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7f9dfed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | from __future__ import annotations
from functools import partial
import gradio as gr
from core.app_state import APP_STATE
from core.events import Event, EventType
from datasets.field_notes import FieldNote, FieldNoteStore
from datasets.ocr import (
export_corrected_ocr_notes,
import_uncertain_predictions,
load_ocr_predictions,
ocr_import_summary,
)
from models.model_catalog import ModelInfo
from ui.progress import CLICK_PROGRESS
def _export_notes(store: FieldNoteStore) -> str:
path = store.export_jsonl()
return f"Exported corrected notes to {path}"
def _export_dataset(store: FieldNoteStore) -> str:
path = store.export_hf_dataset()
return f"Exported local HF Dataset files to {path}"
def _export_ocr_notes(store: FieldNoteStore) -> str:
path = export_corrected_ocr_notes(store)
return f"Exported corrected OCR notes to {path}"
def build_notes_tab(catalog: dict[str, ModelInfo]) -> None:
store = FieldNoteStore()
model_id = gr.Dropdown(list(catalog), value=next(iter(catalog)), label="Model")
prompt = gr.Textbox(label="Prompt", lines=3)
response = gr.Textbox(label="Model response", lines=4)
correction = gr.Textbox(label="Human correction", lines=4)
tags = gr.Textbox(label="Tags", placeholder="ocr, plant-id, demo")
image_path = gr.Textbox(label="Image path", placeholder="Optional local image path")
video_path = gr.Textbox(label="Video path", placeholder="Optional local video path")
use_for_training = gr.Checkbox(label="Use for training", value=True)
save = gr.Button("Save field note", variant="primary")
export = gr.Button("Export corrected JSONL")
export_hf = gr.Button("Export local HF Dataset")
status = gr.Textbox(label="Status", interactive=False)
def save_note(
selected: str,
prompt_text: str,
response_text: str,
correction_text: str,
tag_text: str,
image: str,
video: str,
training: bool,
) -> str:
note = FieldNote.create(
model_id=selected,
prompt=prompt_text,
response=response_text,
correction=correction_text,
tags=tag_text,
image_path=image,
video_path=video,
use_for_training=training,
)
path = store.save(note)
APP_STATE.emit(
Event(
EventType.FIELD_NOTE_SAVED,
{
"model_id": selected,
"path": str(path),
"has_correction": bool(correction_text.strip()),
"tags": tag_text,
"use_for_training": training,
},
)
)
return f"Saved to {path}"
save.click(
save_note,
[model_id, prompt, response, correction, tags, image_path, video_path, use_for_training],
status,
show_progress=CLICK_PROGRESS,
)
export.click(
partial(_export_notes, store),
outputs=status,
show_progress=CLICK_PROGRESS,
)
export_hf.click(
partial(_export_dataset, store),
outputs=status,
show_progress=CLICK_PROGRESS,
)
build_ocr_import_panel(store, model_id, status)
def build_ocr_import_panel(
store: FieldNoteStore,
model_id: gr.Dropdown,
status: gr.Textbox,
) -> None:
gr.Markdown("### OCR correction import")
ocr_path = gr.Textbox(
label="OCR predictions file",
placeholder="Local .csv, .jsonl, or .ndjson with source_path,text,confidence",
)
ocr_threshold = gr.Slider(
label="Uncertain confidence threshold",
minimum=0,
maximum=1,
value=0.8,
step=0.01,
)
with gr.Row():
preview_ocr = gr.Button("Preview uncertain OCR")
import_ocr = gr.Button("Import uncertain OCR", variant="primary")
export_ocr = gr.Button("Export corrected OCR JSONL")
ocr_preview = gr.JSON(label="OCR import preview")
preview_ocr.click(
preview_ocr_predictions,
[ocr_path, ocr_threshold],
ocr_preview,
show_progress=CLICK_PROGRESS,
)
import_ocr.click(
partial(import_ocr_predictions, store),
[model_id, ocr_path, ocr_threshold],
status,
show_progress=CLICK_PROGRESS,
)
export_ocr.click(
partial(_export_ocr_notes, store),
outputs=status,
show_progress=CLICK_PROGRESS,
)
def preview_ocr_predictions(path: str, threshold: float) -> dict:
if not path.strip():
return {"error": "Enter a local OCR prediction file path."}
try:
return ocr_import_summary(path, threshold)
except (FileNotFoundError, ValueError, OSError) as exc:
return {"error": str(exc)}
def import_ocr_predictions(
store: FieldNoteStore,
selected: str,
path: str,
threshold: float,
) -> str:
if not path.strip():
return "Enter a local OCR prediction file path."
try:
predictions = load_ocr_predictions(path)
imported = import_uncertain_predictions(
store,
predictions,
selected,
confidence_threshold=threshold,
)
except (FileNotFoundError, ValueError, OSError) as exc:
return str(exc)
APP_STATE.emit(
Event(
EventType.FIELD_NOTE_SAVED,
{
"model_id": selected,
"path": str(store.path),
"source": path,
"imported": imported,
"tags": "ocr,uncertain",
"use_for_training": False,
},
)
)
return f"Imported {imported} uncertain OCR predictions to {store.path}"
|