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| import gradio as gr | |
| import shap | |
| import numpy as np | |
| import torch | |
| import transformers | |
| from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer, AutoModelForTokenClassification | |
| import matplotlib | |
| matplotlib.use("Agg") | |
| import matplotlib.pyplot as plt | |
| import matplotlib.patches as mpatches | |
| import io | |
| import base64 | |
| import string | |
| import re | |
| import sys | |
| import csv | |
| import os | |
| HF_TOKEN = os.getenv("hf_token") | |
| csv.field_size_limit(sys.maxsize) | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| # ── Load classification model ────────────────────────────────────────────────── | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| "willwim/adr_SJM_Notebook-Copy_for_T3", token=HF_TOKEN | |
| ) | |
| model = AutoModelForSequenceClassification.from_pretrained( | |
| "willwim/adr_SJM_Notebook-Copy_for_T3", token=HF_TOKEN | |
| ).to(device) | |
| pred = transformers.pipeline( | |
| "text-classification", model=model, tokenizer=tokenizer, | |
| top_k=None, device=device | |
| ) | |
| explainer = shap.Explainer(pred) | |
| # ── Load NER model ───────────────────────────────────────────────────────────── | |
| ner_tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all") | |
| ner_model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all") | |
| ner_pipe = pipeline( | |
| "ner", model=ner_model, tokenizer=ner_tokenizer, | |
| aggregation_strategy="simple" | |
| ) | |
| # ── Severity rating renderer ────────────────────────────────────────────────── | |
| def build_severity_html(severe_prob: float) -> str: | |
| """ | |
| Converts the Severe Reaction probability into a Low / Medium / High badge | |
| with a colour-coded progress bar and plain-language description. | |
| Thresholds (tunable): | |
| < 0.35 → Low | |
| 0.35 – 0.65 → Medium | |
| >= 0.65 → High | |
| """ | |
| if severe_prob < 0.35: | |
| level = "Low" | |
| bar_color = "#2eaa5a" # green | |
| bg_color = "#eafaf1" | |
| border_col = "#2eaa5a" | |
| description = ( | |
| "The clinical text shows limited indicators of a serious adverse drug reaction. " | |
| "Routine monitoring is advised." | |
| ) | |
| elif severe_prob < 0.65: | |
| level = "Medium" | |
| bar_color = "#e8a020" # amber | |
| bg_color = "#fffbea" | |
| border_col = "#e8a020" | |
| description = ( | |
| "The clinical text contains some features associated with a significant adverse " | |
| "drug reaction. Further clinical assessment is recommended." | |
| ) | |
| else: | |
| level = "High" | |
| bar_color = "#cc1111" # red | |
| bg_color = "#fff0f0" | |
| border_col = "#cc1111" | |
| description = ( | |
| "The clinical text shows strong indicators of a severe adverse drug reaction. " | |
| "Prompt clinical review is strongly recommended." | |
| ) | |
| pct = round(severe_prob * 100, 1) | |
| bar_width = round(severe_prob * 100, 1) | |
| html = ( | |
| "<div style='" | |
| "background:" + bg_color + "; " | |
| "border:1.5px solid " + border_col + "; " | |
| "border-radius:10px; padding:18px 20px; font-family:system-ui, sans-serif;'>" | |
| # Rating badge + percentage on same row | |
| "<div style='display:flex; align-items:center; justify-content:space-between; " | |
| "margin-bottom:12px;'>" | |
| "<span style='font-size:1.5em; font-weight:800; color:" + bar_color + ";'>" | |
| + level + | |
| "</span>" | |
| "<span style='font-size:1.0em; font-weight:600; color:#555;'>" | |
| + str(pct) + "% severe probability" | |
| "</span>" | |
| "</div>" | |
| # Progress bar track | |
| "<div style='background:#e0e0e0; border-radius:6px; height:14px; " | |
| "overflow:hidden; margin-bottom:14px;'>" | |
| "<div style='background:" + bar_color + "; width:" + str(bar_width) + "%; " | |
| "height:100%; border-radius:6px; " | |
| "transition:width 0.4s ease;'></div>" | |
| "</div>" | |
| # Description | |
| "<p style='margin:0; font-size:0.9em; color:#444; line-height:1.6;'>" | |
| + description + | |
| "</p>" | |
| "</div>" | |
| ) | |
| return html | |
| # ── Custom SHAP bar-chart renderer ───────────────────────────────────────────── | |
| def render_shap_bar_chart(shap_values, class_idx=1): | |
| values = shap_values.values | |
| tokens = shap_values.data | |
| if values.ndim == 2: | |
| sv = values[:, class_idx] | |
| else: | |
| sv = values | |
| STOPWORDS = { | |
| "a", "an", "the", "and", "or", "but", "in", "on", "at", "to", "for", | |
| "of", "with", "by", "from", "is", "was", "are", "were", "be", "been", | |
| "being", "have", "has", "had", "do", "does", "did", "will", "would", | |
| "could", "should", "may", "might", "that", "this", "it", "its", | |
| "he", "she", "they", "we", "i", "my", "his", "her", "their", "our", | |
| "not", "no", "so", "if", "as", "up", "out", "about", "after", | |
| "before", "then", "than", "also", "into", "over", "such", "further", | |
| "while", "which", "who", "whom", "what", "when", "where", "how", | |
| } | |
| def is_meaningful(tok): | |
| t = tok.strip().lower() | |
| if not t: | |
| return False | |
| if all(c in string.punctuation + " \t\n" for c in t): | |
| return False | |
| if len(t) <= 1: | |
| return False | |
| if t in STOPWORDS: | |
| return False | |
| if t.startswith("##"): | |
| return False | |
| if re.fullmatch(r"\d+", t): | |
| return False | |
| return True | |
| tok_arr = np.array(tokens) | |
| mask = np.array([is_meaningful(t) for t in tok_arr]) | |
| sv_f = sv[mask] | |
| tok_f = tok_arr[mask] | |
| # Deduplicate: keep highest |SHAP| per unique token | |
| seen = {} | |
| for i, tok in enumerate(tok_f): | |
| key = tok.strip().lower() | |
| if key not in seen or abs(sv_f[i]) > abs(sv_f[seen[key]]): | |
| seen[key] = i | |
| keep = sorted(seen.values()) | |
| sv_f = sv_f[keep] | |
| tok_f = tok_f[keep] | |
| TOP_N = 20 | |
| order = np.argsort(np.abs(sv_f))[::-1][:TOP_N] | |
| sv_top = sv_f[order] | |
| tok_top = tok_f[order] | |
| plot_order = np.argsort(sv_top) | |
| sv_plot = sv_top[plot_order] | |
| tok_plot = tok_top[plot_order] | |
| COLOR_POSITIVE = "#cc1111" | |
| COLOR_NEGATIVE = "#1a6fcc" | |
| colors = [COLOR_POSITIVE if v > 0 else COLOR_NEGATIVE for v in sv_plot] | |
| fig_height = max(4, len(sv_plot) * 0.38) | |
| fig, ax = plt.subplots(figsize=(8, fig_height), facecolor="white") | |
| ax.set_facecolor("white") | |
| y_pos = np.arange(len(sv_plot)) | |
| ax.barh(y_pos, sv_plot, color=colors, height=0.6, edgecolor="none") | |
| ax.axvline(0, color="#333333", linewidth=0.9, zorder=3) | |
| ax.set_yticks(y_pos) | |
| ax.set_yticklabels(tok_plot, fontsize=10, color="#222222") | |
| ax.set_xlabel("SHAP Value — impact on ADR prediction", fontsize=10, color="#444444") | |
| ax.set_title( | |
| "Token-Feature Importance: Words Driving Prediction", | |
| fontsize=12, fontweight="bold", color="#222222", pad=12 | |
| ) | |
| red_patch = mpatches.Patch(color=COLOR_POSITIVE, label="Increases severe ADR probability") | |
| blue_patch = mpatches.Patch(color=COLOR_NEGATIVE, label="Decreases severe ADR probability") | |
| ax.legend(handles=[red_patch, blue_patch], fontsize=9, loc="lower right", framealpha=0.7) | |
| ax.spines["top"].set_visible(False) | |
| ax.spines["right"].set_visible(False) | |
| ax.spines["left"].set_visible(False) | |
| ax.tick_params(axis="y", length=0) | |
| ax.tick_params(axis="x", colors="#555555") | |
| plt.tight_layout() | |
| buf = io.BytesIO() | |
| fig.savefig(buf, format="png", dpi=130, bbox_inches="tight", facecolor="white") | |
| plt.close(fig) | |
| buf.seek(0) | |
| b64 = base64.b64encode(buf.read()).decode("utf-8") | |
| return ( | |
| "<div style='background:white; padding:12px; border-radius:8px;'>" | |
| "<img src='data:image/png;base64," + b64 + "' " | |
| "style='width:100%; max-width:760px; display:block; margin:auto;' /></div>" | |
| ) | |
| # ── Severity rating widget ───────────────────────────────────────────────────── | |
| def build_severity_html(severe_prob): | |
| if severe_prob >= 0.70: | |
| rating = "HIGH" | |
| rating_color = "#cc1111" | |
| rating_bg = "#fff0f0" | |
| rating_border = "#e07070" | |
| rating_desc = "Strong indicators of a serious adverse drug reaction are present." | |
| rating_icon = "\u26a0\ufe0f" | |
| elif severe_prob >= 0.40: | |
| rating = "MEDIUM" | |
| rating_color = "#b07000" | |
| rating_bg = "#fffbe6" | |
| rating_border = "#d4a800" | |
| rating_desc = "Some indicators of an adverse drug reaction detected. Clinical review recommended." | |
| rating_icon = "\U0001f536" | |
| else: | |
| rating = "LOW" | |
| rating_color = "#2a7a2a" | |
| rating_bg = "#f0fff0" | |
| rating_border = "#5aaa5a" | |
| rating_desc = "Few or no indicators of a severe adverse drug reaction detected." | |
| rating_icon = "\u2705" | |
| bar_pct = int(severe_prob * 100) | |
| return ( | |
| "<div style='background:" + rating_bg + "; border:2px solid " + rating_border + "; " | |
| "border-radius:10px; padding:16px 20px; font-family:system-ui, sans-serif;'>" | |
| "<div style='display:flex; align-items:center; gap:12px; margin-bottom:10px;'>" | |
| "<span style='font-size:1.8em;'>" + rating_icon + "</span>" | |
| "<div>" | |
| "<div style='font-size:0.75em; font-weight:700; letter-spacing:0.1em; " | |
| "text-transform:uppercase; color:#666;'>ADR Severity Rating</div>" | |
| "<div style='font-size:1.6em; font-weight:900; color:" + rating_color + "; " | |
| "letter-spacing:0.04em;'>" + rating + "</div>" | |
| "</div>" | |
| "<div style='margin-left:auto; text-align:right;'>" | |
| "<div style='font-size:0.72em; color:#888; font-weight:600;'>Severe reaction probability</div>" | |
| "<div style='font-size:1.4em; font-weight:800; color:" + rating_color + ";'>" | |
| + str(bar_pct) + "%</div>" | |
| "</div>" | |
| "</div>" | |
| "<div style='background:#e0e0e0; border-radius:999px; height:10px; margin-bottom:10px;'>" | |
| "<div style='background:" + rating_color + "; width:" + str(bar_pct) + "%; " | |
| "height:10px; border-radius:999px;'></div>" | |
| "</div>" | |
| "<div style='font-size:0.88em; color:#555; margin-top:4px;'>" + rating_desc + "</div>" | |
| "</div>" | |
| ) | |
| # ── Main prediction function ─────────────────────────────────────────────────── | |
| def adr_predict(x): | |
| text_input = str(x).lower() | |
| encoded_input = tokenizer(text_input, return_tensors="pt").to(device) | |
| output = model(**encoded_input) | |
| scores = torch.softmax(output.logits, dim=-1)[0].detach().cpu().numpy() | |
| # SHAP | |
| try: | |
| shap_values = explainer([text_input]) | |
| shap_html = render_shap_bar_chart(shap_values[0], class_idx=1) | |
| except Exception as e: | |
| shap_html = "<p style='color:red;'>SHAP explanation error: " + str(e) + "</p>" | |
| # NER | |
| try: | |
| res = ner_pipe(text_input) | |
| entity_config = { | |
| "Severity": {"bg": "#ffe0de", "border": "#e07070", "label": "Severity"}, | |
| "Sign_symptom": {"bg": "#d4f5d4", "border": "#5aaa5a", "label": "Symptom"}, | |
| "Medication": {"bg": "#d0e8ff", "border": "#4a90d9", "label": "Medication"}, | |
| "Age": {"bg": "#fff3cc", "border": "#d4a800", "label": "Age"}, | |
| "Sex": {"bg": "#ffe8fb", "border": "#c070b0", "label": "Sex"}, | |
| "Diagnostic_procedure": {"bg": "#e8e8e8", "border": "#888888", "label": "Diagnostic"}, | |
| "Biological_structure": {"bg": "#ddeeff", "border": "#6699cc", "label": "Body Part"}, | |
| } | |
| default_cfg = {"bg": "#f0f0f0", "border": "#aaaaaa", "label": "Other"} | |
| seen_groups = list(dict.fromkeys( | |
| e["entity_group"] for e in sorted(res, key=lambda e: e["start"]) | |
| )) | |
| legend_html = ( | |
| "<div style='display:flex; flex-wrap:wrap; gap:10px; " | |
| "margin-bottom:16px; padding-bottom:12px; border-bottom:1px solid #e0e0e0;'>" | |
| ) | |
| for grp in seen_groups: | |
| cfg = entity_config.get(grp, default_cfg) | |
| bg = cfg["bg"] | |
| border = cfg["border"] | |
| lbl = cfg["label"] | |
| legend_html += ( | |
| "<span style='display:inline-flex; align-items:center; gap:6px; " | |
| "font-size:0.8em; font-weight:600; color:#444; font-family:system-ui, sans-serif;'>" | |
| "<span style='display:inline-block; width:13px; height:13px; border-radius:3px; " | |
| "background:" + bg + "; border:2px solid " + border + ";'></span>" | |
| + lbl + "</span>" | |
| ) | |
| legend_html += "</div>" | |
| text_html = ( | |
| "<div style='line-height:3.4; font-size:1.05em; color:#111; " | |
| "font-family:Georgia, serif; letter-spacing:0.01em;'>" | |
| ) | |
| prev_end = 0 | |
| res_sorted = sorted(res, key=lambda e: e["start"]) | |
| for entity in res_sorted: | |
| start, end = entity["start"], entity["end"] | |
| word = text_input[start:end] | |
| cfg = entity_config.get(entity["entity_group"], default_cfg) | |
| bg = cfg["bg"] | |
| border = cfg["border"] | |
| lbl = cfg["label"] | |
| text_html += "<span style='color:#111;'>" + text_input[prev_end:start] + "</span>" | |
| text_html += ( | |
| "<span style='display:inline-block; position:relative; " | |
| "vertical-align:middle; margin:0 2px; text-align:center;'>" | |
| "<span style='display:block; font-size:0.6em; font-weight:800; " | |
| "letter-spacing:0.08em; text-transform:uppercase; color:" + border + "; " | |
| "font-family:system-ui, sans-serif; line-height:1.1; margin-bottom:2px;'>" | |
| + lbl + "</span>" | |
| "<span style='background:" + bg + "; border:1.5px solid " + border + "; " | |
| "color:#111; padding:3px 8px; border-radius:6px; " | |
| "font-weight:600; white-space:nowrap;'>" + word + "</span>" | |
| "</span>" | |
| ) | |
| prev_end = end | |
| text_html += "<span style='color:#111;'>" + text_input[prev_end:] + "</span></div>" | |
| htext = legend_html + text_html | |
| except Exception as ex: | |
| htext = "<p style='color:#c00;'>NER processing error: " + str(ex) + "</p>" | |
| label_output = { | |
| "Severe Reaction": float(scores[1]), | |
| "Non-severe Reaction": float(scores[0]), | |
| } | |
| severity_html = build_severity_html(float(scores[1])) | |
| return label_output, severity_html, shap_html, htext | |
| # ── UI ───────────────────────────────────────────────────────────────────────── | |
| custom_css = """ | |
| /* ── Global: light grey page background, dark text everywhere ── */ | |
| body, | |
| .gradio-container, | |
| .main, | |
| .contain, | |
| .gap { | |
| background-color: #f4f6f9 !important; | |
| font-family: 'Inter', system-ui, sans-serif !important; | |
| color: #111111 !important; | |
| } | |
| /* Force ALL text dark by default */ | |
| *, *::before, *::after { | |
| color: inherit; | |
| } | |
| /* ── Uniform white card ── */ | |
| .card { | |
| background: #ffffff !important; | |
| border: 1px solid #e2e6ea !important; | |
| border-radius: 12px !important; | |
| padding: 20px !important; | |
| box-shadow: 0 1px 4px rgba(0,0,0,0.08) !important; | |
| color: #111111 !important; | |
| margin-bottom: 12px !important; | |
| } | |
| /* Every text node inside a card */ | |
| .card *, | |
| .card p, | |
| .card span, | |
| .card div, | |
| .card label, | |
| .card h1, .card h2, .card h3, | |
| .card td, .card th, | |
| .card button:not(.primary) { | |
| color: #111111 !important; | |
| } | |
| /* Header card */ | |
| .header-card { | |
| text-align: center !important; | |
| margin-bottom: 18px !important; | |
| } | |
| /* ── Card section label (uppercase caption) ── */ | |
| .card-label { | |
| font-size: 0.72em !important; | |
| font-weight: 700 !important; | |
| letter-spacing: 0.1em !important; | |
| text-transform: uppercase !important; | |
| color: #777777 !important; | |
| margin: 0 0 10px 0 !important; | |
| padding: 0 !important; | |
| display: block !important; | |
| } | |
| /* Strip double borders / backgrounds from inner Gradio wrappers */ | |
| .card > .form, | |
| .card > .block, | |
| .card .wrap, | |
| .card [data-testid="label"], | |
| .card .label-container, | |
| .card .label-wrap, | |
| .card .prose, | |
| .card .md { | |
| background: transparent !important; | |
| border: none !important; | |
| box-shadow: none !important; | |
| padding: 0 !important; | |
| } | |
| /* ── Gradio Label (probability bar) ── */ | |
| .card .label-wrap, | |
| .card .label-wrap *, | |
| .card [data-testid="label"] * { | |
| background: transparent !important; | |
| color: #111111 !important; | |
| } | |
| /* ── Textbox ── */ | |
| .card textarea, | |
| .card input[type="text"], | |
| .card input { | |
| background: #fafafa !important; | |
| color: #111111 !important; | |
| border: 1px solid #dde1e7 !important; | |
| border-radius: 8px !important; | |
| } | |
| .card textarea::placeholder, | |
| .card input::placeholder { | |
| color: #999999 !important; | |
| } | |
| /* ── Textbox label text ── */ | |
| .card .svelte-1f354aw, | |
| .card label > span { | |
| color: #333333 !important; | |
| font-weight: 600 !important; | |
| } | |
| /* ── Examples table ── */ | |
| .card .examples, | |
| .card .examples table, | |
| .card .examples td, | |
| .card .examples th, | |
| .card .examples button { | |
| background: #ffffff !important; | |
| color: #111111 !important; | |
| border-color: #e2e6ea !important; | |
| } | |
| /* ── Run button ── */ | |
| .run-btn { | |
| width: 100% !important; | |
| margin-top: 10px !important; | |
| } | |
| /* ── Processing spinner area ── */ | |
| .card .generating, | |
| .card .eta-bar, | |
| .card .progress-bar { | |
| background: #f0f0f0 !important; | |
| color: #555555 !important; | |
| } | |
| footer { visibility: hidden; } | |
| """ | |
| with gr.Blocks(title="ADR Detector") as demo: | |
| # ── Header ──────────────────────────────────────────────────────────────── | |
| with gr.Column(elem_classes="card header-card"): | |
| gr.Markdown( | |
| "<h1 style='margin:0 0 6px 0; color:#1a1a2e; font-size:1.8em; text-align:center;'>" | |
| "Adverse Drug Reaction (ADR) Detector</h1>" | |
| "<p style='margin:0; color:#555; font-size:0.97em; text-align:center;'>" | |
| "Analyze clinical text for potential medication-related severity " | |
| "and key medical entities.</p>" | |
| ) | |
| # ── Row 1: Input (left) | Classification + Severity (right) ────────────── | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| with gr.Column(elem_classes="card"): | |
| gr.Markdown( | |
| "<p class='card-label'>Clinical Input</p>" | |
| ) | |
| prob1 = gr.Textbox( | |
| label="Clinical Observations", | |
| lines=4, | |
| placeholder="Example: Patient experienced acute kidney injury after taking Ibuprofen...", | |
| elem_id="input-text", | |
| ) | |
| submit_btn = gr.Button("Run Analysis", variant="primary", elem_classes="run-btn") | |
| with gr.Column(elem_classes="card"): | |
| gr.Markdown("<p class='card-label'>Examples</p>") | |
| gr.Examples( | |
| examples=[ | |
| ["A 42 year-old male developed a severe migraine and elevated blood pressure " | |
| "shortly after taking Aspirin. He was admitted for observation."], | |
| ["A 28 year-old female reported mild nausea and minor discomfort in the upper " | |
| "abdomen after taking Acetaminophen 500mg. Symptoms resolved within two hours."], | |
| ["A 67 year-old male with a history of renal impairment experienced acute kidney " | |
| "injury and oliguria following treatment with Ibuprofen for three days."], | |
| ["A 54 year-old female noted slight dizziness and dry mouth after her first dose " | |
| "of Metformin. No further symptoms were reported at the follow-up visit."], | |
| ], | |
| inputs=[prob1], | |
| ) | |
| with gr.Column(scale=1): | |
| with gr.Column(elem_classes="card"): | |
| gr.Markdown("<p class='card-label'>Classification</p>") | |
| label = gr.Label(label="Severity Probability") | |
| with gr.Column(elem_classes="card"): | |
| gr.Markdown("<p class='card-label'>Severity Rating</p>") | |
| severity_out = gr.HTML(label="Severity Rating") | |
| # ── Row 2: Medical Entities (left) | SHAP (right) ──────────────────────── | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| with gr.Column(elem_classes="card"): | |
| gr.Markdown("<p class='card-label'>Medical Entities</p>") | |
| htext_out = gr.HTML(label="NER Mapping") | |
| with gr.Column(scale=1): | |
| with gr.Column(elem_classes="card"): | |
| gr.Markdown("<p class='card-label'>Model Logic (SHAP)</p>") | |
| shap_out = gr.HTML(label="Feature Importance") | |
| # ── Disclaimer footer ───────────────────────────────────────────────────── | |
| with gr.Column(elem_classes="card"): | |
| gr.Markdown( | |
| "<div style='" | |
| "border-left: 4px solid #e8a020; " | |
| "padding: 14px 18px; " | |
| "background: #fffbea; " | |
| "border-radius: 6px; " | |
| "font-family: system-ui, sans-serif;'>" | |
| "<p style='margin:0 0 6px 0; font-size:0.85em; font-weight:800; " | |
| "letter-spacing:0.08em; text-transform:uppercase; color:#b07800;'>" | |
| "⚠ Academic Project — Not for Medical Use</p>" | |
| "<p style='margin:0 0 8px 0; font-size:0.92em; color:#444; line-height:1.65;'>" | |
| "This tool was developed strictly as an <strong>academic research project</strong> " | |
| "and is intended to demonstrate the application of natural language processing " | |
| "and explainable AI techniques to clinical text. " | |
| "It <strong>must not</strong> be used to make real medical decisions, diagnose conditions, " | |
| "guide treatment, or replace the advice of a qualified healthcare professional.</p>" | |
| "<p style='margin:0; font-size:0.85em; color:#777; line-height:1.5;'>" | |
| "Predictions are generated by a machine learning model and may be inaccurate, incomplete, " | |
| "or misleading. Always consult a licensed medical professional for any health-related concerns. " | |
| "The authors accept no liability for any use of this tool beyond its intended academic purpose.</p>" | |
| "</div>" | |
| ) | |
| submit_btn.click( | |
| fn=adr_predict, | |
| inputs=[prob1], | |
| outputs=[label, severity_out, shap_out, htext_out], | |
| ) | |
| demo.launch(theme=gr.themes.Default(), css=custom_css) |