Update app.py

app.py

@@ -1,3 +1,4 @@
+import os
 import pandas as pd
 import torch
 import transformers
@@ -13,7 +14,6 @@ DEVICE     = "cuda" if torch.cuda.is_available() else "cpu"
 # ── 2) Load & Filter Dataset ─────────────────────────────────────────────
 df = pd.read_csv("medquad.csv")
 df["text"] = df["question"].str.strip() + " " + df["answer"].str.strip()
-
 vc   = df["focus_area"].value_counts()
 keep = vc[vc >= MIN_FREQ].index
 df   = df[df["focus_area"].isin(keep)].reset_index(drop=True)
@@ -36,7 +36,7 @@ def encode_text(s: str, max_length=MAX_LEN):
         add_special_tokens=True
     ).to(DEVICE)
     hidden = bert_model(**toks).last_hidden_state
-    return hidden[:,0].squeeze().cpu()   # CLS token
+    return hidden[:,0].squeeze().cpu()
 
 # ── 4) Precompute Static Label Embeddings ─────────────────────────────────
 label_embs = torch.stack([
@@ -44,28 +44,29 @@ label_embs = torch.stack([
     for lbl in labels
 ])
 
-# ── 5) Classification Function ────────────────────────────────────────────
+# ── 5) Classification fn ───────────────────────────────────────────────────
 def predict_disease(symptoms: str):
-    """
-    Encode the user's input, compute cosine similarity
-    to each label embedding, and return the top label.
-    """
-    q_emb = encode_text(symptoms).unsqueeze(0)      # [1, hidden_size]
-    sims  = cosine_similarity(q_emb, label_embs)   # [1, num_labels]
+    q_emb = encode_text(symptoms).unsqueeze(0)
+    sims  = cosine_similarity(q_emb, label_embs)
     idx   = sims.argmax(dim=1).item()
     return id2label[idx]
 
 # ── 6) Gradio Interface ───────────────────────────────────────────────────
-iface = gr.Interface(
+# Rename to "app" so Hugging Face picks it up automatically:
+app = gr.Interface(
     fn=predict_disease,
     inputs=gr.Textbox(
         lines=3,
-        placeholder="Enter your symptoms here, e.g.\n'I have eye pain and blurred vision...'"
+        placeholder="Enter your symptoms here…"
     ),
     outputs="text",
-    title="🔬 Medical Symptom→Disease Chatbot",
-    description="Type your symptoms; PubMed‐BERT + cosine similarity predicts the most likely disease category."
+    title="🔬 Symptom→Disease Chatbot",
+    description="PubMed-BERT + cosine similarity"
 )
 
-if __name__ == "__main__":
-    iface.launch()
+# Explicitly launch on HF’s host/port
+app.launch(
+    server_name="0.0.0.0",
+    server_port=int(os.environ.get("PORT", 7860)),
+    share=False
+)