architecture overview for afstemming + reference

Architecture_Overview.py

@@ -0,0 +1,127 @@
+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"""
+    # Interview Analysis Pipeline Architecture
+
+    **Project Goal:** Synthesize insights from 26 stakeholder interviews into a unified report.
+
+    **Input:** 26 Interview Transcripts (`.srt`)
+    **Output:** Comprehensive Qualitative Analysis Report
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## High-Level Workflow
+
+    The analysis follows a structured **3-Stage Pipeline** to ensure consistency across all interviews while leveraging the reasoning capabilities of Large Language Models (LLMs).
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Stage 1: Discovery (Theme Definition)
+
+    **Goal:** Establish the "Rules of the Game" to ensure consistent analysis.
+
+    1.  **Input:** A representative sample of 4-5 interviews.
+    2.  **Process:**
+        *   Exploratory analysis to identify recurring topics.
+        *   Grouping topics into **Themes**.
+        *   Defining the **"Other"** category for emerging insights that don't fit established themes.
+    3.  **Output:** `master_codebook.json`
+        *   Contains Theme Names, Definitions, and Color Codes.
+        *   Serves as the strict instruction set for the AI in Stage 2.
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.mermaid("""
+    graph TD
+        A[Raw Transcripts] -->|Sample 4-5| B(Stage 1: Discovery)
+        B -->|Generate| C[Master Codebook]
+        C -->|Input| D(Stage 2: Theme Coding)
+        A -->|All 26 Files| D
+        D -->|Extract| E[Structured Dataset]
+        E -->|Aggregate| F(Stage 3: Synthesis)
+        F -->|Generate| G[Final Report]
+
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Stage 2: Structured Theme Coding (Extraction)
+
+    **Goal:** Convert unstructured text into a structured dataset.
+
+    1.  **Input:** All 26 Transcripts + `master_codebook.json`.
+    2.  **Process:**
+        *   The LLM analyzes each transcript segment-by-segment.
+        *   It extracts specific quotes that match a Theme Definition.
+        *   **Granular Sentiment Analysis:** For each quote, the model identifies:
+            *   **Subject:** The specific topic/object being discussed (e.g., "Login Flow", "Brand Tone").
+            *   **Sentiment:** Positive / Neutral / Negative.
+    3.  **Output:** `coded_segments.csv`
+        *   Columns: `Source_File`, `Speaker`, `Theme`, `Quote`, `Subject`, `Sentiment`, `Context`.
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Stage 3: Synthesis & Reporting
+
+    **Goal:** Derive conclusions from the aggregated data.
+
+    1.  **Input:** `coded_segments.csv` (The consolidated dataset).
+    2.  **Process:**
+        *   **Theme Synthesis:** All quotes for "Theme A" are analyzed together to find patterns, contradictions, and consensus.
+        *   **"Other" Review:** The "Other" category is manually or computationally reviewed to identify missed signals.
+        *   **Global Synthesis:** Cross-theme analysis to build the final narrative.
+    3.  **Output:** Final Report
+        *   Executive Summary
+        *   Theme-by-Theme Deep Dives (with supporting quotes)
+        *   Strategic Recommendations
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Technical Infrastructure
+
+    | Component | Specification | Role |
+    |-----------|---------------|------|
+    | **Model** | `llama3.3:70b` | Primary reasoning engine (128k context) |
+    | **Compute** | NVIDIA H100 (80GB) | High-performance inference |
+    | **Orchestration** | Python + Marimo | Pipeline management and UI |
+    | **Storage** | Local JSON/CSV | Data persistence |
+    """)
+    return
+
+
+if __name__ == "__main__":
+    app.run()

Interviews_Transcription.py

@@ -15,7 +15,7 @@ def _(mo):
     mo.md(r"""
     # Interview Audio Transcription
 
-    Use Whisper-Webui: http://whisper-webui.tail44fa00.ts.net:7860
+    Use Whisper-Webui: http://whisper-webui-h100.tail44fa00.ts.net:7860
     """)
     return
 

Model_Selection_Reference.py

@@ -0,0 +1,153 @@
+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"""
+    # LLM Model Selection Reference
+
+    A reference guide for choosing models for interview transcript thematic analysis.
+
+    ---
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Infrastructure
+
+    | Resource | Specification |
+    |----------|---------------|
+    | **GPU** | NVIDIA H100 (80GB VRAM) |
+    | **VM** | `hiperf-gpu` via Tailscale |
+    | **API** | Ollama Python client |
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Recommended Models for Thematic Analysis
+
+    ### Primary Recommendation: `llama3.3:70b`
+
+    | Aspect | Value |
+    |--------|-------|
+    | **Context Window** | 128K tokens |
+    | **VRAM Usage** | ~45GB |
+    | **Architecture** | Dense (70B always active) |
+    | **Strengths** | Excellent instruction following, proven reliability, great for long documents |
+
+    ### Alternatives
+
+    | Model | Context | VRAM | Best For |
+    |-------|---------|------|----------|
+    | `qwen3:30b` | 256K | ~19GB | Fast iteration, huge context window |
+    | `qwen3:32b` | 40K | ~20GB | Balance of speed and quality |
+    | `qwen3:235b` | 256K | ~142GB (needs quantization) | Maximum quality (MoE: 22B active) |
+    | `deepseek-r1:70b` | 64K | ~45GB | Reasoning transparency (shows thinking) |
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Context Window Considerations
+
+    ### For 1-Hour Interview Transcripts
+
+    - **Estimated size**: ~8,000-10,000 tokens
+    - **Requirement**: Any model with 32K+ context is sufficient
+    - **Recommendation**: `llama3.3:70b` (128K) handles full transcripts easily
+
+    ### When Larger Context Helps ✅
+
+    - Full document fits without chunking
+    - Model can connect themes across entire transcript
+    - Simpler preprocessing pipeline
+
+    ### When Larger Context Can Hurt ⚠️
+
+    | Issue | Explanation |
+    |-------|-------------|
+    | **"Lost in the middle"** | LLMs focus on beginning/end, lose attention to middle |
+    | **Slower inference** | Attention scales quadratically with length |
+    | **Diluted attention** | Key info gets drowned by less relevant content |
+
+    ### Key Insight
+
+    Research shows models often perform **worse** with very long contexts vs. strategically selected shorter contexts. For ~10K token transcripts, **context window size doesn't matter** — choose based on model quality and speed.
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Document Chunking
+
+    ### When You Need Chunking
+
+    | Model Context | 30-min Transcript (~5K tokens) | 1-hour Transcript (~10K tokens) |
+    |---------------|-------------------------------|--------------------------------|
+    | 4K-8K (7B models) | ⚠️ May need chunking | ❌ Needs chunking |
+    | 32K-40K | ✅ Fits | ✅ Fits |
+    | 128K+ | ✅ Fits easily | ✅ Fits easily |
+
+    ### Chunking Strategies (if needed)
+
+    1. **By speaker turns** — Split at natural conversation boundaries
+    2. **By time segments** — 10-15 minute chunks
+    3. **By token count** — Fixed size with overlap
+    4. **Hierarchical** — Summarize chunks, then analyze summaries
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Model Comparison Summary
+
+    ```
+    Quality:     qwen3:235b > llama3.3:70b ≈ qwen3:30b > qwen3:32b
+    Speed:       qwen3:30b > qwen3:32b > llama3.3:70b > qwen3:235b
+    Context:     qwen3:235b (256K) > qwen3:30b (256K) > llama3.3:70b (128K) > qwen3:32b (40K)
+    ```
+
+    ### Final Recommendation
+
+    **Use `llama3.3:70b`** for this project:
+    - 128K context is more than sufficient for 1-hour transcripts
+    - Excellent quality for thematic analysis
+    - Well-tested and reliable
+    - Good balance of speed and quality on H100
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ---
+
+    *Last updated: December 2025*
+    """)
+    return
+
+
+if __name__ == "__main__":
+    app.run()

utils.py

@@ -6,7 +6,6 @@ import re
 from pathlib import Path
 
 import requests
-import ollama
 from ollama import Client