Update app.py

app.py

@@ -1,8 +1,17 @@
 import spacy
 from spacy.cli import download
 import nltk
+import gradio as gr
+import torch
+import re
+import PyPDF2
+import numpy as np
+import pandas as pd
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
 
-# Ensure punkt tokenizer is downloaded
+# Download necessary NLTK resources
 try:
     nltk.data.find('tokenizers/punkt')
 except LookupError:
@@ -17,27 +26,7 @@ except OSError:
     download("en_core_web_sm")
     nlp = spacy.load("en_core_web_sm")
 
-# Your main application logic follows here
-
-
-
-import gradio as gr
-import torch
-import spacy
-import nltk
-import re
-import PyPDF2
-import numpy as np
-import pandas as pd
-from transformers import pipeline
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
-
-# Download necessary NLTK resources
-nltk.download('punkt')
-
-# Load spaCy and Sentence Transformer models
-nlp = spacy.load('en_core_web_sm')
+# Load Sentence Transformer model
 embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
 
 # Check for GPU availability
@@ -94,8 +83,7 @@ CAREER_DATABASE = {
     }
 }
 
-# Function definitions remain unchanged
-
+# Function to extract text from PDF
 def extract_text_from_pdf(file_path):
     try:
         with open(file_path, 'rb') as file:
@@ -106,8 +94,12 @@ def extract_text_from_pdf(file_path):
         print(f"Error extracting PDF text: {e}")
         return ""
 
-# Other functions are unchanged
+# Placeholder function for CV skill analysis
+def analyze_cv_skills(cv_text):
+    # Implement skill analysis and career recommendations
+    return "Skill analysis and recommendations coming soon!"
 
+# Function to process CV and provide recommendations
 def cv_skill_assessment(cv_file):
     try:
         cv_text = extract_text_from_pdf(cv_file.name)