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streamlit_app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import re
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+import string
+import json
+import os
+from utils import load_job_data, preprocess_text, get_top_skills, get_job_recommendations
+
+# Download NLTK resources
+nltk.download('punkt')
+nltk.download('stopwords')
+
+# Page configuration
+st.set_page_config(
+    page_title="Job Title Recommender",
+    page_icon="💼",
+    layout="wide"
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        font-weight: bold;
+        color: #0A66C2;
+        text-align: center;
+        margin-bottom: 1rem;
+    }
+    .sub-header {
+        font-size: 1.2rem;
+        color: #666;
+        text-align: center;
+        margin-bottom: 2rem;
+    }
+    .footer {
+        text-align: center;
+        margin-top: 2rem;
+        color: #666;
+        font-size: 0.9rem;
+    }
+    .recommendation-card {
+        border: 1px solid #ddd;
+        border-radius: 8px;
+        padding: 1rem;
+        margin-bottom: 1rem;
+        background-color: #f9f9f9;
+    }
+    .skill-tag {
+        display: inline-block;
+        background-color: #0A66C2;
+        color: white;
+        padding: 0.3rem 0.8rem;
+        border-radius: 15px;
+        margin: 0.2rem;
+        font-size: 0.9rem;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Initialize session state
+if 'job_data' not in st.session_state:
+    st.session_state.job_data = load_job_data()
+if 'vectorizer' not in st.session_state:
+    st.session_state.vectorizer = None
+if 'job_vectors' not in st.session_state:
+    st.session_state.job_vectors = None
+
+# Header
+st.markdown('<div class="main-header">💼 Job Title Recommender</div>', unsafe_allow_html=True)
+st.markdown('<div class="sub-header">Find the perfect job titles based on your LinkedIn profile or resume</div>', unsafe_allow_html=True)
+
+# Built with anycoder
+st.markdown('<div style="text-align: center; margin-bottom: 1rem;">Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank">anycoder</a></div>', unsafe_allow_html=True)
+
+# Main content
+col1, col2 = st.columns([1, 1])
+
+with col1:
+    st.header("📝 Input Your Profile")
+
+    # Input method selection
+    input_method = st.radio(
+        "Choose input method:",
+        ["Text Input", "File Upload"],
+        horizontal=True
+    )
+
+    if input_method == "Text Input":
+        profile_text = st.text_area(
+            "Paste your LinkedIn profile or resume text here:",
+            height=300,
+            placeholder="Include your work experience, skills, education, and any other relevant information..."
+        )
+    else:
+        uploaded_file = st.file_uploader(
+            "Upload your resume (PDF or TXT):",
+            type=["pdf", "txt"],
+            help="Upload your resume file to analyze"
+        )
+
+        if uploaded_file:
+            try:
+                if uploaded_file.type == "application/pdf":
+                    # For PDF files (would need additional libraries)
+                    st.warning("PDF parsing requires additional libraries. Please use text input or upload a .txt file.")
+                    profile_text = ""
+                else:
+                    # For text files
+                    profile_text = uploaded_file.read().decode("utf-8")
+                    st.success("File uploaded successfully!")
+            except Exception as e:
+                st.error(f"Error reading file: {e}")
+                profile_text = ""
+
+    # Skills input
+    st.subheader("Key Skills")
+    skills_input = st.text_input(
+        "Enter your key skills (comma separated):",
+        placeholder="e.g., Python, Machine Learning, Project Management"
+    )
+
+    # Experience level
+    experience_level = st.select_slider(
+        "Years of Experience:",
+        options=["0-2 years", "3-5 years", "6-10 years", "10+ years"],
+        value="3-5 years"
+    )
+
+    # Education level
+    education_level = st.selectbox(
+        "Highest Education Level:",
+        ["High School", "Associate Degree", "Bachelor's Degree", "Master's Degree", "PhD"]
+    )
+
+    # Analyze button
+    analyze_button = st.button("🔍 Analyze & Get Recommendations", type="primary")
+
+with col2:
+    st.header("🎯 Your Recommendations")
+
+    if analyze_button and profile_text:
+        with st.spinner("Analyzing your profile..."):
+            try:
+                # Preprocess the input text
+                processed_text = preprocess_text(profile_text)
+
+                # Extract skills from input
+                input_skills = [skill.strip() for skill in skills_input.split(",")] if skills_input else []
+                extracted_skills = get_top_skills(processed_text, top_n=10)
+
+                # Combine all skills
+                all_skills = list(set(input_skills + extracted_skills))
+
+                # Create user profile vector
+                if st.session_state.vectorizer is None or st.session_state.job_vectors is None:
+                    # Initialize vectorizer and job vectors if not already done
+                    job_descriptions = st.session_state.job_data['description'].tolist()
+                    st.session_state.vectorizer = TfidfVectorizer(stop_words='english', max_features=5000)
+                    st.session_state.job_vectors = st.session_state.vectorizer.fit_transform(job_descriptions)
+
+                # Transform user profile
+                user_vector = st.session_state.vectorizer.transform([processed_text])
+
+                # Get recommendations
+                recommendations = get_job_recommendations(
+                    user_vector,
+                    st.session_state.job_vectors,
+                    st.session_state.job_data,
+                    all_skills,
+                    experience_level,
+                    education_level,
+                    top_n=5
+                )
+
+                if recommendations.empty:
+                    st.warning("No suitable job titles found. Try adding more details to your profile.")
+                else:
+                    st.success(f"Found {len(recommendations)} suitable job titles for you!")
+
+                    for idx, row in recommendations.iterrows():
+                        with st.expander(f"🏆 {row['job_title']} (Match: {row['match_score']:.1%})"):
+                            st.markdown(f"**Industry:** {row['industry']}")
+                            st.markdown(f"**Experience Level:** {row['experience_level']}")
+                            st.markdown(f"**Education Requirement:** {row['education_requirement']}")
+
+                            # Display skills
+                            st.markdown("**Key Skills:**")
+                            job_skills = [skill.strip() for skill in row['required_skills'].split(",")]
+                            for skill in job_skills[:5]:  # Show top 5 skills
+                                st.markdown(f'<span class="skill-tag">{skill}</span>', unsafe_allow_html=True)
+
+                            st.markdown(f"**Average Salary:** {row['avg_salary']}")
+
+                            if st.button(f"Learn more about {row['job_title']}", key=f"learn_{idx}"):
+                                st.info(f"This would typically link to more information about the {row['job_title']} role.")
+
+                    # Show skills analysis
+                    st.subheader("Your Skills Analysis")
+                    if all_skills:
+                        st.markdown("**Detected Skills:**")
+                        for skill in all_skills[:10]:  # Show top 10 skills
+                            st.markdown(f'<span class="skill-tag">{skill}</span>', unsafe_allow_html=True)
+                    else:
+                        st.info("No specific skills detected. Consider adding more details to your profile.")
+
+            except Exception as e:
+                st.error(f"An error occurred during analysis: {e}")
+    elif analyze_button and not profile_text:
+        st.warning("Please enter your profile information or upload a file.")
+    else:
+        st.info("Enter your LinkedIn profile or resume information and click 'Analyze' to get job recommendations.")
+
+# Footer
+st.markdown("""
+<div class="footer">
+    <p>This tool uses natural language processing to match your skills and experience with suitable job titles.</p>
+    <p>For best results, provide detailed information about your work experience, skills, and education.</p>
+</div>
+""", unsafe_allow_html=True)