Upload 4 files

Courses.py

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+ds_course = [['Machine Learning Crash Course by Google [Free]', 'https://developers.google.com/machine-learning/crash-course'],
+             ['Machine Learning A-Z by Udemy','https://www.udemy.com/course/machinelearning/'],
+             ['Machine Learning by Andrew NG','https://www.coursera.org/learn/machine-learning'],
+             ['Data Scientist Master Program of Simplilearn (IBM)','https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training'],
+             ['Data Science Foundations: Fundamentals by LinkedIn','https://www.linkedin.com/learning/data-science-foundations-fundamentals-5'],
+             ['Data Scientist with Python','https://www.datacamp.com/tracks/data-scientist-with-python'],
+             ['Programming for Data Science with Python','https://www.udacity.com/course/programming-for-data-science-nanodegree--nd104'],
+             ['Programming for Data Science with R','https://www.udacity.com/course/programming-for-data-science-nanodegree-with-R--nd118'],
+             ['Introduction to Data Science','https://www.udacity.com/course/introduction-to-data-science--cd0017'],
+             ['Intro to Machine Learning with TensorFlow','https://www.udacity.com/course/intro-to-machine-learning-with-tensorflow-nanodegree--nd230']]
+
+web_course = [['Django Crash course [Free]','https://youtu.be/e1IyzVyrLSU'],
+              ['Python and Django Full Stack Web Developer Bootcamp','https://www.udemy.com/course/python-and-django-full-stack-web-developer-bootcamp'],
+              ['React Crash Course [Free]','https://youtu.be/Dorf8i6lCuk'],
+              ['ReactJS Project Development Training','https://www.dotnettricks.com/training/masters-program/reactjs-certification-training'],
+              ['Full Stack Web Developer - MEAN Stack','https://www.simplilearn.com/full-stack-web-developer-mean-stack-certification-training'],
+              ['Node.js and Express.js [Free]','https://youtu.be/Oe421EPjeBE'],
+              ['Flask: Develop Web Applications in Python','https://www.educative.io/courses/flask-develop-web-applications-in-python'],
+              ['Full Stack Web Developer by Udacity','https://www.udacity.com/course/full-stack-web-developer-nanodegree--nd0044'],
+              ['Front End Web Developer by Udacity','https://www.udacity.com/course/front-end-web-developer-nanodegree--nd0011'],
+              ['Become a React Developer by Udacity','https://www.udacity.com/course/react-nanodegree--nd019']]
+
+android_course = [['Android Development for Beginners [Free]','https://youtu.be/fis26HvvDII'],
+                  ['Android App Development Specialization','https://www.coursera.org/specializations/android-app-development'],
+                  ['Associate Android Developer Certification','https://grow.google/androiddev/#?modal_active=none'],
+                  ['Become an Android Kotlin Developer by Udacity','https://www.udacity.com/course/android-kotlin-developer-nanodegree--nd940'],
+                  ['Android Basics by Google','https://www.udacity.com/course/android-basics-nanodegree-by-google--nd803'],
+                  ['The Complete Android Developer Course','https://www.udemy.com/course/complete-android-n-developer-course/'],
+                  ['Building an Android App with Architecture Components','https://www.linkedin.com/learning/building-an-android-app-with-architecture-components'],
+                  ['Android App Development Masterclass using Kotlin','https://www.udemy.com/course/android-oreo-kotlin-app-masterclass/'],
+                  ['Flutter & Dart - The Complete Flutter App Development Course','https://www.udemy.com/course/flutter-dart-the-complete-flutter-app-development-course/'],
+                  ['Flutter App Development Course [Free]','https://youtu.be/rZLR5olMR64']]
+
+ios_course = [['IOS App Development by LinkedIn','https://www.linkedin.com/learning/subscription/topics/ios'],
+              ['iOS & Swift - The Complete iOS App Development Bootcamp','https://www.udemy.com/course/ios-13-app-development-bootcamp/'],
+              ['Become an iOS Developer','https://www.udacity.com/course/ios-developer-nanodegree--nd003'],
+              ['iOS App Development with Swift Specialization','https://www.coursera.org/specializations/app-development'],
+              ['Mobile App Development with Swift','https://www.edx.org/professional-certificate/curtinx-mobile-app-development-with-swift'],
+              ['Swift Course by LinkedIn','https://www.linkedin.com/learning/subscription/topics/swift-2'],
+              ['Objective-C Crash Course for Swift Developers','https://www.udemy.com/course/objectivec/'],
+              ['Learn Swift by Codecademy','https://www.codecademy.com/learn/learn-swift'],
+              ['Swift Tutorial - Full Course for Beginners [Free]','https://youtu.be/comQ1-x2a1Q'],
+              ['Learn Swift Fast - [Free]','https://youtu.be/FcsY1YPBwzQ']]
+uiux_course = [['Google UX Design Professional Certificate','https://www.coursera.org/professional-certificates/google-ux-design'],
+               ['UI / UX Design Specialization','https://www.coursera.org/specializations/ui-ux-design'],
+               ['The Complete App Design Course - UX, UI and Design Thinking','https://www.udemy.com/course/the-complete-app-design-course-ux-and-ui-design/'],
+               ['UX & Web Design Master Course: Strategy, Design, Development','https://www.udemy.com/course/ux-web-design-master-course-strategy-design-development/'],
+               ['The Complete App Design Course - UX, UI and Design Thinking','https://www.udemy.com/course/the-complete-app-design-course-ux-and-ui-design/'],
+               ['DESIGN RULES: Principles + Practices for Great UI Design','https://www.udemy.com/course/design-rules/'],
+               ['Become a UX Designer by Udacity','https://www.udacity.com/course/ux-designer-nanodegree--nd578'],
+               ['Adobe XD Tutorial: User Experience Design Course [Free]','https://youtu.be/68w2VwalD5w'],
+               ['Adobe XD for Beginners [Free]','https://youtu.be/WEljsc2jorI'],
+               ['Adobe XD in Simple Way','https://learnux.io/course/adobe-xd']]
+
+resume_videos = ['https://youtu.be/y8YH0Qbu5h4','https://youtu.be/J-4Fv8nq1iA',
+                 'https://youtu.be/yp693O87GmM','https://youtu.be/UeMmCex9uTU',
+                 'https://youtu.be/dQ7Q8ZdnuN0','https://youtu.be/HQqqQx5BCFY',
+                 'https://youtu.be/CLUsplI4xMU','https://youtu.be/pbczsLkv7Cc']
+
+interview_videos = ['https://youtu.be/Ji46s5BHdr0','https://youtu.be/seVxXHi2YMs',
+                    'https://youtu.be/9FgfsLa_SmY','https://youtu.be/2HQmjLu-6RQ',
+                    'https://youtu.be/DQd_AlIvHUw','https://youtu.be/oVVdezJ0e7w'
+                    'https://youtu.be/JZK1MZwUyUU','https://youtu.be/CyXLhHQS3KY']

README.md

@@ -1,10 +1,10 @@
 ---
-title: Shortlist Rank Resumes
-emoji: 🐠
-colorFrom: gray
-colorTo: purple
+title: Resume Analyser
+emoji: 🐨
+colorFrom: pink
+colorTo: pink
 sdk: streamlit
-sdk_version: 1.31.0
+sdk_version: 1.28.1
 app_file: app.py
 pinned: false
 ---

app.py

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+import streamlit as st
+import os
+import fitz  # PyMuPDF
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from spellchecker import SpellChecker  # Import the SpellChecker class from pyspellchecker
+import tempfile
+import pandas as pd
+
+st.title("RESUME RANKER")
+
+# User input for skills
+skills = st.text_input("Enter Skills (comma-separated):")
+
+# User input for job description
+job_description = st.text_area("Enter Job Description:")
+
+# User input for uploading multiple PDF resumes
+pdf_resumes = st.file_uploader("Upload Resumes/CVs", type=["pdf"], accept_multiple_files=True)
+
+if st.button("Rank Resumes"):
+    if not pdf_resumes:
+        st.warning("Please upload PDF resumes.")
+    else:
+        skills = [skill.strip() for skill in skills.split(',')]
+        job_description = job_description.lower()
+        resume_data = []
+
+        # Function to extract text from a PDF file using PyMuPDF (fitz)
+        def extract_text_from_pdf(pdf_path):
+            text = ""
+            with fitz.open(pdf_path) as doc:
+                for page in doc:
+                    text += page.get_text()
+            return text
+
+        # Loop through all uploaded PDF resumes and extract text
+        for pdf_resume in pdf_resumes:
+            if pdf_resume.type == "application/pdf":
+                with tempfile.NamedTemporaryFile(delete=False) as temp_pdf:
+                    temp_pdf.write(pdf_resume.read())
+                    temp_pdf_name = temp_pdf.name
+                pdf_text = extract_text_from_pdf(temp_pdf_name).lower()
+                resume_data.append((pdf_resume.name, pdf_text))
+                os.remove(temp_pdf_name)
+
+        if not resume_data:
+            st.warning("No PDF resumes found in the uploaded files.")
+        else:
+            resume_rankings = []
+
+            # Loop through resumes and check for the presence of input skills
+            for resume_name, resume_text in resume_data:
+                matching_skills = [skill for skill in skills if skill.lower() in resume_text]
+                similarity_score =( len(matching_skills) / len(skills)  )# Calculate a simple similarity score
+                missing_skills = [skill for skill in skills if skill.lower() not in resume_text]
+
+                # Calculate the cosine similarity between job description and resume
+                tfidf_vectorizer = TfidfVectorizer()
+                job_description_matrix = tfidf_vectorizer.fit_transform([job_description])
+                resume_matrix = tfidf_vectorizer.transform([resume_text])
+                job_description_similarity = cosine_similarity(job_description_matrix, resume_matrix)
+                job_description_similarity = (job_description_similarity[0][0])
+                
+                similarity_score = round(similarity_score * 100, 2)
+                job_description_similarity = round(job_description_similarity * 100, 2)
+
+                resume_rankings.append((resume_name, f"{similarity_score}%", f"{job_description_similarity}%", missing_skills))
+
+            # Sort the resumes by similarity score in descending order
+            resume_rankings.sort(key=lambda x: x[1], reverse=True)
+
+            # Create a DataFrame to display the results
+            df = pd.DataFrame(resume_rankings, columns=["File Name", "Skills Match ", "Job Description Match ", "Missing Skills"])
+            
+
+           
+            st.subheader("Ranked Resumes:")
+            st.dataframe(df)

requirements.txt

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+PyMuPDF
+pyspellchecker
+scikit-learn