Update app.py

app.py

@@ -1,4 +1,5 @@
-# Import necessary libraries
+# Importing necessary libraries
+from collections import Counter
 import streamlit as st
 import nltk
 from gensim.models.doc2vec import Doc2Vec, TaggedDocument
@@ -8,17 +9,10 @@ import pandas as pd
 import re
 import matplotlib.pyplot as plt
 import seaborn as sns
-import spacy
 
-# Download necessary NLTK data
+# Downloading the 'punkt' tokenizer from NLTK
 nltk.download('punkt')
 
-# Define regular expressions for pattern matching
-float_regex = re.compile(r'^\d{1,2}(\.\d{1,2})?$')
-email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
-float_digit_regex = re.compile(r'^\d{10}$')
-email_with_phone_regex = re.compile(r'(\d{10}).|.(\d{10})')
-
 # Function to extract text from a PDF file
 def extract_text_from_pdf(pdf_file):
     pdf_reader = PyPDF2.PdfReader(pdf_file)
@@ -27,182 +21,222 @@ def extract_text_from_pdf(pdf_file):
         text += pdf_reader.pages[page_num].extract_text()
     return text
 
-# Function to tokenize text using the NLP model
-def tokenize_text(text, nlp_model):
-    doc = nlp_model(text, disable=["tagger", "parser"])
-    tokens = [(token.text.lower(), token.label_) for token in doc.ents]
-    return tokens
-
-# Function to extract CGPA from a resume
-def extract_cgpa(resume_text):
-    cgpa_pattern = r'\b(?:CGPA|GPA|C\.G\.PA|Cumulative GPA)\s*:?[\s-]([0-9]+(?:\.[0-9]+)?)\b|\b([0-9]+(?:\.[0-9]+)?)\s(?:CGPA|GPA)\b'
-    match = re.search(cgpa_pattern, resume_text, re.IGNORECASE)
-    if match:
-        cgpa = match.group(1) if match.group(1) else match.group(2)
-        return float(cgpa)
-    else:
-        return None
-
-# Function to extract skills from a resume
+# Function to extract skills from a text using a list of skill keywords
 def extract_skills(text, skills_keywords):
-    skills = [skill.lower() for skill in skills_keywords if re.search(r'\b' + re.escape(skill.lower()) + r'\b', text.lower())]
+    skills = [skill.lower()
+              for skill in skills_keywords if re.search(r'\b' + re.escape(skill.lower()) + r'\b', text.lower())]
     return skills
 
-# Function to preprocess text
+# Function to preprocess text by tokenizing and converting to lowercase
 def preprocess_text(text):
     return word_tokenize(text.lower())
 
-# Function to train a Doc2Vec model
+# Function to extract mobile numbers from a text
+def extract_mobile_numbers(text):
+    mobile_pattern = r'\b\d{10}\b|\b\d{3}[-.\s]?\d{3}[-.\s]?\d{4}\b'
+    return re.findall(mobile_pattern, text)
+
+# Function to extract emails from a text
+def extract_emails(text):
+    email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
+    return re.findall(email_pattern, text)
+
+# Function to train a Doc2Vec model on a list of tagged documents
 def train_doc2vec_model(documents):
     model = Doc2Vec(vector_size=20, min_count=2, epochs=50)
     model.build_vocab(documents)
-    model.train(documents, total_examples=model.corpus_count, epochs=model.epochs)
+    model.train(documents, total_examples=model.corpus_count,
+                epochs=model.epochs)
     return model
 
-# Function to calculate similarity between two texts
+# Function to calculate the cosine similarity between two texts using a trained Doc2Vec model
 def calculate_similarity(model, text1, text2):
     vector1 = model.infer_vector(preprocess_text(text1))
     vector2 = model.infer_vector(preprocess_text(text2))
     return model.dv.cosine_similarities(vector1, [vector2])[0]
 
-# Function to calculate accuracy
+# Function to calculate accuracy based on true positives, false positives, and false negatives
 def accuracy_calculation(true_positives, false_positives, false_negatives):
     total = true_positives + false_positives + false_negatives
     accuracy = true_positives / total if total != 0 else 0
     return accuracy
 
+# Function to extract CGPA from a text
+def extract_cgpa(resume_text):
+    # Define a regular expression pattern for CGPA extraction
+    cgpa_pattern = r'\b(?:CGPA|GPA|C.G.PA|Cumulative GPA)\s*:?[\s-]* ([0-9]+(?:\.[0-9]+)?)\b|\b([0-9]+(?:\.[0-9]+)?)\s*(?:CGPA|GPA)\b'
+
+    # Search for CGPA pattern in the text
+    match = re.search(cgpa_pattern, resume_text, re.IGNORECASE)
+
+    # Check if a match is found
+    if match:
+        cgpa = match.group(1)
+        if cgpa is not None:
+            return float(cgpa)
+        else:
+            return float(match.group(2))
+    else:
+        return None
+
+# Regular expressions for email and phone number patterns
+email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
+phone_pattern = r'\b\d{10}\b|\b\d{3}[-.\s]?\d{3}[-.\s]?\d{4}\b'
+
 # Streamlit Frontend
 st.markdown("# Resume Matching Tool 📃📃")
 st.markdown("An application to match resumes with a job description.")
 
 # Sidebar - File Upload for Resumes
 st.sidebar.markdown("## Upload Resumes PDF")
-resumes_files = st.sidebar.file_uploader("Upload Resumes PDF", type=["pdf"], accept_multiple_files=True)
+resumes_files = st.sidebar.file_uploader(
+    "Upload Resumes PDF", type=["pdf"], accept_multiple_files=True)
 
 if resumes_files:
     # Sidebar - File Upload for Job Descriptions
     st.sidebar.markdown("## Upload Job Description PDF")
-    job_descriptions_file = st.sidebar.file_uploader("Upload Job Description PDF", type=["pdf"])
+    job_descriptions_file = st.sidebar.file_uploader(
+        "Upload Job Description PDF", type=["pdf"])
 
     if job_descriptions_file:
-        # Load the pre-trained NLP model
-        nlp_model_path = "en_Resume_Matching_Keywords"
-        nlp = spacy.load(nlp_model_path)
+        # Sidebar - Sorting Options
+        sort_options = ['Weighted Score', 'Similarity Score']
+        selected_sort_option = st.sidebar.selectbox(
+            "Sort results by", sort_options)
 
         # Backend Processing
         job_description_text = extract_text_from_pdf(job_descriptions_file)
-        resumes_texts = [extract_text_from_pdf(resume_file) for resume_file in resumes_files]
-        job_description_text = extract_text_from_pdf(job_descriptions_file)
-        job_description_tokens = tokenize_text(job_description_text, nlp)
-
-        # Initialize counters
-        overall_skill_matches = 0
-        overall_qualification_matches = 0
-
-        # Create a list to store individual results
-        results_list = []
-        job_skills = set()
-        job_qualifications = set()
-
-        for job_token, job_label in job_description_tokens:
-            if job_label == 'QUALIFICATION':
-                job_qualifications.add(job_token.replace('\n', ' '))
-            elif job_label == 'SKILLS':
-                job_skills.add(job_token.replace('\n', ' '))
-
-        job_skills_number = len(job_skills)
-        job_qualifications_number = len(job_qualifications)
-
-        # Lists to store counts of matched skills for all resumes
-        skills_counts_all_resumes = []
-
-        # Iterate over all uploaded resumes
-        for uploaded_resume in resumes_files:
-            resume_text = extract_text_from_pdf(uploaded_resume)
-            resume_tokens = tokenize_text(resume_text, nlp)
-
-            # Initialize counters for individual resume
-            skillMatch = 0
-            qualificationMatch = 0
-            cgpa = ""
-
-            # Lists to store matched skills and qualifications for each resume
-            matched_skills = set()
-            matched_qualifications = set()
-            email = set()
-            phone = set()
-            name = set()
-
-            # Compare the tokens in the resume with the job description
-            for resume_token, resume_label in resume_tokens:
-                for job_token, job_label in job_description_tokens:
-                    if resume_token.lower().replace('\n', ' ') == job_token.lower().replace('\n', ' '):
-                        if resume_label == 'SKILLS':
-                            matched_skills.add(resume_token.replace('\n', ' '))
-                        elif resume_label == 'QUALIFICATION':
-                            matched_qualifications.add(resume_token.replace('\n', ' '))
-                    elif resume_label == 'PHONE' and bool(float_digit_regex.match(resume_token)):
-                        phone.add(resume_token)  
-                    elif resume_label == 'QUALIFICATION':
-                        matched_qualifications.add(resume_token.replace('\n', ' '))
-
-            skillMatch = len(matched_skills)
-            qualificationMatch = len(matched_qualifications)
-
-            # Convert the list of emails to a set
-            email_set = set(re.findall(email_pattern, resume_text.replace('\n', ' ')))
-            email.update(email_set)
-
-            numberphone=""
-            for email_str in email:
-                numberphone = email_with_phone_regex.search(email_str)
-                if numberphone:
-                    email.remove(email_str)
-                    val=numberphone.group(1) or numberphone.group(2)
-                    phone.add(val)
-                    email.add(email_str.strip(val))
-
-            # Increment overall counters based on matches
-            overall_skill_matches += skillMatch
-            overall_qualification_matches += qualificationMatch
-
-            # Add count of matched skills for this resume to the list
-            skills_counts_all_resumes.append([resume_text.count(skill.lower()) for skill in job_skills])
-
-            # Create a dictionary for the current resume and append to the results list
-            result_dict = {
-                "Resume": uploaded_resume.name,
-                "Similarity Score": (skillMatch/job_skills_number)*100,
-                "Skill Matches": skillMatch,
-                "Matched Skills": matched_skills,
-                "CGPA": extract_cgpa(resume_text),
-                "Email": email,
-                "Phone": phone,
-                "Qualification Matches": qualificationMatch,
-                "Matched Qualifications": matched_qualifications
-            }
-
-            results_list.append(result_dict)
-
-        # Display overall matches
-        st.subheader("Overall Matches")
-        st.write(f"Total Skill Matches: {overall_skill_matches}")
-        st.write(f"Total Qualification Matches: {overall_qualification_matches}")
-        st.write(f"Job Qualifications: {job_qualifications}")
-        st.write(f"Job Skills: {job_skills}")
-
-        # Display individual results in a table
-        results_df = pd.DataFrame(results_list)
-        st.subheader("Individual Results")
-        st.dataframe(results_df)
-        tagged_resumes = [TaggedDocument(words=preprocess_text(text), tags=[str(i)]) for i, text in enumerate(resumes_texts)]
+        resumes_texts = [extract_text_from_pdf(
+            resume_file) for resume_file in resumes_files]
+
+        tagged_resumes = [TaggedDocument(words=preprocess_text(
+            text), tags=[str(i)]) for i, text in enumerate(resumes_texts)]
         model_resumes = train_doc2vec_model(tagged_resumes)
 
+        true_positives_mobile = 0
+        false_positives_mobile = 0
+        false_negatives_mobile = 0
+
+        true_positives_email = 0
+        false_positives_email = 0
+        false_negatives_email = 0
+
+        results_data = {'Resume': [], 'Similarity Score': [],
+                        'Weighted Score': [], 'Email': [], 'Contact': [], 'CGPA': []}
+
+        for i, resume_text in enumerate(resumes_texts):
+            extracted_mobile_numbers = set(extract_mobile_numbers(resume_text))
+            extracted_emails = set(extract_emails(resume_text))
+            extracted_cgpa = extract_cgpa(resume_text)
+
+            ground_truth_mobile_numbers = {'1234567890', '9876543210'}
+            ground_truth_emails = {
+                '[email protected]', '[email protected]'}
+
+            true_positives_mobile += len(
+                extracted_mobile_numbers.intersection(ground_truth_mobile_numbers))
+            false_positives_mobile += len(
+                extracted_mobile_numbers.difference(ground_truth_mobile_numbers))
+            false_negatives_mobile += len(
+                ground_truth_mobile_numbers.difference(extracted_mobile_numbers))
+
+            true_positives_email += len(
+                extracted_emails.intersection(ground_truth_emails))
+            false_positives_email += len(
+                extracted_emails.difference(ground_truth_emails))
+            false_negatives_email += len(
+                ground_truth_emails.difference(extracted_emails))
+
+            similarity_score = calculate_similarity(
+                model_resumes, resume_text, job_description_text)
+
+            other_criteria_score = 0
+
+            weighted_score = (0.6 * similarity_score) + \
+                (0.4 * other_criteria_score)
+
+            results_data['Resume'].append(resumes_files[i].name)
+            results_data['Similarity Score'].append(similarity_score * 100)
+            results_data['Weighted Score'].append(weighted_score)
+
+            emails = ', '.join(re.findall(email_pattern, resume_text))
+            contacts = ', '.join(re.findall(phone_pattern, resume_text))
+            results_data['Email'].append(emails)
+            results_data['Contact'].append(contacts)
+            results_data['CGPA'].append(extracted_cgpa)
+
+        results_df = pd.DataFrame(results_data)
+
+        if selected_sort_option == 'Similarity Score':
+            results_df = results_df.sort_values(
+                by='Similarity Score', ascending=False)
+        else:
+            results_df = results_df.sort_values(
+                by='Weighted Score', ascending=False)
+
+        st.subheader(f"Results Table (Sorted by {selected_sort_option}):")
+
+        # Define a custom function to highlight maximum values in the specified columns
+        def highlight_max(data, color='grey'):
+            is_max = data == data.max()
+            return [f'background-color: {color}' if val else '' for val in is_max]
+
+        # Apply the custom highlighting function to the DataFrame
+        st.dataframe(results_df.style.apply(highlight_max, subset=[
+                     'Similarity Score', 'Weighted Score', 'CGPA']))
+
+
+        highest_score_index = results_df['Similarity Score'].idxmax()
+        highest_score_resume_name = resumes_files[highest_score_index].name
+
+        st.subheader("\nDetails of Highest Similarity Score Resume:")
+        st.write(f"Resume Name: {highest_score_resume_name}")
+        st.write(
+            f"Similarity Score: {results_df.loc[highest_score_index, 'Similarity Score']:.2f}")
+
+        if 'Weighted Score' in results_df.columns:
+            weighted_score_value = results_df.loc[highest_score_index,
+                                                  'Weighted Score']
+            st.write(f"Weighted Score: {weighted_score_value:.2f}" if pd.notnull(
+                weighted_score_value) else "Weighted Score: Not Mentioned")
+        else:
+            st.write("Weighted Score: Not Mentioned")
+
+        if 'Email' in results_df.columns:
+            email_value = results_df.loc[highest_score_index, 'Email']
+            st.write(f"Email: {email_value}" if pd.notnull(
+                email_value) else "Email: Not Mentioned")
+        else:
+            st.write("Email: Not Mentioned")
+
+        if 'Contact' in results_df.columns:
+            contact_value = results_df.loc[highest_score_index, 'Contact']
+            st.write(f"Contact: {contact_value}" if pd.notnull(
+                contact_value) else "Contact: Not Mentioned")
+        else:
+            st.write("Contact: Not Mentioned")
+
+        if 'CGPA' in results_df.columns:
+            cgpa_value = results_df.loc[highest_score_index, 'CGPA']
+            st.write(f"CGPA: {cgpa_value}" if pd.notnull(
+                cgpa_value) else "CGPA: Not Mentioned")
+        else:
+            st.write("CGPA: Not Mentioned")
+
+        mobile_accuracy = accuracy_calculation(
+            true_positives_mobile, false_positives_mobile, false_negatives_mobile)
+        email_accuracy = accuracy_calculation(
+            true_positives_email, false_positives_email, false_negatives_email)
+
         st.subheader("\nHeatmap:")
-       
+        # st.write(f"Mobile Number Accuracy: {mobile_accuracy:.2%}")
+        # st.write(f"Email Accuracy: {email_accuracy:.2%}")
+
         # Get skills keywords from user input
-        skills_keywords_input = st.text_input("Enter skills keywords separated by commas (e.g., python, java, machine learning):")
-        skills_keywords = [skill.strip() for skill in skills_keywords_input.split(',') if skill.strip()]
+        skills_keywords_input = st.text_input(
+            "Enter skills keywords separated by commas (e.g., python, java, machine learning):")
+        skills_keywords = [skill.strip()
+                        for skill in skills_keywords_input.split(',') if skill.strip()]
 
         if skills_keywords:
             # Calculate the similarity score between each skill keyword and the resume text
@@ -210,16 +244,20 @@ if resumes_files:
             for resume_text in resumes_texts:
                 resume_text_similarity_scores = []
                 for skill in skills_keywords:
-                    similarity_score = calculate_similarity(model_resumes, resume_text, skill)
+                    similarity_score = calculate_similarity(
+                        model_resumes, resume_text, skill)
                     resume_text_similarity_scores.append(similarity_score)
                 skills_similarity_scores.append(resume_text_similarity_scores)
 
             # Create a DataFrame with the similarity scores and set the index to the names of the PDFs
-            skills_similarity_df = pd.DataFrame(skills_similarity_scores, columns=skills_keywords, index=[resume_file.name for resume_file in resumes_files])
+            skills_similarity_df = pd.DataFrame(
+                skills_similarity_scores, columns=skills_keywords, index=[resume_file.name for resume_file in resumes_files])
 
             # Plot the heatmap
             fig, ax = plt.subplots(figsize=(12, 8))
-            sns.heatmap(skills_similarity_df, cmap='YlGnBu', annot=True, fmt=".2f", ax=ax)
+
+            sns.heatmap(skills_similarity_df,
+                        cmap='YlGnBu', annot=True, fmt=".2f", ax=ax)
             ax.set_title('Heatmap for Skills Similarity')
             ax.set_xlabel('Skills')
             ax.set_ylabel('Resumes')
@@ -232,6 +270,7 @@ if resumes_files:
         else:
             st.write("Please enter at least one skill keyword.")
 
+
     else:
         st.warning("Please upload the Job Description PDF to proceed.")
 else: