ss

app.py

@@ -9,35 +9,15 @@ import os
 import tempfile
 import base64
 from rank_bm25 import BM25Okapi
+from transformers import AutoModel, AutoTokenizer
+from sentence_transformers import SentenceTransformer
+from nltk.tokenize import word_tokenize, sent_tokenize
+from tqdm import tqdm
 import re
 import io
 import PyPDF2
 from docx import Document
 import csv
-
-# Fix for Replicate issue - must be before model imports
-import torch
-try:
-    from einops.layers.torch import Replicate
-except ImportError:
-    # Define our own Replicate class if not available
-    class Replicate(torch.nn.Module):
-        def __init__(self, *args, **kwargs):
-            super().__init__()
-        def forward(self, x):
-            return x
-
-# Make sure Replicate is available in the right module
-import sys
-import einops.layers.torch
-einops.layers.torch.Replicate = Replicate
-sys.modules['einops.layers.torch'].Replicate = Replicate
-
-# Now import models
-from transformers import AutoModel, AutoTokenizer
-from sentence_transformers import SentenceTransformer
-from nltk.tokenize import word_tokenize, sent_tokenize
-from tqdm import tqdm
 from explanation_generator import ExplanationGenerator
 
 # Download NLTK resources
@@ -386,11 +366,8 @@ class ResumeScreener:
         bm25_scores = self.calculate_bm25_scores(resume_texts, job_description)
         
         # Normalize BM25 scores
-        if bm25_scores and len(bm25_scores) > 0 and max(bm25_scores) > 0:
+        if max(bm25_scores) > 0:
             bm25_scores = [score / max(bm25_scores) for score in bm25_scores]
-        else:
-            # If BM25 scores are empty or all zero, use neutral values
-            bm25_scores = [0.5] * len(resume_texts)
         
         # Calculate hybrid scores
         keyword_weight = 1.0 - semantic_weight

explanation_generator.py

@@ -6,28 +6,9 @@ using the QwQ-32B model from Hugging Face.
 """
 
 import torch
-# Fix for Replicate issue - must be before model imports
-try:
-    from einops.layers.torch import Replicate
-except ImportError:
-    # Define our own Replicate class if not available
-    class Replicate(torch.nn.Module):
-        def __init__(self, *args, **kwargs):
-            super().__init__()
-        def forward(self, x):
-            return x
-
-# Make sure Replicate is available in the right module
-import sys
-import einops.layers.torch
-einops.layers.torch.Replicate = Replicate
-sys.modules['einops.layers.torch'].Replicate = Replicate
-
 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
 import os
 import re
-from einops.layers.torch import Rearrange, Reduce
-from einops import rearrange, reduce, repeat
 
 # Load QwQ model at initialization time
 print("Loading Qwen/QwQ-32B model with 4-bit quantization...")

requirements.txt

@@ -15,6 +15,6 @@ pandas==2.1.3
 numpy==1.24.3
 tqdm==4.66.1
 huggingface-hub>=0.30.0,<1.0
-einops
+einops==0.5.0
 bitsandbytes>=0.41.0
 accelerate>=0.23.0