Daily Hugging Face Trending AI Models Digest to Telegram via Google Gemini

id: ai-huggingface-trending-models-gemini-telegram
namespace: company.team

triggers:
  - id: daily_digest_schedule
    type: io.kestra.plugin.core.trigger.Schedule
    cron: "0 8 * * *"
    disabled: true

tasks:
  - id: fetch_trending_models
    type: io.kestra.plugin.core.http.Request
    description: "Pull the five most-liked models of the past seven days from the
      public Hugging Face API."
    uri: "https://huggingface.co/api/models?sort=likes7d&direction=-1&limit=5"
    method: GET

  - id: analyze_models_with_gemini
    type: io.kestra.plugin.gemini.StructuredOutputCompletion
    description: "Send the raw model metadata to Gemini and get back a structured
      developer briefing with a ready-to-post Telegram message."
    apiKey: "{{ secret('GEMINI_API_KEY') }}"
    model: "gemini-3.5-flash-lite"
    prompt: |
      You are an AI research analyst. Analyze the following list of top trending open-source AI models from Hugging Face.

      For each model produce:
      - **Model name** and author
      - One-line summary of what it does
      - Primary architecture (e.g. Transformer, Diffusion, MoE)
      - Why it is trending this week

      Format the output as a clean Telegram-compatible Markdown message with bullet points.

      Raw models payload:
      {{ outputs.fetch_trending_models.body }}
    jsonResponseSchema: |
      {
        "type": "object",
        "properties": {
          "digest_date": {
            "type": "string",
            "description": "ISO date of the digest"
          },
          "model_count": {
            "type": "integer",
            "description": "Number of models analyzed"
          },
          "top_highlights": {
            "type": "string",
            "description": "One-paragraph executive summary of this week's trends"
          },
          "formatted_telegram_message": {
            "type": "string",
            "description": "Full Telegram-ready Markdown message with per-model bullet points"
          }
        },
        "required": ["formatted_telegram_message", "top_highlights"]
      }

  - id: verify_and_notify
    type: io.kestra.plugin.core.flow.If
    description: "Gate the notification: only post to Telegram when Gemini returned
      a valid digest."
    condition: "{{ outputs.analyze_models_with_gemini.predictions | first |
      jq('.formatted_telegram_message') | first != null }}"
    then:
      - id: send_telegram_digest
        type: io.kestra.plugin.telegram.TelegramSend
        description: "Post the formatted AI digest to the configured Telegram channel."
        channel: "{{ secret('TELEGRAM_CHANNEL_ID') }}"
        token: "{{ secret('TELEGRAM_BOT_TOKEN') }}"
        payload: |
          *Hugging Face Trending AI Models, Daily Digest*

          {{ outputs.analyze_models_with_gemini.predictions | first | jq('.top_highlights') | first }}

          {{ outputs.analyze_models_with_gemini.predictions | first | jq('.formatted_telegram_message') | first }}

          _Powered by Kestra + Google Gemini_

      - id: log_success
        type: io.kestra.plugin.core.log.Log
        message: "Digest sent to Telegram for execution {{ execution.id }}."
    else:
      - id: log_skip
        type: io.kestra.plugin.core.log.Log
        message: "Gemini returned no digest; Telegram notification skipped."