Create app.py

app.py

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+# Import necessary libraries
+import streamlit as st
+import nltk
+from gensim.models.doc2vec import Doc2Vec, TaggedDocument
+from nltk.tokenize import word_tokenize
+import PyPDF2
+import pandas as pd
+import re
+import matplotlib.pyplot as plt
+import seaborn as sns
+import spacy
+
+# Download necessary NLTK data
+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)
+    text = ""
+    for page_num in range(len(pdf_reader.pages)):
+        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
+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())]
+    return skills
+
+# Function to preprocess text
+def preprocess_text(text):
+    return word_tokenize(text.lower())
+
+# Function to train a Doc2Vec model
+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)
+    return model
+
+# Function to calculate similarity between two texts
+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
+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
+
+# 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)
+
+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"])
+
+    if job_descriptions_file:
+        # Load the pre-trained NLP model
+        nlp_model_path = "en_Resume_Matching_Keywords"
+        nlp = spacy.load(nlp_model_path)
+
+        # 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)]
+        model_resumes = train_doc2vec_model(tagged_resumes)
+
+        st.subheader("\nHeatmap:")
+       
+        # 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()]
+
+        if skills_keywords:
+            # Calculate the similarity score between each skill keyword and the resume text
+            skills_similarity_scores = []
+            for resume_text in resumes_texts:
+                resume_text_similarity_scores = []
+                for skill in skills_keywords:
+                    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])
+
+            # Plot the heatmap
+            fig, ax = plt.subplots(figsize=(12, 8))
+            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')
+
+            # Rotate the y-axis labels for better readability
+            plt.yticks(rotation=0)
+
+            # Display the Matplotlib figure using st.pyplot()
+            st.pyplot(fig)
+        else:
+            st.write("Please enter at least one skill keyword.")
+
+    else:
+        st.warning("Please upload the Job Description PDF to proceed.")
+else:
+    st.warning("Please upload Resumes PDF to proceed.")