ss

app.py

@@ -6,7 +6,41 @@ import re
 import pandas as pd
 import matplotlib.pyplot as plt
 from transformers import pipeline
-from sentence_transformers import SentenceTransformer, util
+# Import SentenceTransformer with try-except
+try:
+    from sentence_transformers import SentenceTransformer
+    # Try to import util, if it fails, we'll create our own minimal version
+    try:
+        from sentence_transformers import util
+    except ImportError:
+        # Create a minimal util module replacement with the functions we need
+        class util:
+            @staticmethod
+            def pytorch_cos_sim(a, b):
+                """
+                Compute cosine similarity between two PyTorch tensors
+                """
+                import torch
+                if not isinstance(a, torch.Tensor):
+                    a = torch.tensor(a)
+                if not isinstance(b, torch.Tensor):
+                    b = torch.tensor(b)
+                
+                if len(a.shape) == 1:
+                    a = a.unsqueeze(0)
+                if len(b.shape) == 1:
+                    b = b.unsqueeze(0)
+                
+                a_norm = torch.nn.functional.normalize(a, p=2, dim=1)
+                b_norm = torch.nn.functional.normalize(b, p=2, dim=1)
+                return torch.mm(a_norm, b_norm.transpose(0, 1))
+except ImportError:
+    st.error("Failed to import SentenceTransformer. Semantic matching will be disabled.")
+    SentenceTransformer = None
+    class util:
+        @staticmethod
+        def pytorch_cos_sim(*args, **kwargs):
+            return 0
 import subprocess
 import sys
 import torch
@@ -59,15 +93,26 @@ def download_spacy_model():
 # Load the NLP models
 @st.cache_resource
 def load_models():
-    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-    nlp = download_spacy_model()
+    try:
+        summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    except Exception as e:
+        st.error(f"Failed to load summarization model: {str(e)}")
+        # Fallback to a simpler summarizer that just takes the first few sentences
+        summarizer = lambda text, **kwargs: [{"summary_text": ". ".join(text.split(". ")[:3]) + "."}]
     
-    # Load sentence transformer for semantic matching
     try:
-        sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
+        nlp = download_spacy_model()
     except Exception as e:
-        st.error(f"Failed to load sentence transformer: {str(e)}")
-        sentence_model = None
+        st.error(f"Failed to load spaCy model: {str(e)}")
+        nlp = None
+    
+    # Load sentence transformer for semantic matching
+    sentence_model = None
+    if SentenceTransformer is not None:
+        try:
+            sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
+        except Exception as e:
+            st.error(f"Failed to load sentence transformer: {str(e)}")
     
     return summarizer, nlp, sentence_model
 
@@ -461,7 +506,7 @@ def analyze_resume(text, job_title, sentence_model):
     
     # Semantic matching with job description
     semantic_score = 0
-    if sentence_model:
+    if sentence_model is not None and SentenceTransformer is not None:
         try:
             resume_embedding = sentence_model.encode(text[:5000])  # Limit to first 5000 chars to avoid memory issues
             job_embedding = sentence_model.encode(job_descriptions[job_title]["semantic_description"])

fix_dependencies.py

@@ -13,7 +13,7 @@ def fix_dependencies():
         "pdfplumber==0.9.0",
         "spacy==3.5.0",
         "transformers==4.28.1",
-        "torch>=1.13.1",
+        "torch==1.13.1",
         "huggingface-hub==0.14.1",
         "sentence-transformers==2.2.2",
         "nltk==3.8.1",
@@ -21,7 +21,12 @@ def fix_dependencies():
         "pandas==1.5.3",
         "numpy==1.24.3",
         "matplotlib==3.7.1",
-        "pydantic<2.0.0"
+        "pydantic==1.10.8",
+        "protobuf<4.0.0",
+        "tqdm>=4.27",
+        "regex>=2022.1.18",
+        "scikit-learn==1.0.2",
+        "scipy==1.8.1"
     ]
     
     # Install each package

requirements.txt

@@ -2,7 +2,7 @@ streamlit==1.22.0
 pdfplumber==0.9.0
 spacy==3.5.0
 transformers==4.28.1
-torch>=1.13.1
+torch==1.13.1
 huggingface-hub==0.14.1
 sentence-transformers==2.2.2
 nltk==3.8.1
@@ -10,4 +10,9 @@ plotly==5.14.1
 pandas==1.5.3
 numpy==1.24.3
 matplotlib==3.7.1
-pydantic<2.0.0
+pydantic==1.10.8
+protobuf<4.0.0
+tqdm>=4.27
+regex>=2022.1.18
+scikit-learn==1.0.2
+scipy==1.8.1