Started on looping through all interviews

This reverts commit b21f402e1e.

Architecture_Overview.py

@@ -82,7 +82,6 @@ def _(mo):
     2.  **Process:**
         *   The LLM analyzes each transcript segment-by-segment.
         *   It extracts specific quotes that match a Theme Definition.
-        *   **Multi-Theme Handling:** If a quote applies to multiple themes, it is tagged with *all* relevant themes. In the dataset, this creates multiple entries (one per theme) so the quote informs the synthesis of each relevant topic.
         *   **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.

Thematic_Analysis.py

@@ -13,8 +13,8 @@ def _():
     VM_NAME = 'hiperf-gpu'
     MODEL = 'llama3.3:70b'
 
-    client = connect_qumo_ollama(VM_NAME)
-    return MODEL, Path, client, load_srt, mo
+    #client = connect_qumo_ollama(VM_NAME)
+    return MODEL, Path, load_srt, mo
 
 
 @app.cell(hide_code=True)
@@ -186,6 +186,316 @@ def _(mo):
     return
 
 
+@app.cell
+def _(mo):
+    # Step 3a: Define themes for labelling
+    themes_input = mo.ui.text_area(
+        value="""brand voice and tone
+    customer experience priorities
+    design system and consistency
+    AI and conversational interfaces""",
+        label="Themes (one per line)",
+        full_width=True,
+        rows=6,
+    )
+
+    mo.md("""### Step 3a: Define Themes
+
+    Enter one theme per line. These will be used to
+    label each interview transcript. Themes may overlap; the
+    same section can relate to multiple themes.
+    """)
+
+    themes_input
+    return (themes_input,)
+
+
+@app.cell
+def _(themes_input):
+    # Parse themes into a clean Python list
+    raw_lines = themes_input.value.splitlines() if themes_input.value and themes_input.value else []
+    theme_list = [t.strip() for t in raw_lines if t.strip()]
+    return (theme_list,)
+
+
+@app.cell
+def _(Path, mo):
+    # Configuration for JSON output directory
+    OUTPUT_DIR = Path("data/labels")
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+    mo.md(f"""### Step 3b: LLM-based Theme Labelling
+
+    This step runs an LLM over the current interview transcript
+    for each defined theme and saves one JSON file per theme
+    for this interview in `{OUTPUT_DIR}`.
+
+    For each theme, the model will return full sections of the
+    conversation (multi-sentence chunks, not just short quotes)
+    that are about that theme.
+    """)
+
+    label_button = mo.ui.run_button(label="Run Theme Labelling for This Interview")
+    label_button
+    return
+
+
+@app.cell
+def _(
+    MODEL,
+    OUTPUT_THEME_DIR,
+    Path,
+    client,
+    file_dropdown,
+    labeled_transcript,
+    mo,
+    theme_label_button,
+    theme_list,
+):
+    import json
+    from datetime import datetime
+
+    theme_label_results = {}
+
+    if theme_label_button.value and file_dropdown.value and theme_list:
+        interview_id = Path(file_dropdown.value).stem
+
+        for theme in theme_list:
+            prompt = f"""You are an expert qualitative researcher.
+
+            You will analyse a single interview transcript for ONE specific theme.
+
+            Theme: "{theme}"
+
+            Tasks:
+            1. Decide if the theme is present in this interview.
+            2. If present, estimate how relevant it is on a 0–1 scale
+            where 0 = not mentioned, 0.5 = moderately important,
+            1 = central theme of the interview.
+            3. Identify all sections of the conversation that are
+            primarily about this theme. A section can span multiple
+            consecutive utterances and should form a coherent piece
+            of the dialogue about the theme, not just a single
+            sentence.
+
+            Each section should include:
+            - the dominant speaker label (or "mixed" if multiple)
+            - the full section text (one or more sentences)
+
+            Return your answer ONLY as a JSON object with this schema:
+            {{
+            "theme": string,                  // the theme name
+            "present": bool,                  // whether the theme appears
+            "relevance": float,               // 0.0–1.0
+                "sections": [
+                    {{
+                        "speaker": string,           // main speaker label for the section
+                        "section_text": string       // full section text about the theme
+                    }}
+                ]
+            }}
+
+            Transcript:
+            """
+            {labeled_transcript}
+            """
+            """
+
+            response = client.generate(model=MODEL, prompt=prompt)
+            raw_text = response.response.strip()
+
+            try:
+                parsed = json.loads(raw_text)
+            except json.JSONDecodeError:
+                # Fallback: try to extract JSON between braces
+                try:
+                    start = raw_text.index("{")
+                    end = raw_text.rindex("}") + 1
+                    parsed = json.loads(raw_text[start:end])
+                except Exception:
+                    parsed = {
+                        "theme": theme,
+                        "present": False,
+                        "relevance": 0.0,
+                        "sections": [],
+                        "_parse_error": True,
+                        "_raw": raw_text,
+                    }
+
+            # Normalise fields
+            parsed["theme"] = parsed.get("theme", theme)
+            parsed["present"] = bool(parsed.get("present", False))
+            try:
+                parsed["relevance"] = float(parsed.get("relevance", 0.0))
+            except (TypeError, ValueError):
+                parsed["relevance"] = 0.0
+            if not isinstance(parsed.get("sections"), list):
+                parsed["sections"] = []
+
+            theme_label_results[theme] = parsed
+
+            # Write per-interview-per-theme JSON file
+            out_path = OUTPUT_THEME_DIR / f"{interview_id}__{theme.replace(' ', '_')}.json"
+            out_data = {
+                "interview_id": interview_id,
+                "theme": parsed["theme"],
+                "present": parsed["present"],
+                "relevance": parsed["relevance"],
+                "sections": parsed["sections"],
+                "generated_at": datetime.utcnow().isoformat() + "Z",
+            }
+            out_path.write_text(json.dumps(out_data, ensure_ascii=False, indent=2), encoding="utf-8")
+
+    if theme_label_button.value:
+        if not file_dropdown.value:
+            status = "No transcript selected."
+        elif not theme_list:
+            status = "No themes defined. Please add at least one theme."
+        else:
+            status = f"Labelled {len(theme_label_results)} themes for current interview. JSON files written to '{OUTPUT_THEME_DIR}'."
+    else:
+        status = "Click 'Run Theme Labelling for This Interview' to start."
+
+    mo.md(f"""### Theme Labelling Status
+
+    {status}
+    """)
+    return
+
+
+@app.cell
+def _(Path, mo):
+    # Step 3c: Load all labeled transcripts (assumed precomputed)
+    LABELED_DIR = Path("data/labeled_transcripts")
+    LABELED_DIR.mkdir(parents=True, exist_ok=True)
+
+    labeled_files = sorted(LABELED_DIR.glob("*.json"))
+
+    mo.md(f"""### Step 3c: Use Pre-Labeled Transcripts
+
+    Found **{len(labeled_files)}** labeled transcript files in `{LABELED_DIR}`.
+    These will be used to aggregate themes across all interviews.
+    """)
+
+    labeled_files
+    return (labeled_files,)
+
+
+@app.cell
+def _(labeled_files):
+    import json
+
+    all_labeled_records = []
+    for f in labeled_files:
+        try:
+            data = json.loads(f.read_text(encoding="utf-8"))
+        except Exception:
+            # Skip unreadable files
+            continue
+
+        interview_id = data.get("interview_id") or f.stem.split("__", 1)[0]
+        theme = data.get("theme", "")
+        present = bool(data.get("present", False))
+        try:
+            relevance = float(data.get("relevance", 0.0))
+        except (TypeError, ValueError):
+            relevance = 0.0
+        sections = data.get("sections") or []
+
+        all_labeled_records.append(
+            {
+                "interview_id": interview_id,
+                "theme": theme,
+                "present": present,
+                "relevance": relevance,
+                "sections": sections,
+            }
+        )
+    return (all_labeled_records,)
+
+
+@app.cell
+def _(all_labeled_records, mo):
+    # Derive full theme and interview sets
+    all_themes = sorted({r["theme"] for r in all_labeled_records if r["theme"]})
+    all_interviews = sorted({r["interview_id"] for r in all_labeled_records})
+
+    theme_selector = mo.ui.dropdown(
+        options={t: t for t in all_themes},
+        label="Select theme to explore across all interviews",
+    )
+
+    mo.md("### Step 3d: Explore Themes Across All Labeled Transcripts")
+    theme_selector
+    return all_interviews, theme_selector
+
+
+@app.cell
+def _(all_interviews, all_labeled_records, mo, theme_selector):
+    import statistics
+
+    selected_theme = theme_selector.value
+    theme_summary = {}
+    theme_sections = []
+
+    if selected_theme:
+        theme_records = [
+            r for r in all_labeled_records if r["theme"] == selected_theme
+        ]
+
+        present_flags = [r["present"] for r in theme_records]
+        relevances = [r["relevance"] for r in theme_records if r["present"]]
+
+        theme_summary = {
+            "theme": selected_theme,
+            "num_interviews": len(all_interviews),
+            "num_interviews_with_theme": sum(present_flags),
+            "share_of_interviews_with_theme": (
+                sum(present_flags) / len(all_interviews) if all_interviews else 0.0
+            ),
+            "avg_relevance_if_present": (
+                statistics.mean(relevances) if relevances else 0.0
+            ),
+        }
+
+        for r in theme_records:
+            interview_id = r["interview_id"]
+            for s in r["sections"]:
+                theme_sections.append(
+                    {
+                        "interview_id": interview_id,
+                        "speaker": s.get("speaker", ""),
+                        "section_text": s.get("section_text", ""),
+                        "relevance": r["relevance"],
+                    }
+                )
+
+    mo.md(
+        f"""#### Theme Overview: `{selected_theme or "None selected"}`
+
+    - Total interviews: **{len(all_interviews)}**
+    - Interviews where theme is present: **{theme_summary.get("num_interviews_with_theme", 0)}**
+    - Share of interviews with theme: **{theme_summary.get("share_of_interviews_with_theme", 0.0):.2f}**
+    - Avg. relevance (when present): **{theme_summary.get("avg_relevance_if_present", 0.0):.2f}**
+    """
+    )
+
+    if theme_sections:
+        table_rows = [
+            {
+                "Interview": s["interview_id"],
+                "Speaker": s["speaker"],
+                "Relevance": f"{s['relevance']:.2f}",
+                "Section": s["section_text"],
+            }
+            for s in theme_sections
+        ]
+        mo.ui.table(table_rows)
+    else:
+        mo.md("_No sections for this theme yet._")
+    return
+
+
 @app.cell
 def _(mo):
     # Editable analysis task prompt