End to End Scalable E-Commerce AI Chatbot with Multi-Model NLP using pytorch | #codewithhafiz

🔧 Technical Stack & Skills
Natural Language Processing (NLP):
Preprocessed text with NLTK (tokenization, lemmatization)
Designed intent classification using PyTorch with custom neural networks
Implemented ensemble modeling (2 trained models) for robust predictions
Backend: FastAPI (REST endpoint for seamless integration)
Frontend: Streamlit for interactive chat UI
Deployment: Container-ready with modular architecture

🛠️ Key Features
Multi-Model NLP Pipeline:
Combined predictions from two trained models for higher accuracy.
Confidence-based fallback for ambiguous queries.

Context-Aware Conversations:
Tracked order status, returns, and product info using context management.
Regex-based order ID detection for seamless order lookup.

Business Logic Integration:
Connected to mock product/order databases with real-world responses.
Dynamic responses for shipping, warranties, and compatibility.

📈 Why This Matters
Scalable: Designed for easy model swapping/retraining.
Real-World Ready: Handles edge cases (low-confidence queries, order tracking).

💡 Lessons Learned
Balancing model complexity vs. interpretability for business use.
The importance of context persistence in multi-turn conversations.
FastAPI’s for serving ML models.

hashtag#MachineLearning hashtag#AI hashtag#Chatbots hashtag#NLP hashtag#PyTorch hashtag#FastAPI hashtag#Python hashtag#MLE hashtag#ArtificialIntelligence hashtag#TechForBusiness #codewithhafiz