Upload app.py

app.py

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+import json, torch, gradio as gr
+from transformers import AutoTokenizer, AutoModel
+from focus_area_model import LabelEmbCls
+from huggingface_hub import hf_hub_download
+
+MODEL = "mihir-s/medquad_classify"
+
+# Load tokenizer + base model
+tok  = AutoTokenizer.from_pretrained(MODEL)
+base = AutoModel.from_pretrained(MODEL).eval()
+
+# Load label data + weights
+id2label   = json.load(open("id2label.json"))
+label_embs = torch.load("label_embs.pt", map_location="cpu")
+
+# Load custom head
+model = LabelEmbCls(base, label_embs)
+model_path = hf_hub_download(repo_id=MODEL, filename="pytorch_model.bin")
+model.load_state_dict(torch.load(model_path, map_location="cpu"))
+model.eval()
+
+# ✅ One-box input logic
+def predict(text):
+    inputs = tok(text.strip(), return_tensors="pt", truncation=True, max_length=256, padding="max_length")
+    with torch.no_grad():
+        logits = model(**inputs)
+    return id2label[str(logits.argmax(1).item())]
+
+# Gradio interface with 1 text box
+gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Enter your medical text for classification"),
+    outputs=gr.Textbox(label="Predicted Focus Area"),
+    title="🧠 MedQuad Focus-Area Classifier"
+).launch()