Added Hugging Face Space metadata

README.md

@@ -1,184 +1,24 @@
-💼 Twitter Sentiment Intelligence
-The solution spans data ingestion, preprocessing, model training, governance, deployment readiness, and business storytelling — all within a lightweight, reproducible ML pipeline.
-
-🧰 Technology Stack & Analytics Capabilities
-
-Python, Pandas, Scikit-learn, NLTK – rapid experimentation & production-ready sentiment pipelines
-
-TF-IDF Vectorization + Multiclass Logistic Regression – explainable predictions across positive, neutral, negative classes
-
-Streamlit UI (app/app.py) – displays class probabilities & narrative insights for stakeholders
-
-Notebook-driven exploration (notebooks/) – retraining lineage and model interpretability
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-Container-ready CI/CD pipeline – automates tests, linting, and retraining
-
-🧭 Why This Project Stands Out
-
-Oracle ecosystem alignment – optional ingestion from Oracle Autonomous Database with OCI deployment playbooks
-
-Consulting-grade engineering – modular Python package, logging, configuration management, GitHub Actions CI
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-Business storytelling – dashboards, KPIs, and executive-friendly insights translating data into measurable ROI
-
-🔗 Live Resources & Quick Links
-Asset	Purpose	Link
-Live Streamlit Demo	Interview-ready interactive dashboard	https://<your-app>.streamlit.app
-
-Architecture Blueprint	Deep dive on data, model & DevOps layers	docs/architecture.md
-
-OCI Deployment Playbook	Containerized rollout on Oracle Cloud	deployment/oracle_cloud.md
-
-Streamlit Cloud Guide	Launch public URL in minutes	deployment/streamlit_cloud.md
-
-Vercel Redirect Playbook	Vanity domain / QR-friendly link	deployment/vercel_redirect.md
-
-GitHub Live Validation	Verify live deployment health	deployment/github_live_validation.md
-
-Training Data Sample	Quick-start dataset for retraining	data/twitter_training.csv
-
-✅ After publishing, replace the placeholder URL above and embed it directly in your resume / interview slides.
-
-🏗️ Architecture Overview
-flowchart LR
-    subgraph Data_Layer
-        A[Oracle Autonomous DB] -- optional --> B[(CSV Data)]
-    end
-    subgraph Processing_Layer
-        B --> C[Data Loader]
-        C --> D[Text Preprocessor]
-        D --> E[Scikit-learn Pipeline]
-        E --> F[Model Metrics]
-    end
-    subgraph Experience_Layer
-        E --> G[Streamlit Dashboard]
-        E --> H[CLI Automation]
-        F --> G
-        F --> I[Reporting / GitHub Pages]
-    end
-    subgraph DevOps_Layer
-        J[GitHub Actions CI]
-        J --> E
-        J --> G
-    end
-
-📁 Repository Structure
-Path	Description
-app/	Streamlit app for demos
-artifacts/	Generated model artifacts (gitignored; created via scripts/train.py)
-config/	Central YAML settings controlling ingestion & deployment toggles
-data/	Sample labelled tweets
-deployment/	Multi-cloud deployment guides
-docs/	Architecture diagrams & KPI catalogue
-scripts/	Automation scripts for training & inference
-src/twitter_sentiment/	Core reusable Python package
-tests/	Pytest-based unit tests
-.github/workflows/	CI pipeline (lint + tests)
-🚀 Quick Start
-# 1️⃣  Create virtual environment
-python -m venv .venv
-source .venv/bin/activate  # Windows: .venv\Scripts\activate
-
-# 2️⃣  Install dependencies
-pip install --upgrade pip
-pip install -r requirements.txt
-
-# 3️⃣  Train pipeline & generate artifacts
-python scripts/train.py
-
-# 4️⃣  Launch Deloitte Storytelling Dashboard
-streamlit run app/app.py
-
-
-Note: Artifacts are excluded from source control.
-Run python scripts/train.py whenever cloning the repo to recreate artifacts/sentiment_pipeline.joblib.
-
-🧪 Quality Gates
-Check	Command
-Unit tests	pytest -q
-Package compile check	python -m compileall src
-Model retrain	python scripts/train.py
-
-CI runs automatically on every push or PR to main.
-
-🧩 Feature Highlights
-
-Config-driven ingestion switch (CSV ⇄ Oracle DB)
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-Reusable text-cleaning module (URL, mention, punctuation handling)
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-Auditable Scikit-learn pipeline with persisted metrics
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-Streamlit dashboard → probabilities + governance tab
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-CLI utilities for automated workflows
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-Pytest suite + GitHub Actions CI/CD
-
-☁️ Cloud Deployment
-
-See deployment/oracle_cloud.md
- for the Oracle Cloud Infrastructure (OCI) guide:
-
-Containerize Streamlit app using OCI Container Instances
-
-Automate retraining via OCI Data Science jobs / GitHub Actions
-
-Connect to Autonomous DB with wallet credentials
-
-Or follow deployment/streamlit_cloud.md
- for a free Streamlit Cloud public link—perfect for interviews.
-
-🗃️ Oracle Database Integration (Optional)
-oracle_integration:
-  enabled: true
-  wallet_location: /path/to/wallet
-  user: DATA_ENGINEER
-  dsn: myadb_high
-  sql_query: |
-    SELECT text, sentiment
-    FROM analytics.twitter_training_data
-    WHERE created_at >= SYSDATE - 30
-
-📊 Storytelling in Interviews
-
-Business Impact – sentiment tracking → improved ROI & retention
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-Oracle Expertise – wallet connectivity, SQL modeling, OCI integration
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-Engineering Rigor – modular design, testing, CI/CD
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-Consulting Mindset – executive-ready Streamlit deliverable
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-📦 GitHub Best Practices
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-Use feature branches (e.g., feature/oracle-ingestion)
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-Raise PRs with CI status + screenshots
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-Track backlog via GitHub Projects
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-Tag releases (e.g., v1.0.0) post-deployment
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-🧠 Training Artifacts
-
-Generated after running scripts/train.py:
-
-sentiment_pipeline.joblib – Full TF-IDF + Logistic Regression pipeline
-
-Legacy (compatible) files:
-
-app/logistic_model.pkl
-
-app/tfidf_vectorizer.pkl
-
-sentiment_model.pkl
-
-Use these for quick demos or backward comparisons.
-
-🤝 Contributing
-
-Pull requests welcome!
-Please open an issue describing any enhancement or bugfix before major changes.
- 
-"# redeploy" 
+---
+title: Twitter Sentiment Intelligence
+emoji: 📊
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.37.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# 🧠 Twitter Sentiment Intelligence
+A Deloitte-ready AI dashboard that performs **real-time sentiment analysis** on tweets using a fine-tuned pipeline deployed via Hugging Face.
+
+## 🚀 Features
+- Real-time tweet sentiment prediction  
+- Visual sentiment distribution graphs  
+- Interactive Streamlit dashboard  
+- Integrated Hugging Face model inference  
+
+---
+
+👨‍💻 **Developed by:** [Vishnu Singh](https://github.com/Youranalyst-coder)