Added taguette to uv

This commit is contained in:
mtorsij
2025-12-06 11:12:38 +01:00
parent 98202ac3f2
commit 523a59f864
3 changed files with 668 additions and 0 deletions

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@@ -0,0 +1,212 @@
import marimo
__generated_with = "0.18.0"
app = marimo.App(width="medium")
@app.cell
def _():
import marimo as mo
import json
import pandas as pd
from pathlib import Path
from utils import connect_qumo_ollama, load_srt
# Configuration
CODEBOOK_PATH = Path("data/labels/master_codebook.json")
TRANSCRIPT_DIR = Path("data/transcripts")
OUTPUT_DIR = Path("data/labeled_transcripts")
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
# Initialize LLM Client
client = connect_qumo_ollama("hiperf-gpu")
return (
CODEBOOK_PATH,
OUTPUT_DIR,
Path,
TRANSCRIPT_DIR,
client,
connect_qumo_ollama,
json,
load_srt,
mo,
pd,
)
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
# Stage 2: Structured Theme Coding
**Goal:** Extract specific quotes for defined themes from full transcripts.
""")
return
@app.cell
def _(CODEBOOK_PATH, json, mo):
# Load Codebook
if CODEBOOK_PATH.exists():
with open(CODEBOOK_PATH, "r") as f:
codebook = json.load(f)
else:
codebook = []
mo.md(f"**Loaded {len(codebook)} Themes from Codebook**")
return codebook, f
@app.cell
def _(TRANSCRIPT_DIR, mo):
# File Selector
srt_files = list(TRANSCRIPT_DIR.glob("*.srt"))
file_dropdown = mo.ui.dropdown(
options={f.name: str(f) for f in srt_files},
label="Select Transcript"
)
file_dropdown
return file_dropdown, srt_files
@app.cell
def _(mo):
run_btn = mo.ui.run_button(label="Start Analysis")
run_btn
return (run_btn,)
@app.cell
def _(
OUTPUT_DIR,
client,
codebook,
file_dropdown,
json,
load_srt,
mo,
pd,
run_btn,
):
# Analysis Logic
results = []
status_message = mo.md("")
if run_btn.value and file_dropdown.value:
transcript_path = file_dropdown.value
transcript_name = file_dropdown.selected_key.replace(".srt", "")
transcript_text = load_srt(transcript_path)
with mo.status.progress_bar(codebook, title="Analyzing Themes") as bar:
for theme in bar:
theme_name = theme["name"]
theme_def = theme["definition"]
prompt = f"""
You are a qualitative data analyst. Analyze the following transcript for the theme: "{theme_name}".
Definition: {theme_def}
Extract ALL relevant quotes that match this definition.
For each quote, identify the specific Subject and the Sentiment (Positive, Neutral, Negative).
Return ONLY a JSON array of objects with these keys: "quote", "subject", "sentiment".
If no quotes are found, return an empty array [].
Transcript:
{transcript_text}
"""
try:
response = client.generate(model="llama3.3:70b", prompt=prompt, format="json")
content = response.get("response", "[]")
extracted = json.loads(content)
# Add metadata
for item in extracted:
item["theme"] = theme_name
item["source_file"] = transcript_name
results.append(item)
except Exception as e:
print(f"Error processing theme {theme_name}: {e}")
# "Other" Category Analysis (Negative Constraint Strategy)
if results or codebook: # Proceed if we have themes to exclude
status_message = mo.md("🔍 Analyzing for 'Other' emerging themes...")
# Format existing themes for exclusion
existing_themes_text = "\n".join([f"- {t['name']}: {t['definition']}" for t in codebook])
other_prompt = f"""
You are a qualitative data analyst.
Your goal is to identify "Emerging Themes" in the transcript that have NOT been captured by our existing codebook.
### EXISTING THEMES (IGNORE THESE)
We have already analyzed the transcript for the following themes. DO NOT extract quotes that primarily fit these definitions:
{existing_themes_text}
### INSTRUCTIONS
1. Analyze the transcript below.
2. Identify significant quotes, insights, or patterns that are distinct from the "Existing Themes" listed above.
3. Label these findings as "Other".
4. If a quote is borderline, only include it if it offers a novel angle not covered by the existing definition.
Return ONLY a JSON array of objects with these keys: "quote", "subject", "sentiment".
If no new insights are found, return an empty array [].
### TRANSCRIPT
{transcript_text}
"""
try:
response = client.generate(model="llama3.3:70b", prompt=other_prompt, format="json")
content = response.get("response", "[]")
extracted_other = json.loads(content)
for item in extracted_other:
item["theme"] = "Other"
item["source_file"] = transcript_name
results.append(item)
except Exception as e:
print(f"Error processing 'Other' theme: {e}")
# Save Results
if results:
df = pd.DataFrame(results)
output_path = OUTPUT_DIR / f"{transcript_name}_coded.csv"
df.to_csv(output_path, index=False)
status_message = mo.md(f"✅ Analysis Complete! Saved to `{output_path}`")
else:
status_message = mo.md("⚠️ No quotes found for any theme.")
df = pd.DataFrame()
elif run_btn.value and not file_dropdown.value:
status_message = mo.md("⚠️ Please select a transcript first.")
df = pd.DataFrame()
else:
df = pd.DataFrame()
mo.vstack([status_message, mo.ui.table(df)])
return (
bar,
content,
df,
extracted,
item,
output_path,
prompt,
response,
results,
status_message,
theme,
theme_def,
theme_name,
transcript_name,
transcript_path,
transcript_text,
)
if __name__ == "__main__":
app.run()

View File

@@ -10,4 +10,5 @@ dependencies = [
"ollama>=0.6.1",
"pyzmq>=27.1.0",
"requests>=2.32.5",
"taguette>=1.5.1",
]

455
uv.lock generated
View File

@@ -2,6 +2,19 @@ version = 1
revision = 3
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