Create app.py

app.py

@@ -0,0 +1,118 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# 1) Classification model
+model = AutoModelForSequenceClassification.from_pretrained("calerio-uva/roberta-adr-model")
+tokenizer = AutoTokenizer.from_pretrained("calerio-uva/roberta-adr-model")
+
+# 2) Unified NER pipeline
+ner = pipeline(
+    "ner",
+    model="d4data/biomedical-ner-all",
+    tokenizer="d4data/biomedical-ner-all",
+    aggregation_strategy="simple"
+)
+
+# 3) Tight tag sets
+SYMPTOM_TAGS = {"sign_symptom", "symptom"}
+DISEASE_TAGS = {"disease_disorder"}
+MED_TAGS     = {"medication", "administration", "therapeutic_procedure"}
+
+# 4) Helper: drop <3‑char & dedupe
+def dedupe_and_filter(tokens):
+    seen, out = set(), []
+    for tok in tokens:
+        w = tok.strip()
+        if len(w) < 3:
+            continue
+        lw = w.lower()
+        if lw not in seen:
+            seen.add(lw)
+            out.append(w)
+    return out
+
+def classify_adr(text: str):
+    print("🔍 [DEBUG] Running classify_adr", flush=True)
+
+    # Clean
+    clean = text.strip().replace("nan", "").replace("  ", " ")
+    print("🔍 [DEBUG] clean[:50]:", clean[:50], "...", flush=True)
+
+    # Severity
+    inputs = tokenizer(clean, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    probs = torch.softmax(logits, dim=1)[0].cpu().numpy()
+
+    # Raw NER
+    ents = ner(clean)
+    print("🔍 [DEBUG] raw ents:", [(e["entity_group"], e["word"], e["start"], e["end"]) for e in ents], flush=True)
+
+    # 1) Build & merge spans by offsets
+    spans = []
+    for ent in ents:
+        grp, start, end, score = ent["entity_group"].lower(), ent["start"], ent["end"], ent.get("score", 1.0)
+        if spans and spans[-1]["group"] == grp and start <= spans[-1]["end"]:
+            spans[-1]["end"]   = max(spans[-1]["end"], end)
+            spans[-1]["score"] = max(spans[-1]["score"], score)
+        else:
+            spans.append({"group": grp, "start": start, "end": end, "score": score})
+    print("🔍 [DEBUG] merged spans:", spans, flush=True)
+
+    # 2) Extend med spans out to full word
+    for s in spans:
+        if s["group"] in MED_TAGS:
+            st, en = s["start"], s["end"]
+            # extend forward while alphabetic
+            while en < len(clean) and clean[en].isalpha():
+                en += 1
+            s["end"] = en
+
+    # 3) Filter by confidence ≥0.6
+    spans = [s for s in spans if s["score"] >= 0.6]
+    print("🔍 [DEBUG] post‑filter spans:", spans, flush=True)
+
+    # 4) Extract text
+    tokens = [clean[s["start"]:s["end"]] for s in spans]
+    print("🔍 [DEBUG] tokens:", tokens, flush=True)
+
+    # Bucket & dedupe
+    symptoms    = dedupe_and_filter([t for t, s in zip(tokens, spans) if s["group"] in SYMPTOM_TAGS])
+    diseases    = dedupe_and_filter([t for t, s in zip(tokens, spans) if s["group"] in DISEASE_TAGS])
+    medications = dedupe_and_filter([t for t, s in zip(tokens, spans) if s["group"] in MED_TAGS])
+
+    # Interpretation
+    if probs[1] > 0.9:
+        comment = "❗ High confidence this is a severe ADR."
+    elif probs[1] > 0.5:
+        comment = "⚠️ Borderline case — may be severe."
+    else:
+        comment = "✅ Likely not severe."
+
+    return (
+        f"Not Severe (0): {probs[0]:.3f}\nSevere (1): {probs[1]:.3f}",
+        "\n".join(symptoms)    or "None detected",
+        "\n".join(diseases)    or "None detected",
+        "\n".join(medications) or "None detected",
+        comment
+    )
+
+# 5) Gradio UI
+demo = gr.Interface(
+    fn=classify_adr,
+    inputs=gr.Textbox(lines=4, label="ADR Description"),
+    outputs=[
+        gr.Textbox(label="Predicted Probabilities"),
+        gr.Textbox(label="Symptoms"),
+        gr.Textbox(label="Diseases or Conditions"),
+        gr.Textbox(label="Medications"),
+        gr.Textbox(label="Interpretation"),
+    ],
+    title="ADR Severity & NER Classifier",
+    description="Paste an ADR description to classify severity and extract symptoms, diseases & medications.",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()