ss

README.md

@@ -12,16 +12,74 @@ license: mit
 
 # Resume Screener and Skill Extractor
 
-An intelligent resume screening application that analyzes resumes and extracts relevant skills based on job positions. Built with Streamlit and Hugging Face transformers.
+A comprehensive application for analyzing resumes, matching them to job positions, and providing personalized career advice.
 
 ## Features
 
-- Upload PDF or DOCX resumes
-- Select from multiple job positions
-- Automatic skill matching with percentage score
-- AI-powered resume summarization
-- Skills gap analysis
-- Modern, user-friendly interface
+- **Skill Extraction**: Identifies relevant skills for specific job positions
+- **Resume Summarization**: Generates concise summaries of candidate backgrounds
+- **Skill Gap Analysis**: Identifies missing skills for target roles
+- **Career Advice**: Provides personalized recommendations for skill development and projects
+- **Experience Analysis**: Analyzes work history and career progression
+- **Fraud Detection**: Flags potential inconsistencies for verification
+
+## Installation
+
+### Fix Dependencies (Recommended)
+
+If you encounter any dependency issues, run the dependency fixer script:
+
+```bash
+python fix_dependencies.py
+```
+
+This will install compatible versions of all required packages.
+
+### Manual Installation
+
+Alternatively, you can install the dependencies manually:
+
+```bash
+pip install -r requirements.txt
+python -m spacy download en_core_web_sm
+python -c "import nltk; nltk.download('punkt')"
+```
+
+## Common Issues and Solutions
+
+### ImportError: cannot import name 'cached_download' from 'huggingface_hub'
+
+This occurs due to version incompatibility between huggingface_hub and sentence_transformers. To fix:
+
+1. Run the dependency fixer script: `python fix_dependencies.py`
+2. Or manually install compatible versions: `pip install huggingface-hub==0.14.1 sentence-transformers==2.2.2`
+
+## Running the Application
+
+```bash
+streamlit run app.py
+```
+
+## Usage
+
+1. Upload a resume in PDF format
+2. Select a target job position
+3. Review the analysis results in the different tabs
+4. Click "Generate Personalized Career Advice" to get recommendations
+
+## Dependencies
+
+- streamlit
+- pdfplumber
+- spacy
+- transformers
+- sentence-transformers
+- torch
+- nltk
+- plotly
+- pandas
+- numpy
+- matplotlib
 
 ## Supported Job Positions
 

app.py

@@ -5,7 +5,7 @@ import spacy
 import re
 import pandas as pd
 import matplotlib.pyplot as plt
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+from transformers import pipeline
 from sentence_transformers import SentenceTransformer, util
 import subprocess
 import sys
@@ -18,6 +18,18 @@ import plotly.graph_objects as go
 import numpy as np
 from collections import defaultdict
 
+# Fix for huggingface_hub import issue
+try:
+    # For newer versions of huggingface_hub
+    from huggingface_hub import hf_hub_download
+except ImportError:
+    try:
+        # For older versions of huggingface_hub
+        from huggingface_hub import cached_download as hf_hub_download
+    except ImportError:
+        st.error("Could not import required functions from huggingface_hub. Please check your installation.")
+        hf_hub_download = None
+
 # Initialize NLTK
 @st.cache_resource
 def download_nltk_resources():
@@ -57,24 +69,10 @@ def load_models():
         st.error(f"Failed to load sentence transformer: {str(e)}")
         sentence_model = None
     
-    # Load Qwen3-8B model for career advice
-    try:
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        qwen_tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
-        qwen_model = AutoModelForCausalLM.from_pretrained(
-            "Qwen/Qwen3-8B",
-            torch_dtype=torch.float16 if device == "cuda" else torch.float32,
-            device_map="auto"
-        )
-    except Exception as e:
-        st.error(f"Failed to load Qwen3-8B model: {str(e)}")
-        qwen_tokenizer = None
-        qwen_model = None
-    
-    return summarizer, nlp, sentence_model, qwen_tokenizer, qwen_model
+    return summarizer, nlp, sentence_model
 
 # Initialize models
-summarizer, nlp, sentence_model, qwen_tokenizer, qwen_model = load_models()
+summarizer, nlp, sentence_model = load_models()
 
 # Job descriptions and required skills
 job_descriptions = {
@@ -483,56 +481,154 @@ def analyze_resume(text, job_title, sentence_model):
         "semantic_score": semantic_score
     }
 
-def generate_career_advice(resume_text, job_title, analysis_results):
-    if qwen_model is None or qwen_tokenizer is None:
-        return "Career advice model not available. Please check the model installation."
-    
-    # Get missing skills
-    missing_skills = [skill for skill in job_descriptions[job_title]["skills"] 
-                    if skill not in analysis_results["found_skills"]]
+def generate_career_advice(resume_text, job_title, found_skills, missing_skills):
+    """
+    Generate career advice using a template-based approach instead of Qwen3-8B
+    to avoid dependency issues
+    """
+    # Template-based advice generation
+    advice = f"""## Career Development Plan for {job_title} Position
+
+### Skills to Develop
+
+The following skills would strengthen your resume for this position:
+
+"""
     
-    # Create a prompt for the model
-    prompt = f"""
-You are a professional career advisor. Based on the resume and the target job position, 
-provide personalized advice on skills to develop and suggest projects that would help the candidate 
-become a better fit for the position.
+    # Add advice for each missing skill
+    for skill in missing_skills:
+        if skill == "python":
+            advice += f"""#### Python
+- **How to develop**: Take online courses focused on Python for {job_title.lower()} applications
+- **Project idea**: Build a data analysis tool or web application using Python and popular frameworks
+- **Resources**: Coursera's Python for Everybody, Python.org tutorials, Real Python website
 
-Resume summary: {analysis_results["summary"]}
+"""
+        elif skill == "java":
+            advice += f"""#### Java
+- **How to develop**: Complete a comprehensive Java course with practical exercises
+- **Project idea**: Develop a backend service with Spring Boot
+- **Resources**: Oracle's Java tutorials, Udemy courses on Java, "Effective Java" by Joshua Bloch
 
-Target position: {job_title}
+"""
+        elif skill == "javascript":
+            advice += f"""#### JavaScript
+- **How to develop**: Practice with modern JavaScript frameworks
+- **Project idea**: Create an interactive web application with React or Vue.js
+- **Resources**: MDN Web Docs, freeCodeCamp, "Eloquent JavaScript" by Marijn Haverbeke
 
-Job requirements: {job_descriptions[job_title]["description"]}
+"""
+        elif skill == "sql":
+            advice += f"""#### SQL
+- **How to develop**: Practice with database design and complex queries
+- **Project idea**: Design a database system for a small business with reports and analytics
+- **Resources**: SQLZoo, Mode Analytics SQL tutorial, W3Schools SQL course
 
-Skills the candidate has: {', '.join([f"{skill} ({analysis_results['skill_proficiencies'].get(skill, 'Basic')})" for skill in analysis_results["found_skills"]])}
+"""
+        elif "algorithms" in skill or "data structures" in skill:
+            advice += f"""#### Algorithms & Data Structures
+- **How to develop**: Solve coding problems regularly on platforms like LeetCode
+- **Project idea**: Implement classic algorithms and optimize them for specific use cases
+- **Resources**: "Cracking the Coding Interview" book, AlgoExpert, Coursera Algorithms specialization
+
+"""
+        elif "git" in skill:
+            advice += f"""#### Git & Version Control
+- **How to develop**: Contribute to open source projects to practice Git workflows
+- **Project idea**: Set up a personal project with proper branching strategies and CI/CD
+- **Resources**: Git documentation, GitHub Learning Lab, Atlassian Git tutorials
 
-Skills the candidate needs to develop: {', '.join(missing_skills)}
+"""
+        elif "cloud" in skill:
+            advice += f"""#### Cloud Technologies
+- **How to develop**: Get hands-on experience with a major cloud provider (AWS, Azure, GCP)
+- **Project idea**: Deploy an application to the cloud with proper infrastructure as code
+- **Resources**: Cloud provider documentation, A Cloud Guru courses, free tier accounts
 
-Current experience: {analysis_results["years_experience"]} years
-Current seniority level: {analysis_results["seniority_score"]}/10
-Potential next career moves: {', '.join(analysis_results["next_roles"])}
+"""
+        elif "ui" in skill or "ux" in skill:
+            advice += f"""#### UI/UX Design
+- **How to develop**: Study design principles and practice creating user interfaces
+- **Project idea**: Redesign an existing website or app with focus on user experience
+- **Resources**: Nielsen Norman Group articles, Interaction Design Foundation, Figma tutorials
 
-Provide the following:
-1. Specific advice on how to develop the missing skills
-2. 3-5 project ideas that would showcase these skills and align with the candidate's career trajectory
-3. Resources for learning (courses, books, websites)
-4. Suggestions on how to position existing experience for this role
 """
+        elif "machine learning" in skill:
+            advice += f"""#### Machine Learning
+- **How to develop**: Take courses on ML fundamentals and practice with datasets
+- **Project idea**: Build a predictive model to solve a real-world problem
+- **Resources**: Andrew Ng's Coursera courses, Kaggle competitions, "Hands-On Machine Learning" book
 
-    # Generate advice using Qwen3-8B
-    try:
-        inputs = qwen_tokenizer(prompt, return_tensors="pt").to(qwen_model.device)
-        with torch.no_grad():
-            outputs = qwen_model.generate(
-                **inputs,
-                max_new_tokens=1024,
-                temperature=0.7,
-                top_p=0.9,
-                do_sample=True
-            )
-        advice = qwen_tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
-        return advice
-    except Exception as e:
-        return f"Failed to generate career advice: {str(e)}"
+"""
+        elif "data analysis" in skill:
+            advice += f"""#### Data Analysis
+- **How to develop**: Practice analyzing datasets and creating visualizations
+- **Project idea**: Perform an exploratory data analysis on a public dataset
+- **Resources**: DataCamp courses, Kaggle datasets, "Python for Data Analysis" by Wes McKinney
+
+"""
+        else:
+            advice += f"""#### {skill.title()}
+- **How to develop**: Research industry best practices and take relevant courses
+- **Project idea**: Create a portfolio piece that showcases this skill
+- **Resources**: Online courses, industry blogs, and practice projects
+
+"""
+    
+    # Add project recommendations based on job title
+    advice += f"""
+### Recommended Projects for {job_title}
+
+Based on the target position and the skills needed, here are some project ideas:
+
+"""
+    if job_title == "Software Engineer":
+        advice += """
+1. **Full-Stack Web Application**: Build a complete web app with frontend, backend, and database
+2. **API Service**: Create a RESTful or GraphQL API with proper authentication and documentation
+3. **Mobile Application**: Develop a cross-platform mobile app using React Native or Flutter
+4. **Automation Tools**: Build scripts or applications that automate repetitive tasks
+5. **Contribution to Open Source**: Find a project aligned with your skills and contribute meaningfully
+
+"""
+    elif job_title == "Interaction Designer":
+        advice += """
+1. **Design System**: Create a comprehensive design system with components and usage guidelines
+2. **Website Redesign**: Redesign an existing website with focus on improved UX
+3. **Mobile App Prototype**: Design a fully interactive mobile app prototype
+4. **User Research Project**: Conduct user research and create a report with insights and recommendations
+5. **Design Case Study**: Document your design process for solving a specific problem
+
+"""
+    elif job_title == "Data Scientist":
+        advice += """
+1. **Predictive Model**: Build a machine learning model that solves a real-world problem
+2. **Data Visualization Dashboard**: Create an interactive dashboard to visualize complex data
+3. **Natural Language Processing**: Develop a text analysis or sentiment analysis project
+4. **Time Series Analysis**: Analyze time-based data and build forecasting models
+5. **A/B Testing Framework**: Design and implement a framework for testing hypotheses
+
+"""
+    
+    # General advice for all positions
+    advice += """
+### Learning Resources
+
+- **Online Platforms**: Coursera, Udemy, Pluralsight, LinkedIn Learning
+- **Documentation**: Official language and framework documentation
+- **Communities**: Stack Overflow, GitHub, Reddit programming communities
+- **Books**: O'Reilly publications specific to your target technologies
+- **YouTube Channels**: Traversy Media, Tech With Tim, freeCodeCamp
+
+### Positioning Your Experience
+
+- Highlight transferable skills from your current experience
+- Quantify achievements with metrics where possible
+- Frame previous work in terms relevant to the target position
+- Create a tailored resume that emphasizes relevant projects and responsibilities
+"""
+    
+    return advice
 
 # Streamlit UI
 st.title("📄 Comprehensive Resume Analyzer")
@@ -928,7 +1024,7 @@ if uploaded_file and job_title:
             
             if st.button("Generate Personalized Career Advice"):
                 with st.spinner("Generating detailed career advice and development plan..."):
-                    advice = generate_career_advice(text, job_title, analysis_results)
+                    advice = generate_career_advice(text, job_title, analysis_results["found_skills"], missing_skills)
                     st.markdown(advice)
         
     except Exception as e:

fix_dependencies.py

@@ -0,0 +1,42 @@
+import subprocess
+import sys
+
+def fix_dependencies():
+    """
+    Fix dependency issues by installing compatible versions of required packages
+    """
+    print("Fixing dependencies for Resume Screener application...")
+    
+    # List of compatible package versions
+    packages = [
+        "streamlit==1.22.0",
+        "pdfplumber==0.9.0",
+        "spacy==3.5.0",
+        "transformers==4.28.1",
+        "torch>=1.13.1",
+        "huggingface-hub==0.14.1",
+        "sentence-transformers==2.2.2",
+        "nltk==3.8.1",
+        "plotly==5.14.1",
+        "pandas==1.5.3",
+        "numpy==1.24.3",
+        "matplotlib==3.7.1"
+    ]
+    
+    # Install each package
+    for package in packages:
+        print(f"Installing {package}...")
+        subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+    
+    # Download spaCy model
+    print("Downloading spaCy model...")
+    subprocess.check_call([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])
+    
+    # Download NLTK data
+    print("Downloading NLTK data...")
+    subprocess.check_call([sys.executable, "-c", "import nltk; nltk.download('punkt')"])
+    
+    print("Dependencies fixed successfully!")
+
+if __name__ == "__main__":
+    fix_dependencies() 

requirements.txt

@@ -1,14 +1,12 @@
-streamlit==1.29.0
-pdfplumber==0.10.2
-spacy==3.7.2
-transformers==4.36.2
+streamlit==1.22.0
+pdfplumber==0.9.0
+spacy==3.5.0
+transformers==4.28.1
+torch>=1.13.1
+huggingface-hub==0.14.1
 sentence-transformers==2.2.2
-torch==2.1.2
 nltk==3.8.1
-pandas==2.1.4
-matplotlib==3.8.2
-plotly==5.18.0
-numpy==1.26.2
-python-docx==1.0.1
-huggingface-hub==0.19.4
-accelerate==0.25.0
+plotly==5.14.1
+pandas==1.5.3
+numpy==1.24.3
+matplotlib==3.7.1