chore: remove duplicate files (old Streamlit README, duplicate video)

README_SPACE.md

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-# Sentiment Evolution Tracker – Hugging Face Space Edition
-
-MCP-powered customer sentiment monitoring packaged for Hugging Face Spaces and local demos.
-
-> Nota: el dashboard Streamlit es opcional y no forma parte del entregable principal. Solo ejecútalo si quieres experimentar con la versión interactiva local.
-
-## 🚀 Launch The Demo (Opcional)
-
-```powershell
-streamlit run app.py
-```
-
-Open `http://localhost:8501` for the interactive dashboard.
-
-## 📊 Feature Set
-
-### Interactive Dashboard
-- Four KPIs (customers, analyses, sentiment, alerts).
-- Two charts (churn risk vs. time, sentiment trend).
-- Detailed customer table with statuses.
-
-### Deep-Dive Panels
-- Select any customer to view historical analyses.
-- Inspect sentiment velocity and recommended actions.
-- Highlight churn drivers automatically.
-
-### Multi-Customer Trends
-- Compare sentiment trajectories across clients.
-- Identify shared risk signals.
-
-### MCP Tooling (7 tools)
-1. `analyze_sentiment_evolution`
-2. `detect_risk_signals`
-3. `predict_next_action`
-4. `get_customer_history`
-5. `get_high_risk_customers`
-6. `get_database_statistics`
-7. `save_analysis`
-
-## 💻 Local Setup
-
-Requirements: Python 3.10+, pip.
-
-```powershell
-git clone https://huggingface.co/spaces/MCP-1st-Birthday/sentiment-tracker
-cd mcp-nlp-server
-pip install -r requirements.txt
-python init_db.py
-python tools\populate_demo_data.py
-python tools\dashboard.py
-python tools\generate_report.py      # opens data/reporte_clientes.html
-streamlit run app.py
-```
-
-## 🔧 MCP Configuration
-
-1. Edit `config/claude_desktop_config.json`.
-2. Point the server entry to `src/mcp_server.py`.
-3. Restart Claude Desktop and select the sentiment tracker server.
-
-```json
-{
-  "mcpServers": {
-    "sentiment-tracker": {
-      "command": "python",
-      "args": ["src/mcp_server.py"],
-      "cwd": "C:/path/to/mcp-nlp-server"
-    }
-  }
-}
-```
-
-## 📈 Use Cases
-
-### 1. Churn Prediction
-```
-Input → customer ID
-Process → trend analysis + risk signals + alerts
-Output → alert if risk > 70% with suggested actions
-```
-
-### 2. Real-Time Monitoring
-```
-Dashboard highlights:
-- Critical accounts (red)
-- At-risk accounts (orange)
-- Healthy accounts (green)
-Updated whenever new analyses are stored
-```
-
-### 3. Executive Reporting
-```
-Generate the HTML report to share daily:
-- Risk charts
-- Sentiment evolution
-- Top 5 accounts needing attention
-- Actionable recommendations
-```
-
-### 4. LLM Integration
-```
-Claude workflow:
-→ get_high_risk_customers()
-→ get_customer_history()
-→ predict_next_action()
-→ Respond with urgency, revenue at risk, and next steps
-```
-
-## 📊 Sample Dataset
-
-- Five demo customers (manufacturing, tech, retail, healthcare, finance).
-- Seventeen conversations across rising/declining/stable trends.
-- Alerts triggered automatically when risk exceeds thresholds.
-
-## 🎯 Architecture
-
-```
-User / Team Lead
-        ↓
-Claude Desktop (optional)
-        ↓ MCP Protocol (stdio)
-Sentiment Tracker Server (7 tools)
-        ↓
-SQLite Database (customer_profiles, conversations, risk_alerts)
-```
-
-## 🔑 Key Advantages
-
-- **Local-first**: keep customer data on-prem.
-- **Zero external APIs**: predictable cost, improved privacy.
-- **Real-time**: sentiment scoring < 100 ms per request.
-- **Predictive**: churn detection 5–7 days ahead.
-- **Agentic**: Claude drives the workflow autonomously.
-- **Scalable**: handles thousands of customers on commodity hardware.
-
-## 📚 Documentation
-
-- [Architecture](docs/ARCHITECTURE.md)
-- [Quick Start](docs/QUICK_START.md)
-- [Blog Post](../BLOG_POST.md)
-
-## 🤝 Contributions
-
-Suggestions are welcome—open an issue or submit a pull request.
-
-## 📝 License
-
-MIT License.
-
-## 🙏 Acknowledgements
-
-- Anthropic for MCP.
-- Hugging Face for the hosting platform.
-- TextBlob + NLTK for NLP utilities.
-
----
-
-Built for the MCP 1st Birthday Hackathon 🎉
-
-[GitHub](https://github.com) • [Blog](../BLOG_POST.md) • [Docs](docs/)