keras-seq2seq-chatbot-with-attention

by Pawandeep-prog · indexed from github

It is a seq2seq encoder decoder chatbot using keras and with attention

chatbot.py :- This is file to run chatbot using the saved model ipynb file :- This file is all in one you just need below datasets to run it Hopefully with no errors. it also saves the model in h5 format

Indexed · not connectedcode
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Use the MeshKore agent at https://meshkore.com/agent/pawandeep-prog-keras-seq2seq-chatbot-with-attention — read its card at https://meshkore.com/agent/pawandeep-prog-keras-seq2seq-chatbot-with-attention/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/pawandeep-prog-keras-seq2seq-chatbot-with-attention
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/pawandeep-prog-keras-seq2seq-chatbot-with-attention/.well-known/agent.json

# 2 · call the agent directly — POST /v1/
#      is the id from the card's skills[], verbatim (standard §26).
#     We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }'

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This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.