274 lines
6.7 KiB
Python
274 lines
6.7 KiB
Python
import marimo
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__generated_with = "0.18.0"
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app = marimo.App(width="medium")
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@app.cell
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def _():
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import marimo as mo
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from pathlib import Path
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from utils import connect_qumo_ollama, load_srt
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VM_NAME = 'hiperf-gpu'
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MODEL = 'llama3.3:70b'
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client = connect_qumo_ollama(VM_NAME)
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return MODEL, Path, client, load_srt, mo
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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# Interview Transcript Thematic Analysis
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This notebook loads interview transcripts (SRT files) and runs thematic analysis using LLMs.
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""")
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return
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@app.cell
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def _(Path, mo):
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# Load transcript from SRT file
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TRANSCRIPT_DIR = Path("data/transcripts")
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srt_files = list(TRANSCRIPT_DIR.glob("*.srt"))
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# File selector
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file_dropdown = mo.ui.dropdown(
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options={f.name: str(f) for f in srt_files},
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label="Select transcript file"
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)
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file_dropdown
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return (file_dropdown,)
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@app.cell
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def _(file_dropdown, load_srt, mo):
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# Load and display transcript preview
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transcript_raw = ""
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if file_dropdown.value:
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transcript_raw = load_srt(file_dropdown.value)
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mo.md(f"""
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## Transcript Preview
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**File:** `{file_dropdown.value or 'None selected'}`
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**Length:** {len(transcript_raw)} characters, ~{len(transcript_raw.split())} words
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<details>
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<summary>Show first 2000 characters</summary>
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```
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{transcript_raw[:2000]}...
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```
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</details>
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""")
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return (transcript_raw,)
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Step 1: Infer Speaker Roles
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The model will analyze the transcript to identify who is the interviewer and who is the interviewee.
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""")
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return
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@app.cell
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def _(mo, transcript_raw):
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# Infer speaker roles from transcript context
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role_inference_prompt = f"""Analyze this interview transcript and identify the role of each speaker.
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Based on the conversation context, determine who is:
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- The interviewer(s) - asking questions, guiding the conversation
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- The interviewee(s) - providing answers, sharing expertise/opinions
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Return ONLY a simple mapping in this exact format (one per line):
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SPEAKER_XX: Role - Brief description
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For example:
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SPEAKER_00: Interviewer - Michael from the voice branding team
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SPEAKER_01: Interviewee - Head of Digital Design
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<transcript>
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{transcript_raw[:4000]}
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</transcript>
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"""
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infer_roles_button = mo.ui.run_button(label="Infer Speaker Roles")
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infer_roles_button
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return infer_roles_button, role_inference_prompt
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@app.cell
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def _(MODEL, client, infer_roles_button, mo, role_inference_prompt):
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inferred_roles_text = ""
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if infer_roles_button.value:
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response = client.generate(model=MODEL, prompt=role_inference_prompt)
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inferred_roles_text = response.response
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mo.md(f"""
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### Inferred Roles
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{inferred_roles_text if inferred_roles_text else "_Click 'Infer Speaker Roles' to analyze the transcript_"}
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""")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Step 2: Confirm or Edit Speaker Roles
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Review the inferred roles below and make corrections if needed.
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""")
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return
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@app.cell
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def _(mo, transcript_raw):
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import re
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# Extract unique speakers from transcript
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speakers = sorted(set(re.findall(r'(SPEAKER_\d+):', transcript_raw)))
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# Create editable text inputs for each speaker
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role_inputs = {
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speaker: mo.ui.text(
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value=f"{speaker}",
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label=speaker,
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full_width=True
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)
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for speaker in speakers
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}
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mo.md("### Edit Speaker Labels\n\nEnter the name/role for each speaker:")
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return (role_inputs,)
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@app.cell
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def _(mo, role_inputs):
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# Display role inputs as a form
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mo.vstack([role_inputs[k] for k in sorted(role_inputs.keys())])
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return
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@app.cell
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def _(mo, role_inputs, transcript_raw):
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# Apply role labels to transcript
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labeled_transcript = transcript_raw
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for speaker_id, input_widget in role_inputs.items():
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if input_widget.value and input_widget.value != speaker_id:
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labeled_transcript = labeled_transcript.replace(f"{speaker_id}:", f"{input_widget.value}:")
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# Build role mapping summary
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role_mapping = "\n".join([
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f"- {speaker_id} → {input_widget.value}"
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for speaker_id, input_widget in sorted(role_inputs.items())
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])
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mo.md(f"""
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### Role Mapping Applied
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{role_mapping}
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""")
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return labeled_transcript, role_mapping
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Step 3: Thematic Analysis
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Configure your analysis task and run the thematic analysis.
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""")
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return
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@app.cell
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def _(mo):
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# Editable analysis task prompt
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analysis_task_input = mo.ui.text_area(
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value="""Perform a thematic analysis of this interview transcript.
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Identify and describe:
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1. **Key Themes** - Major topics and ideas that emerge from the conversation
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2. **Supporting Quotes** - Direct quotes that exemplify each theme (include speaker attribution)
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3. **Insights** - Notable observations or implications from the discussion
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Focus on themes related to:
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- Brand voice and tone strategy
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- Customer experience priorities
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- Design system and consistency
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- AI/conversational interface considerations""",
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label="Analysis Task",
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full_width=True,
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rows=12
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)
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analysis_task_input
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return (analysis_task_input,)
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@app.cell
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def _(analysis_task_input, labeled_transcript, mo, role_mapping):
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# Build full analysis prompt
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full_analysis_prompt = f"""You are an expert qualitative researcher specializing in thematic analysis of interview data.
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## Speaker Roles
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{role_mapping}
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## Task
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{analysis_task_input.value}
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## Interview Transcript
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'''
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<transcript>
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{labeled_transcript}
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</transcript>
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'''
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Provide your analysis in well-structured markdown format."""
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run_analysis_button = mo.ui.run_button(label="Run Thematic Analysis")
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mo.vstack([
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mo.md(f"**Prompt length:** ~{len(full_analysis_prompt.split())} words"),
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run_analysis_button
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])
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return full_analysis_prompt, run_analysis_button
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@app.cell
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def _(full_analysis_prompt, mo):
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mo.md(rf"""
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# Full Analysis Prompt
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---
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{full_analysis_prompt}
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""")
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return
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@app.cell
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def _(MODEL, client, full_analysis_prompt, mo, run_analysis_button):
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analysis_response = ""
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if run_analysis_button.value:
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response_2 = client.generate(model=MODEL, prompt=full_analysis_prompt)
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analysis_response = response_2.response
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mo.md(f"""
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## Analysis Results
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{analysis_response if analysis_response else "_Click 'Run Thematic Analysis' to generate analysis_"}
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""")
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return
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if __name__ == "__main__":
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app.run()
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