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README.md

@@ -1,12 +1,32 @@
 ---
 title: VizAnnot
-emoji: 🌖
+emoji: 🔬
 colorFrom: blue
-colorTo: yellow
+colorTo: green
 sdk: gradio
-sdk_version: 6.0.1
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
+license: mit
+datasets:
+  - rntc/biomed-fr-pancreas-annotations
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Pancreas Cancer Clinical Report Annotations Explorer
+
+This Gradio app allows you to explore annotations extracted from synthetic French clinical reports about pancreas cancer.
+
+## Features
+
+- **Highlighted text view**: Clinical reports with colored spans showing where information was extracted
+- **Hover tooltips**: Hover over highlighted spans to see the variable name and extracted value
+- **Annotations table**: Complete table of all extracted variables with values and source spans
+- **Search**: Search through clinical reports by text content
+
+## Dataset
+
+The app loads data from [rntc/biomed-fr-pancreas-annotations](https://huggingface.co/datasets/rntc/biomed-fr-pancreas-annotations).
+
+## Usage
+
+Use the slider to navigate between samples, or use the search box to find specific content.

app.py

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+"""
+Gradio app to explore pancreas or lymphome clinical report annotations.
+"""
+
+import os
+from functools import partial
+from pathlib import Path
+
+import gradio as gr
+from datasets import load_dataset
+
+MIN_ANNOTATIONS = 10
+PANCREAS_REPO_ID = os.getenv("PANCREAS_REPO_ID", "rntc/biomed-fr-pancreas-annotations")
+LYMPHOME_REPO_ID = os.getenv("LYMPHOME_REPO_ID", "rntc/biomed-fr-lymphome-annotations")
+LYMPHOME_LOCAL_JSONL = (
+    Path(__file__).resolve().parent.parent
+    / "Qwen--Qwen3-235B-A22B-Instruct-2507-FP8-4-lymphome-annotation-20251201_153807.jsonl"
+)
+
+# Colors for highlighting
+COLORS = [
+    "#FFEB3B",
+    "#4CAF50",
+    "#2196F3",
+    "#FF9800",
+    "#E91E63",
+    "#9C27B0",
+    "#00BCD4",
+    "#8BC34A",
+    "#FF5722",
+    "#607D8B",
+]
+
+
+def count_real_annotations(annotation):
+    """Count real annotations (excluding 'not found' placeholders)."""
+    count = 0
+    for var_data in annotation.values():
+        if var_data and isinstance(var_data, dict):
+            value = var_data.get("value")
+            span = var_data.get("span", "")
+            if value:
+                if span and "pas de mention" in span.lower():
+                    continue
+                if "not performed" in str(value).lower():
+                    continue
+                count += 1
+    return count
+
+
+def escape_html(text):
+    if not text:
+        return ""
+    return str(text).replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")
+
+
+def highlight_text(cr_text, annotation):
+    """Highlight spans in CR text."""
+    if not cr_text or not annotation:
+        return f"<pre style='white-space:pre-wrap;'>{escape_html(cr_text)}</pre>"
+
+    # Collect valid spans (that exist in text)
+    spans = []
+    for var_name, var_data in annotation.items():
+        if var_data and isinstance(var_data, dict):
+            span = var_data.get("span")
+            value = var_data.get("value")
+            if span and value and span in cr_text:
+                spans.append(
+                    {
+                        "text": span,
+                        "start": cr_text.find(span),
+                        "var": var_name.replace("_", " ").title(),
+                        "value": str(value),
+                    }
+                )
+
+    if not spans:
+        return f"<pre style='white-space:pre-wrap;'>{escape_html(cr_text)}</pre>"
+
+    # Sort by position and remove overlaps
+    spans.sort(key=lambda x: x["start"])
+    filtered = []
+    for s in spans:
+        s["end"] = s["start"] + len(s["text"])
+        if not filtered or s["start"] >= filtered[-1]["end"]:
+            filtered.append(s)
+
+    # Build HTML
+    html = []
+    pos = 0
+    color_map = {}
+    color_idx = 0
+
+    for s in filtered:
+        if s["start"] > pos:
+            html.append(escape_html(cr_text[pos : s["start"]]))
+
+        if s["var"] not in color_map:
+            color_map[s["var"]] = COLORS[color_idx % len(COLORS)]
+            color_idx += 1
+
+        color = color_map[s["var"]]
+        html.append(
+            f'<mark style="background:{color};padding:1px 3px;border-radius:3px;" '
+            f'title="{escape_html(s["var"])}: {escape_html(s["value"])}">'
+            f'{escape_html(s["text"])}</mark>'
+        )
+        pos = s["end"]
+
+    if pos < len(cr_text):
+        html.append(escape_html(cr_text[pos:]))
+
+    return f"<pre style='white-space:pre-wrap;line-height:1.6;'>{''.join(html)}</pre>"
+
+
+def format_table(annotation):
+    """Format annotations as HTML table."""
+    if not annotation:
+        return "<p>No annotations</p>"
+
+    rows = []
+    for var_name, var_data in annotation.items():
+        if var_data and isinstance(var_data, dict):
+            value = var_data.get("value")
+            span = var_data.get("span", "")
+
+            var_label = var_name.replace("_", " ").title()
+
+            if value:
+                if span and "pas de mention" in span.lower():
+                    display_value = "/"
+                    display_span = ""
+                elif "not performed" in str(value).lower():
+                    display_value = "/"
+                    display_span = ""
+                else:
+                    display_value = str(value)
+                    display_span = span[:60] + "..." if span and len(span) > 60 else (span or "")
+            else:
+                display_value = "/"
+                display_span = ""
+
+            rows.append(
+                f"""<tr>
+                <td style="padding:6px 10px;border-bottom:1px solid #ddd;font-weight:500;">{escape_html(var_label)}</td>
+                <td style="padding:6px 10px;border-bottom:1px solid #ddd;color:#1565C0;">{escape_html(display_value)}</td>
+                <td style="padding:6px 10px;border-bottom:1px solid #ddd;color:#666;font-size:12px;font-style:italic;">{escape_html(display_span)}</td>
+            </tr>"""
+            )
+
+    return f"""<table style="width:100%;border-collapse:collapse;font-size:13px;">
+        <thead><tr style="background:#f5f5f5;">
+            <th style="padding:8px 10px;text-align:left;border-bottom:2px solid #ddd;">Variable</th>
+            <th style="padding:8px 10px;text-align:left;border-bottom:2px solid #ddd;">Value</th>
+            <th style="padding:8px 10px;text-align:left;border-bottom:2px solid #ddd;">Source</th>
+        </tr></thead>
+        <tbody>{"".join(rows)}</tbody>
+    </table>"""
+
+
+def load_pancreas_dataset():
+    print(f"Loading pancreas dataset from {PANCREAS_REPO_ID}...")
+    dataset = load_dataset(PANCREAS_REPO_ID, split="train")
+    print(f"Loaded {len(dataset)} pancreas samples")
+    return dataset
+
+
+def load_lymphome_dataset():
+    print(f"Loading lymphome dataset from {LYMPHOME_REPO_ID} (Hub)...")
+    try:
+        dataset = load_dataset(LYMPHOME_REPO_ID, split="train")
+        print(f"Loaded {len(dataset)} lymphome samples from Hub")
+        return dataset
+    except Exception as exc:  # noqa: BLE001 (we want to surface any failure)
+        print(f"Failed to load lymphome dataset from Hub: {exc}")
+        if LYMPHOME_LOCAL_JSONL.exists():
+            print(f"Falling back to local lymphome JSONL at {LYMPHOME_LOCAL_JSONL}")
+            dataset = load_dataset("json", data_files=str(LYMPHOME_LOCAL_JSONL), split="train")
+            print(f"Loaded {len(dataset)} lymphome samples from local file")
+            return dataset
+        raise
+
+
+def filter_indices(dataset, min_annotations):
+    return [
+        i
+        for i, sample in enumerate(dataset)
+        if count_real_annotations(sample.get("annotation", {})) >= min_annotations
+    ]
+
+
+def prepare_source(key, label, loader, min_annotations):
+    """Load a dataset source and precompute filtered indices."""
+    try:
+        dataset = loader()
+        filtered = filter_indices(dataset, min_annotations)
+        print(f"{label}: filtered to {len(filtered)} samples with >= {min_annotations} annotations")
+        return {
+            "label": label,
+            "dataset": dataset,
+            "filtered_indices": filtered,
+            "min_annotations": min_annotations,
+            "error": None,
+        }
+    except Exception as exc:  # noqa: BLE001 (we want to surface any failure)
+        print(f"Failed to load {label}: {exc}")
+        return {
+            "label": label,
+            "dataset": None,
+            "filtered_indices": [],
+            "min_annotations": min_annotations,
+            "error": str(exc),
+        }
+
+
+SOURCES = {
+    "pancreas": prepare_source("pancreas", "Pancréas", load_pancreas_dataset, MIN_ANNOTATIONS),
+    "lymphome": prepare_source("lymphome", "Lymphome", load_lymphome_dataset, MIN_ANNOTATIONS),
+}
+
+
+def display_sample_for_source(source_key, slider_idx):
+    """Display a sample for a given dataset source."""
+    source = SOURCES[source_key]
+
+    if source["error"]:
+        message = f"Dataset unavailable: {source['error']}"
+        return message, message, message
+
+    if not source["filtered_indices"]:
+        message = f"No samples with >= {source['min_annotations']} annotations."
+        return message, message, message
+
+    slider_idx = int(slider_idx)
+    if slider_idx < 0 or slider_idx >= len(source["filtered_indices"]):
+        return "Invalid", "Invalid", "Invalid"
+
+    real_idx = source["filtered_indices"][slider_idx]
+    sample = source["dataset"][real_idx]
+
+    original = sample.get("original_text", "")
+    cr = sample.get("CR", "")
+    annotation = sample.get("annotation", {})
+
+    n_annotations = count_real_annotations(annotation)
+
+    original_html = f"<pre style='white-space:pre-wrap;line-height:1.6;'>{escape_html(original)}</pre>"
+    cr_html = (
+        f"<p><b>Sample #{real_idx}</b> — {n_annotations} annotations</p>"
+        + highlight_text(cr, annotation)
+    )
+
+    return original_html, cr_html, format_table(annotation)
+
+
+def build_tab(source_key):
+    source = SOURCES[source_key]
+    label = source["label"]
+
+    with gr.TabItem(label):
+        if source["error"]:
+            gr.Markdown(f"⚠️ Could not load {label} dataset: {escape_html(source['error'])}")
+            return
+
+        if not source["filtered_indices"]:
+            gr.Markdown(f"⚠️ No samples with >= {source['min_annotations']} annotations.")
+            return
+
+        gr.Markdown(
+            f"Showing {len(source['filtered_indices'])} samples with >= "
+            f"{source['min_annotations']} annotations. Hover over highlights to see values."
+        )
+
+        with gr.Row():
+            slider = gr.Slider(
+                0,
+                len(source["filtered_indices"]) - 1,
+                value=0,
+                step=1,
+                label="Sample",
+            )
+
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### Original (English)")
+                original_html = gr.HTML()
+            with gr.Column():
+                gr.Markdown("### Generated CR (French)")
+                cr_html = gr.HTML()
+            with gr.Column():
+                gr.Markdown("### Extracted Variables")
+                table_html = gr.HTML()
+
+        slider.change(
+            fn=partial(display_sample_for_source, source_key),
+            inputs=[slider],
+            outputs=[original_html, cr_html, table_html],
+        )
+        demo.load(
+            fn=partial(display_sample_for_source, source_key),
+            inputs=[slider],
+            outputs=[original_html, cr_html, table_html],
+        )
+
+
+# Build UI
+with gr.Blocks(title="Clinical Annotations Explorer", theme=gr.themes.Base()) as demo:
+    gr.Markdown("# 🔬 Clinical Annotation Explorer")
+    gr.Markdown(
+        "Use the tabs below to switch between pancreas and lymphome annotations. "
+        "Hover over highlights to see the extracted values."
+    )
+
+    with gr.Tabs():
+        build_tab("pancreas")
+        build_tab("lymphome")
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio==4.44.0
+datasets
+huggingface_hub<0.27