Reputation_Radar/
├── app.py                 # Streamlit orchestrator & layout
├── components/            # Sidebar, dashboard, summaries, loaders
├── services/              # Reddit/Twitter clients, Trustpilot scraper, LLM wrapper, utilities
├── samples/               # Demo JSON payloads (auto-loaded when credentials missing)
├── tests/                 # Pytest coverage for utilities and LLM fallback
├── assets/                # Placeholder icons/logo
├── logs/                  # Streaming log output
├── requirements.txt       # Runtime dependencies (includes PDF + Excel writers)
├── Dockerfile             # Containerised deployment recipe
└── Makefile               # Helper targets for install/run/test

Each service returns a normalised payload to keep the downstream sentiment pipeline deterministic. Deduplication is handled centrally via fuzzy matching, and timestamps are coerced to UTC before analysis.

Quick Start

Clone & enter the project directory (community-contributions/Reputation_Radar).
Install dependencies and launch Streamlit:
```
pip install -r requirements.txt && streamlit run app.py
```
(Use a virtual environment if preferred.)
Populate the sidebar: add your brand name, optional filters, toggled sources, and API credentials (stored only in session state).
Click “Run Analysis 🚀” – follow the status indicators as sources load, sentiment processes, and summaries render.

Optional Docker Run

docker build -t reputation-radar .
docker run --rm -p 8501:8501 -e OPENAI_API_KEY=your_key reputation-radar

Configuration & Credentials

The app reads from .env, Streamlit secrets, or direct sidebar input. Expected variables:

Variable	Purpose
`OPENAI_API_KEY`	Enables OpenAI sentiment + executive summary (falls back to VADER if absent).
`REDDIT_CLIENT_ID`	PRAW client ID for Reddit API access.
`REDDIT_CLIENT_SECRET`	PRAW client secret.
`REDDIT_USER_AGENT`	Descriptive user agent (e.g., `ReputationRadar/1.0 by you`).
`TWITTER_BEARER_TOKEN`	Twitter/X v2 recent search bearer token.

Credential validation mirrors the guidance from week1/day1.ipynb—mistyped OpenAI keys surface helpful warnings before analysis begins.

Running Tests

pytest

Tests cover sentiment fallback behaviour and core sanitisation/deduplication helpers. Extend them as you add new data transforms or UI logic.

Working Without API Keys

Reddit/Twitter/Trustpilot can be toggled independently; missing credentials raise gentle warnings rather than hard failures.
Curated fixtures in samples/ automatically load for any disabled source, keeping charts, exports, and PDF output functional in demo mode.
The LLM layer drops to VADER sentiment scoring and skips the executive summary when OPENAI_API_KEY is absent.

Exports & Deliverables

CSV – Clean, UTF-8 dataset for quick spreadsheet edits.
Excel – Auto-sized columns, formatted timestamps, instantaneous import into stakeholder workbooks.
PDF – Professionally typeset executive summary with bullet lists, consistent margins, and wrapped excerpts (thanks to ReportLab’s Platypus engine).

All exports are regenerated on demand and never persisted server-side.

Troubleshooting

OpenAI key missing/invalid – Watch the sidebar notices; the app falls back gracefully but no executive summary will be produced.
Twitter 401/403 – Confirm your bearer token scope and that the project has search access enabled.
Rate limiting (429) – Built-in sleeps help, but repeated requests may require manual pauses. Try narrowing filters or reducing per-source limits.
Trustpilot blocks – Respect robots.txt. If scraping is denied, switch to the official API or provide compliant CSV imports.
PDF text clipping – Resolved by the new layout; if you customise templates ensure col widths/table styles remain inside page margins.

Legal & Compliance

ReputationRadar surfaces public discourse for legitimate monitoring purposes. Always comply with each platform’s Terms of Service, local regulations, and privacy expectations. Avoid storing third-party data longer than necessary, and never commit API keys to version control—the app only keeps them in Streamlit session state.

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