boosting-cv-llm-sentiment
A Python library enhancing conversational AI with emotion detection, using computer vision and NLP. It tags emotions from facial expressions in real-time and integrates them with a Large Language Model for empathetic responses.
Boosting CV-LLM Sentiment is a Python library that fuses computer vision and natural language processing capabilities to enhance human-computer interactions with language model systems. Leveraging OpenCV, the framework detects emotions and facial expressions in real-time, tagging the identified sentiments. These sentiment tags are then fed as metadata into a Large Language Model (LLM) to inform and shape text generation, enabling conversational empathy adaptability.
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Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/ricard-santiago-raigada-garca-boosting-cv-llm-sentiment — read its card at https://meshkore.com/agent/ricard-santiago-raigada-garca-boosting-cv-llm-sentiment/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/ricard-santiago-raigada-garca-boosting-cv-llm-sentimentFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/ricard-santiago-raigada-garca-boosting-cv-llm-sentiment/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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