sss

README.md

@@ -1,13 +1,48 @@
 ---
-title: Resume Screener And Skill Extractor
-emoji: 🏆
-colorFrom: gray
-colorTo: pink
+title: Resume Screener and Skill Extractor
+emoji: 📄
+colorFrom: blue
+colorTo: green
 sdk: streamlit
-sdk_version: 1.42.0
+sdk_version: 1.31.0
 app_file: app.py
 pinned: false
 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.
+
+## 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
+
+## Supported Job Positions
+
+- Software Engineer
+- Interaction Designer
+- Data Scientist
+
+## How it Works
+
+1. Upload your resume (PDF or DOCX format)
+2. Select the target job position
+3. The app will analyze your resume and provide:
+   - A list of matched skills with a match percentage
+   - An AI-generated summary of your resume
+   - Suggestions for skills you might want to develop
+
+## Technologies Used
+
+- Streamlit for the web interface
+- Hugging Face Transformers for AI-powered text summarization
+- spaCy for natural language processing
+- PyPDF2 and python-docx for document parsing
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,4 +1,148 @@
 import streamlit as st
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.set_page_config(
+    page_title="Resume Screener & Skill Extractor",
+    page_icon="📄",
+    layout="wide"
+)
+
+# Load the NLP model
+@st.cache_resource
+def load_models():
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    nlp = spacy.load("en_core_web_sm")
+    return summarizer, nlp
+
+# Initialize models
+summarizer, nlp = load_models()
+
+# Job descriptions and required skills
+job_descriptions = {
+    "Software Engineer": {
+        "skills": ["python", "java", "javascript", "sql", "algorithms", "data structures", 
+                  "git", "cloud", "web development", "software development", "coding"],
+        "description": "Looking for software engineers with strong programming skills and experience in software development."
+    },
+    "Interaction Designer": {
+        "skills": ["ui", "ux", "user research", "wireframing", "prototyping", "figma", 
+                  "sketch", "adobe", "design thinking", "interaction design"],
+        "description": "Seeking interaction designers with expertise in user experience and interface design."
+    },
+    "Data Scientist": {
+        "skills": ["python", "r", "statistics", "machine learning", "data analysis", 
+                  "sql", "tensorflow", "pytorch", "pandas", "numpy"],
+        "description": "Looking for data scientists with strong analytical and machine learning skills."
+    }
+}
+
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+def extract_text_from_docx(docx_file):
+    doc = Document(docx_file)
+    text = ""
+    for paragraph in doc.paragraphs:
+        text += paragraph.text + "\n"
+    return text
+
+def analyze_resume(text, job_title):
+    # Extract relevant skills
+    doc = nlp(text.lower())
+    found_skills = []
+    required_skills = job_descriptions[job_title]["skills"]
+    
+    for skill in required_skills:
+        if skill in text.lower():
+            found_skills.append(skill)
+    
+    # Generate summary
+    chunks = [text[i:i + 1000] for i in range(0, len(text), 1000)]
+    summaries = []
+    for chunk in chunks[:3]:  # Process first 3000 characters to avoid token limits
+        summary = summarizer(chunk, max_length=150, min_length=50, do_sample=False)[0]["summary_text"]
+        summaries.append(summary)
+    
+    return found_skills, " ".join(summaries)
+
+# Streamlit UI
+st.title("📄 Resume Screener & Skill Extractor")
+
+# Add description
+st.markdown("""
+This app helps recruiters analyze resumes by:
+- Extracting relevant skills for specific job positions
+- Generating a concise summary of the candidate's background
+- Identifying skill gaps for the selected role
+""")
+
+# Create two columns
+col1, col2 = st.columns([2, 1])
+
+with col1:
+    # File upload
+    uploaded_file = st.file_uploader("Upload Resume (PDF or DOCX)", type=["pdf", "docx"])
+
+with col2:
+    # Job selection
+    job_title = st.selectbox("Select Job Position", list(job_descriptions.keys()))
+    
+    # Show job description
+    if job_title:
+        st.info(f"**Required Skills:**\n" + 
+                "\n".join([f"- {skill.title()}" for skill in job_descriptions[job_title]["skills"]]))
+
+if uploaded_file and job_title:
+    try:
+        # Show spinner while processing
+        with st.spinner("Analyzing resume..."):
+            # Extract text based on file type
+            if uploaded_file.type == "application/pdf":
+                text = extract_text_from_pdf(uploaded_file)
+            else:
+                text = extract_text_from_docx(uploaded_file)
+            
+            # Analyze resume
+            found_skills, summary = analyze_resume(text, job_title)
+        
+        # Display results in tabs
+        tab1, tab2, tab3 = st.tabs(["📊 Skills Match", "📝 Resume Summary", "🎯 Skills Gap"])
+        
+        with tab1:
+            # Display matched skills
+            st.subheader("🎯 Matched Skills")
+            if found_skills:
+                for skill in found_skills:
+                    st.success(f"✅ {skill.title()}")
+                
+                # Calculate match percentage
+                match_percentage = len(found_skills) / len(job_descriptions[job_title]["skills"]) * 100
+                st.metric("Skills Match", f"{match_percentage:.1f}%")
+            else:
+                st.warning("No direct skill matches found.")
+        
+        with tab2:
+            # Display resume summary
+            st.subheader("📝 Resume Summary")
+            st.write(summary)
+        
+        with tab3:
+            # Display missing skills
+            st.subheader("📌 Skills to Develop")
+            missing_skills = [skill for skill in job_descriptions[job_title]["skills"] 
+                            if skill not in found_skills]
+            if missing_skills:
+                for skill in missing_skills:
+                    st.warning(f"➖ {skill.title()}")
+            else:
+                st.success("Great! The candidate has all the required skills!")
+        
+    except Exception as e:
+        st.error(f"An error occurred while processing the resume: {str(e)}")
+
+# Add footer
+st.markdown("---")
+st.markdown("Made with ❤️ using Streamlit and Hugging Face")