Update app.py

app.py

@@ -1,19 +1,3 @@
-
-# CollegeGenius HF — 100% Hugging Face, No External APIs
-# ------------------------------------------------------
-# Everything runs inside the Space using open models from the Hugging Face Hub.
-# No third‑party web APIs or keys required.
-#
-# Models:
-# - sentence-transformers/paraphrase-MiniLM-L6-v2  (embeddings)
-# - textattack/roberta-base-CoLA                   (grammar acceptability per sentence)
-# - sshleifer/distilbart-cnn-12-6                 (summarization / light rewriting)
-# - j-hartmann/emotion-english-distilroberta-base (emotion for interviews)
-# - distilroberta-base                             (masked LM for resume verb suggestions)
-#
-# Launch config is at bottom: demo.queue(...).launch()
-# ------------------------------------------------------
-
 import os
 import re
 import io
@@ -65,7 +49,8 @@ _EMBEDDER = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L6-v2")
 # Grammar acceptability (CoLA)
 _CoLA_tok = AutoTokenizer.from_pretrained("textattack/roberta-base-CoLA")
 _CoLA = AutoModelForSequenceClassification.from_pretrained("textattack/roberta-base-CoLA")
-_acc_pipe = pipeline("text-classification", model=_CoLA, tokenizer=_CoLA_tok, return_all_scores=True)
+# Use top_k=None instead of deprecated return_all_scores=True
+_acc_pipe = pipeline("text-classification", model=_CoLA, tokenizer=_CoLA_tok, top_k=None)
 
 # Summarizer / lightweight rewriting
 _SUM_tok = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")
@@ -161,7 +146,8 @@ CLICHES = [
 ]
 
 def acceptability_score(sentence: str) -> float:
-    out = _acc_pipe(sentence)[0]
+    out = _acc_pipe(sentence)[0]  # list of dicts with 'label' and 'score'
+    # When top_k=None, pipeline returns all class scores
     scores = {o["label"]: o["score"] for o in out}
     return float(scores.get("LABEL_1", 0.0))
 
@@ -669,4 +655,5 @@ with gr.Blocks(title="CollegeGenius HF", theme=gr.themes.Soft()) as demo:
 app = demo
 
 if __name__ == "__main__":
-demo.queue(concurrency_count=2, max_size=32).launch()
+    # Gradio 4+ no longer accepts concurrency_count here
+    demo.queue().launch()