Update app.py

app.py

@@ -1,13 +1,16 @@
 import gradio as gr
-from transformers import BertTokenizer, BertModel
+from transformers import AutoTokenizer, AutoModel
 import torch
 import pdfplumber
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 
-# Load BERT once
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertModel.from_pretrained("bert-base-uncased")
+# ----------------------------------------------------------
+# ✅ Load YOUR fine-tuned model from Hugging Face
+# ----------------------------------------------------------
+MODEL_NAME = "Novricana25/ats-bert-model"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModel.from_pretrained(MODEL_NAME)
 
 def extract_text(file):
     with pdfplumber.open(file) as pdf:
@@ -43,7 +46,7 @@ demo = gr.Interface(
     inputs=[gr.File(label="Upload Resume (PDF)"), gr.Textbox(label="Paste Job Description")],
     outputs=["text"],
     title="Smart ATS Resume Optimizer",
-    description="An AI tool that analyzes and optimizes your resume against job descriptions using NLP for better ATS compatibility.",
+    description="Analyzes and scores how well a resume matches a job description using your fine-tuned BERT model hosted on Hugging Face.",
 )
 
 demo.launch()