Spaces:
Runtime error
Runtime error
| title: Sales Email Generator | |
| license: mit | |
| sdk: gradio | |
| emoji: 💻 | |
| colorFrom: blue | |
| colorTo: gray | |
| app_file: app.py | |
| # Smart Sales Email Generator | |
| An AI-powered tool that generates contextual and professional follow-up emails based on previous customer interactions, using RAG, sentiment analysis and quality scoring. | |
| ## AI Tools & Technologies | |
| - RAG (Retrieval Augmented Generation) Implementation: | |
| * Vector Store: ChromaDB for email template storage | |
| * Embeddings: HuggingFace Sentence Transformers | |
| * Similarity Search for context retrieval | |
| - LangChain for orchestrating the RAG pipeline | |
| - Hugging Face Transformers for sentiment analysis | |
| - DeepSeek model for email generation | |
| - Gradio for the interactive web interface | |
| - Transformers pipeline for NLP tasks | |
| ## Key Features | |
| - RAG-powered contextual email generation | |
| - Retrieval of similar past interactions | |
| - Automated sentiment analysis for tone detection | |
| - Customizable urgency levels and situation types | |
| - Real-time email quality scoring | |
| - Multiple pre-built templates for common scenarios | |
| - Context-aware response generation | |
| ## Technical Skills Demonstrated | |
| - RAG System Implementation | |
| - Vector Database Management | |
| - Embedding Generation | |
| - Natural Language Processing (NLP) | |
| - Large Language Model (LLM) integration | |
| - Prompt engineering | |
| - API integration (Hugging Face Hub) | |
| - Web application development | |
| - Machine Learning model deployment | |
| - GPU acceleration support | |
| - Error handling and input validation | |
| ## Architecture | |
| - RAG Components: | |
| * Vector Store for template storage | |
| * Embedding model for text vectorization | |
| * Similarity search for context retrieval | |
| - Language Models: | |
| * DeepSeek for generation | |
| * BERT-based model for sentiment analysis | |
| - Interface: | |
| * Gradio for web UI | |
| * Real-time processing | |
| ## Use Cases | |
| - Customer Service Follow-ups | |
| - Complaint Resolution | |
| - Service Issue Communication | |
| - Payment Dispute Handling | |
| - Product Query Responses | |
| - General Business Communication | |
| ## How to Use | |
| 1. Enter the previous customer interaction | |
| 2. Select the situation type from available options | |
| 3. Choose tone (optional - will be automatically detected) | |
| 4. Set urgency level (High/Medium/Low) | |
| 5. Submit to generate a professional follow-up email with quality score | |
| ## Development Stack | |
| - Python 3.x | |
| - LangChain Framework | |
| - ChromaDB | |
| - HuggingFace Transformers | |
| - Gradio UI Framework | |
| - CUDA support for GPU acceleration | |
| ## Future Enhancements | |
| - Enhanced RAG capabilities | |
| - Expanded template database | |
| - Response time optimization | |
| - Direct email system integration | |
| - Analytics and tracking capabilities | |
| - Enhanced scoring system | |
| ## License | |
| MIT License | |
| ## Author | |
| [Tobi Ajibola] |