import marimo

__generated_with = "0.18.0"
app = marimo.App(width="medium")


@app.cell
def _():
    import marimo as mo
    return (mo,)


@app.cell(hide_code=True)
def _(mo):
    mo.md(r"""
    # Sentiment & Thematic Analysis of Interviews Using LLMs

    ## ✅ Step 1: Transcribe Audio Interviews
    - Use a high-quality speech-to-text model:
      - [OpenAI Whisper](https://github.com/openaihttps://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text context

    ---

    ## ✅ Step 2: Preprocess Text
    - Clean transcripts:
      - Remove filler words
      - Normalize punctuation
    - Segment by:
      - **Survey question triggers**
      - **Brand character mentions**

    ---

    ## ✅ Step 3: Combine Survey Data
    - Use survey responses as **metadata**:
      - Link each interview segment to corresponding survey answers
      - Helps LLM understand context (e.g., "This person rated Brand A as 'trustworthy' but said X in the interview")

    ---

    ## ✅ Step 4: Use LLM for Sentiment + Thematic Analysis

    ### **A. Sentiment Analysis**
    - Define **custom sentiment dimensions** relevant to brand characters:
      - Trustworthiness
      - Friendliness
      - Professionalism
      - Authenticity
    - Prompt the LLM with **few-shot examples**:
      - Show examples of text and classification for each dimension
    - Example output format:
    ```json
    {
      "brand_character": "Brand A",
      "voice": "Friendly",
      "sentiment": {
        "trustworthiness": "positive",
        "friendliness": "neutral",
        "professionalism": "negative"
      },
      "key_quotes": ["I felt it was too casual for a serious brand."]
    }
    """)
    return


@app.cell
def _(mo):
    mo.md(r"""
    # Findings from Foundational Research Report

    ## Brand character

    ### Brand tone (Foundation research delivery v1 for more details) (we need to provide the LLM with definitions of these attributes)
    - Confident
    - Progressive
    - Clear
    - Intentional

    ### Six CDA brand character personalities
    - The bank teller: patient, grounded, down-to-earth, knowledgable, stable, steady, balanced, competent
    - The familiar friend: warm, friendly, approachable, familiar, casual, appreciative, benevolent
    - The coach: empowering, encouraging, caring, positive, optimistic, guiding, reassuring
    - The personal assistant: proactive, progressive, cooperative, intentional, deliberate, resourceful, attentive adaptive
    - The engineer: clear, modest, savvy, plainspoken, straight forward, direct, practical, transparent
    - The counselor: confident, calm, reliable, dependable, respectable, reassuring, upright

    ### Personality model alternative dimensions (dimensions which were used to defined the six characters)
    - Approachable: friendly, warm, welcoming
    - Social-entertaining: humorous, playful, engaging
    - Social-inclined: eager to converse, talkative, socially oriented
    - Social assisting: supportive, empathetic, encouraging
    - Self-conscious: cautious, modest, hesitant
    - Artifical: robotic, mechanical, lacking human-like warmth

    ## Voice
    """)
    return


if __name__ == "__main__":
    app.run()