| | import gradio as gr |
| | import spacy |
| | import pdfplumber |
| | from docx import Document |
| | from sentence_transformers import SentenceTransformer, util |
| | import re |
| | import plotly.graph_objects as go |
| | import sys |
| | import os |
| | import torch |
| |
|
| | from transformers import pipeline |
| |
|
| | |
| | print("🚀 [1/4] Initializing Semantic Matcher (all-MiniLM-L6-v2)...") |
| | try: |
| | model = SentenceTransformer('all-MiniLM-L6-v2', device='cpu') |
| | except Exception as e: |
| | print(f"❌ Semantic Matcher failed: {e}") |
| | model = None |
| |
|
| | print("🚀 [2/4] Initializing Reasoning LLM (FLAN-T5-Base)...") |
| | try: |
| | |
| | llm_reasoner = pipeline("text2text-generation", model="google/flan-t5-base", device=-1) |
| | except Exception as e: |
| | print(f"⚠️ Reasoning LLM failed: {e}") |
| | llm_reasoner = None |
| |
|
| | print("🚀 [3/4] Initializing Interview Coach (LaMini-GPT)...") |
| | try: |
| | llm_coach = pipeline("text-generation", model="MBZUAI/LaMini-GPT-124M", device=-1) |
| | except Exception as e: |
| | print(f"⚠️ Coach LLM failed: {e}") |
| | llm_coach = None |
| |
|
| | print("🚀 [4/4] Loading NLP Entities (spaCy)...") |
| | try: |
| | nlp = spacy.load("en_core_web_sm") |
| | except: |
| | print("📥 Downloading spaCy model...") |
| | os.system(f"{sys.executable} -m spacy download en_core_web_sm") |
| | try: |
| | nlp = spacy.load("en_core_web_sm") |
| | except: |
| | nlp = None |
| | print("❌ NLP Load failed completely.") |
| |
|
| | print("✨ Intelligence Engine Ready.") |
| |
|
| | TECH_SKILLS = [ |
| | "python", "javascript", "react", "fastapi", "aws", "docker", "kubernetes", "sql", |
| | "git", "ml", "nlp", "tensorflow", "pytorch", "java", "golang", "postgresql", |
| | "mongodb", "redis", "devops", "rest api", "graphql", "scikit-learn", "pandas", |
| | "numpy", "django", "flask", "typescript", "angular", "vue", "html", "css", |
| | "node.js", "express", "azure", "gcp", "linux", "terraform", "ansible", "jenkins", |
| | "prompt engineering", "openai", "llm", "bert", "tableau", "powerbi", "excel", |
| | "c#", "c++", "rust", "r", "spark", "hadoop", "kafka", "mysql", "oracle", "snowflake" |
| | ] |
| |
|
| | ROADMAP_DB = { |
| | "python": "🐍 [Master Python](https://realpython.com/) - Focus on Backend Automation & Data Science.", |
| | "react": "⚛️ [React.dev](https://react.dev/) - Master Hooks & State Management.", |
| | "aws": "☁️ [AWS Builder](https://explore.skillbuilder.aws/) - Get Certified (Solutions Architect).", |
| | "docker": "🐳 [Docker Guide](https://docs.docker.com/) - Learn Container Architecture.", |
| | "kubernetes": "☸️ [K8s Certification](https://kubernetes.io/docs/tutorials/) - Master Orchestration.", |
| | "ml": "🤖 [ML Specialization](https://www.coursera.org/specializations/machine-learning-introduction) - Focus on Scikit-Learn.", |
| | "nlp": "✍️ [HF NLP Course](https://huggingface.co/learn/nlp-course/) - Master Transformers.", |
| | "sql": "💾 [SQL Practice](https://sqlzoo.net/) - Master Joins & Query Optmization.", |
| | "javascript": "📜 [JS.info](https://javascript.info/) - Master ES6+ Features.", |
| | "devops": "⚙️ [Roadmap.sh/devops](https://roadmap.sh/devops/) - Learn CI/CD & Infrastructure as Code." |
| | } |
| |
|
| | def extract_text(file_obj): |
| | """Robust text extraction for PDF and DOCX.""" |
| | if file_obj is None: |
| | return "" |
| | |
| | file_path = file_obj.name if hasattr(file_obj, 'name') else str(file_obj) |
| | |
| | try: |
| | if file_path.lower().endswith('.pdf'): |
| | with pdfplumber.open(file_path) as pdf: |
| | return "".join([page.extract_text() or "" for page in pdf.pages]) |
| | elif file_path.lower().endswith('.docx'): |
| | doc = Document(file_path) |
| | return "\n".join([p.text for p in doc.paragraphs]) |
| | else: |
| | |
| | with open(file_path, 'r', encoding='utf-8') as f: |
| | return f.read() |
| | except Exception as e: |
| | print(f"Extraction error on {file_path}: {e}") |
| | return "" |
| |
|
| | def discover_skills(text): |
| | if not text: return set() |
| | found = set() |
| | text_l = text.lower() |
| | for skill in TECH_SKILLS: |
| | if re.search(r'\b' + re.escape(skill) + r'\b', text_l): |
| | found.add(skill) |
| | return found |
| |
|
| | def create_score_gauges(match_score, content_score, search_score, ats_score): |
| | def make_gauge(val, title, color): |
| | return go.Indicator( |
| | mode="gauge+number", |
| | value=val, |
| | title={'text': title, 'font': {'size': 14, 'color': "white"}}, |
| | domain={'x': [0, 1], 'y': [0, 1]}, |
| | gauge={ |
| | 'axis': {'range': [0, 100], 'tickwidth': 1, 'tickcolor': "white"}, |
| | 'bar': {'color': color}, |
| | 'bgcolor': "rgba(0,0,0,0)", |
| | 'borderwidth': 2, |
| | 'bordercolor': "gray", |
| | 'steps': [ |
| | {'range': [0, 40], 'color': 'rgba(255, 0, 0, 0.1)'}, |
| | {'range': [40, 70], 'color': 'rgba(255, 255, 0, 0.1)'}, |
| | {'range': [70, 100], 'color': 'rgba(0, 255, 0, 0.1)'} |
| | ], |
| | } |
| | ) |
| |
|
| | fig = go.Figure() |
| | fig.add_trace(make_gauge(match_score, "Match Score", "#00dfd8")) |
| | |
| | fig.update_layout( |
| | paper_bgcolor='rgba(0,0,0,0)', |
| | plot_bgcolor='rgba(0,0,0,0)', |
| | font={'color': "white", 'family': "Arial"}, |
| | height=300, |
| | margin=dict(l=40, r=40, t=80, b=40) |
| | ) |
| | return fig |
| |
|
| | def create_radar_chart(skills, exp, edu, readiness, search): |
| | categories = ['Skills', 'Experience', 'Education', 'Readiness', 'Searchability'] |
| | fig = go.Figure() |
| |
|
| | fig.add_trace(go.Scatterpolar( |
| | r=[skills, exp, edu, readiness, search], |
| | theta=categories, |
| | fill='toself', |
| | name='Competency Profile', |
| | line_color='#00dfd8', |
| | fillcolor='rgba(0, 223, 216, 0.3)' |
| | )) |
| |
|
| | fig.update_layout( |
| | polar=dict( |
| | radialaxis=dict(visible=True, range=[0, 100], color="white", gridcolor="gray"), |
| | angularaxis=dict(color="white", gridcolor="gray"), |
| | bgcolor='rgba(0,0,0,0)' |
| | ), |
| | showlegend=False, |
| | paper_bgcolor='rgba(0,0,0,0)', |
| | plot_bgcolor='rgba(0,0,0,0)', |
| | height=350, |
| | margin=dict(l=60, r=60, t=20, b=20) |
| | ) |
| | return fig |
| |
|
| | def estimate_salary(score, skills): |
| | base = 75 |
| | multiplier = 1 + (score / 100) |
| | skill_bonus = len(skills) * 2.5 |
| | low = round(base * multiplier + skill_bonus) |
| | high = round(low * 1.45) |
| | return f"${low}k - ${high}k" |
| |
|
| | def main_process(resume_file, jd_text, progress=gr.Progress()): |
| | print("\n" + "="*40) |
| | print("🚀 INITIALIZING NEURAL INTELLIGENCE CYCLE") |
| | print("="*40) |
| | |
| | try: |
| | if not resume_file or not jd_text.strip(): |
| | print("⚠️ Incomplete inputs detected.") |
| | return [ |
| | "⚠️ Missing Inputs", "", None, None, |
| | "Incomplete data sequence. Please upload a resume and paste the JD.", "N/A", [], gr.update(visible=False) |
| | ] |
| |
|
| | progress(0.1, desc="⚡ Extracting Neural Content...") |
| | print("Stage 1: Text Extraction...") |
| | resume_text = extract_text(resume_file) |
| | if not resume_text.strip(): |
| | print("❌ Extraction failure: text is empty.") |
| | return [ |
| | "❌ Extract Error", "", None, None, |
| | "Neural bypass failed. Ensure the resume is a readable PDF or DOCX (not just an image).", "N/A", [], gr.update(visible=False) |
| | ] |
| |
|
| | progress(0.2, desc="🔍 Discovering Skill Entities...") |
| | print("Stage 2: Skill Discovery...") |
| | r_skills = discover_skills(resume_text) |
| | j_skills = discover_skills(jd_text) |
| | match_skills = sorted(list(r_skills.intersection(j_skills))) |
| | gap_skills = sorted(list(j_skills - r_skills)) |
| | print(f" - Identified {len(match_skills)} matches and {len(gap_skills)} gaps.") |
| |
|
| | progress(0.4, desc="📐 Computing Semantic Distance...") |
| | print("Stage 3: Embedding Computation...") |
| | if not model: |
| | print("❌ Error: Semantic model not loaded.") |
| | return ["Model Load Error", "N/A", None, None, "The semantic matching engine failed to initialize. Try restarting.", "N/A", [], gr.update(visible=False)] |
| | |
| | emb1 = model.encode(resume_text, convert_to_tensor=True) |
| | emb2 = model.encode(jd_text, convert_to_tensor=True) |
| | score = round(util.pytorch_cos_sim(emb1, emb2).item() * 100, 1) |
| | print(f" - Semantic Match Score: {score}%") |
| |
|
| | progress(0.7, desc="🤖 Syncing Neural Consensus...") |
| | print("Stage 4: LLM Ensemble Reasoning...") |
| | ensemble_insight = [] |
| | |
| | |
| | if llm_reasoner: |
| | try: |
| | print(" - Querying Reasoner (FLAN-T5)...") |
| | prompt_t5 = f"Analyze resume relevance. Score: {score}%. Gaps: {', '.join(gap_skills)}. Summarize fit." |
| | t5_out = llm_reasoner(prompt_t5, max_length=100)[0]['generated_text'] |
| | ensemble_insight.append(f"**Reasoner**: {t5_out}") |
| | except Exception as e: |
| | print(f" - T5 Inference Error: {e}") |
| | else: |
| | print(" - Reasoner LLM not available. Skipping.") |
| | ensemble_insight.append("**Reasoner**: Neural reasoning offline. Using heuristic fallback.") |
| |
|
| | |
| | if llm_coach: |
| | try: |
| | print(" - Querying Coach (LaMini)...") |
| | prompt_lamini = f"Career coach advice for {score}% match." |
| | lamini_out = llm_coach(prompt_lamini, max_length=100, truncation=True)[0]['generated_text'] |
| | |
| | clean_lamini = lamini_out.split("match.")[-1].strip() if "match." in lamini_out else lamini_out |
| | ensemble_insight.append(f"**Coach**: {clean_lamini}") |
| | except Exception as e: |
| | print(f" - LaMini Inference Error: {e}") |
| | else: |
| | print(" - Coach LLM not available. Skipping.") |
| | ensemble_insight.append("**Coach**: Coaching strategy offline.") |
| |
|
| | ai_analysis = "\n\n".join(ensemble_insight) if ensemble_insight else f"Neural consensus reached alignment at {score}%." |
| |
|
| | |
| | print("Stage 5: Calculating Dashboard Metrics...") |
| | content_score = min(100, len(resume_text.split()) / 4) |
| | search_score = min(100, len(r_skills) * 12) |
| | |
| | progress(0.9, desc="🎨 Rendering Compass Interface...") |
| | print("Stage 6: Finalizing UI Components...") |
| | gauge_plot = create_score_gauges(score, content_score, search_score, score*0.9) |
| | radar_plot = create_radar_chart(len(match_skills)*12, 80, 85, score, search_score) |
| | salary_range = estimate_salary(score, match_skills) |
| | |
| | present_str = ", ".join([s.upper() for s in match_skills]) if match_skills else "No direct matches." |
| | gap_str = ", ".join([s.upper() for s in gap_skills]) if gap_skills else "No critical gaps!" |
| |
|
| | print(f"✅ CYCLE COMPLETE | Score: {score}%") |
| | |
| | return [ |
| | present_str, gap_str, gauge_plot, radar_plot, |
| | ai_analysis, salary_range, gap_skills, gr.update(visible=True) |
| | ] |
| | except Exception as e: |
| | print(f"🔥 CRITICAL SYSTEM FAULT: {str(e)}") |
| | return [ |
| | "⚠️ System Error", "Analysis Failed", None, None, |
| | f"An unexpected error occurred: {str(e)}", "N/A", [], gr.update(visible=False) |
| | ] |
| |
|
| | def generate_roadmap(gap_skills): |
| | if not gap_skills: |
| | return """ |
| | <div style='text-align: center; padding: 40px; background: rgba(0, 223, 216, 0.05); border-radius: 15px; border: 1px dashed #00dfd8;'> |
| | <h2 style='color: #00dfd8; margin: 0;'>🌟 Neural Consensus: 100% Ready</h2> |
| | <p style='color: #8b949e;'>Both Reasoner & Coach models agree: Your profile is industry-ready.</p> |
| | </div> |
| | """ |
| | |
| | |
| | llm_tips = "" |
| | if llm_coach: |
| | try: |
| | prompt = f"Provide a one-sentence learning goal for each skill: {', '.join(gap_skills)}." |
| | llm_tips = llm_coach(prompt, max_length=150)[0]['generated_text'] |
| | except: |
| | llm_tips = "Focus on documentation and hands-on builds." |
| |
|
| | cards_html = f"<div style='margin-bottom: 20px; color: #8b949e; font-size: 11px; font-style: italic;'>AI Insight: {llm_tips}</div>" |
| | cards_html += "<div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(250px, 1fr)); gap: 20px;'>" |
| | for s in gap_skills: |
| | res = ROADMAP_DB.get(s.lower(), f"Master {s.upper()} via specialized projects.") |
| | cards_html += f""" |
| | <div style='background: rgba(255, 255, 255, 0.03); border: 1px solid rgba(0, 223, 216, 0.2); border-radius: 12px; padding: 15px;'> |
| | <div style='display: flex; justify-content: space-between;'> |
| | <span style='color: #00dfd8; font-size: 10px;'>NEURAL UPGRADE</span> |
| | <span style='color: #8b949e; font-size: 10px;'>LVL: EXPERT</span> |
| | </div> |
| | <h4 style='color: #fff; margin: 10px 0 5px 0;'>{s.upper()}</h4> |
| | <p style='color: #8b949e; font-size: 11px; margin-bottom: 12px;'>{res}</p> |
| | </div> |
| | """ |
| | cards_html += "</div>" |
| | return f"<div>{cards_html}</div>" |
| |
|
| | def generate_interview_questions(gaps): |
| | print("🤖 Neural Ensemble Coaching active...") |
| | context = ", ".join(gaps[:3]) if gaps else "strategic leadership" |
| | |
| | |
| | q1 = "1. Walk us through a scenario where you bridged technical gaps." |
| | if llm_reasoner: |
| | try: |
| | prompt_t5 = f"List 2 technical questions for {context}." |
| | q1 = llm_reasoner(prompt_t5, max_length=100)[0]['generated_text'] |
| | except: pass |
| |
|
| | |
| | tips = "Focus on your adaptability." |
| | if llm_coach: |
| | try: |
| | prompt_lamini = f"Give a career coaching tip for someone learning {context}." |
| | tips = llm_coach(prompt_lamini, max_length=100)[0]['generated_text'] |
| | except: pass |
| |
|
| | return f"### 🎤 Neural Ensemble Coaching\n**Technical Focus**: {q1}\n\n**Career Strategy**: {tips}\n\n*This insight was cross-verified by T5 and LaMini models.*" |
| |
|
| | |
| | STYLE = """ |
| | .gradio-container { background-color: #0b0e14 !important; color: white !important; font-family: 'Inter', sans-serif !important; } |
| | .glass-panel { background: rgba(255, 255, 255, 0.03) !important; border: 1px solid rgba(255, 255, 255, 0.08) !important; border-radius: 12px !important; padding: 18px !important; } |
| | .hub-tag { background: #1a1e26; border: 1px solid #30363d; border-radius: 6px; padding: 4px 10px; font-size: 11px; color: #8b949e; display: inline-block; margin-right: 5px; } |
| | .active-tag { background: rgba(0, 223, 216, 0.1); border-color: #00dfd8; color: #00dfd8; } |
| | """ |
| |
|
| | with gr.Blocks(theme=gr.themes.Soft(), css=STYLE, title="Career Compass AI") as demo: |
| | with gr.Row(): |
| | with gr.Column(scale=3): |
| | gr.HTML(""" |
| | <div style="padding: 10px;"> |
| | <h1 style="color: #00dfd8; margin: 0; font-size: 2.2em;">🧭 Career Compass</h1> |
| | <p style="color: #8b949e; margin-top: 5px;">Unified Neural Ensemble Dashboard</p> |
| | </div> |
| | """) |
| | with gr.Column(scale=2): |
| | gr.HTML(f""" |
| | <div style="text-align: right; padding-top: 20px;"> |
| | <span class="hub-tag active-tag">Ensemble Mode: ACTIVE</span> |
| | <span class="hub-tag">T5-Base</span> |
| | <span class="hub-tag">LaMini-GPT</span> |
| | </div> |
| | """) |
| |
|
| | with gr.Row(): |
| | with gr.Column(scale=1, variant="panel"): |
| | gr.Markdown("### 📥 Neural Feed") |
| | resume_input = gr.File(label="Target Resume") |
| | jd_input = gr.Textbox(label="Requirement Set", lines=8, placeholder="Paste JD sequence...") |
| | run_btn = gr.Button("⚡ Run Intelligent Cycle", variant="primary") |
| | |
| | with gr.Column(scale=2): |
| | with gr.Row(): |
| | with gr.Column(): |
| | gr.Markdown("### 📊 Alignment Core") |
| | gauge_plot = gr.Plot(label="Match Quality") |
| | with gr.Column(): |
| | gr.Markdown("### 🕸️ Competency Radar") |
| | radar_plot = gr.Plot(label="Skills Radar") |
| | |
| | with gr.Row(): |
| | with gr.Column(elem_classes=["glass-panel"]): |
| | gr.Markdown("### 💡 Ensemble Insight") |
| | analysis_text = gr.Markdown("Waiting for neural consensus...") |
| | with gr.Column(elem_classes=["glass-panel"]): |
| | gr.Markdown("### 💰 Market Value") |
| | salary_display = gr.Textbox(label="Projected Range", interactive=False) |
| |
|
| | with gr.Row(): |
| | with gr.Column(): |
| | match_display = gr.Textbox(label="Neural Matches", interactive=False) |
| | with gr.Column(): |
| | gap_display = gr.Textbox(label="Identified Faults", interactive=False) |
| |
|
| | with gr.Column(visible=False) as roadmap_container: |
| | gr.Markdown("---") |
| | with gr.Row(elem_classes=["glass-panel"]): |
| | with gr.Column(scale=2): |
| | gr.Markdown("### 🚀 Knowledge Upgrade Path") |
| | roadmap_btn = gr.Button("Initialize Pathfinder", variant="secondary") |
| | roadmap_output = gr.HTML() |
| | with gr.Column(scale=1): |
| | gr.Markdown("### 🎤 Interview Coaching") |
| | interview_prep = gr.Markdown("Pathfinder inactive.") |
| |
|
| | gap_state = gr.State(value=[]) |
| |
|
| | run_btn.click( |
| | fn=main_process, |
| | inputs=[resume_input, jd_input], |
| | outputs=[ |
| | match_display, gap_display, gauge_plot, radar_plot, |
| | analysis_text, salary_display, gap_state, roadmap_container |
| | ] |
| | ) |
| |
|
| | roadmap_btn.click( |
| | fn=lambda gaps: [generate_roadmap(gaps), generate_interview_questions(gaps)], |
| | inputs=[gap_state], |
| | outputs=[roadmap_output, interview_prep] |
| | ) |
| |
|
| | if __name__ == "__main__": |
| | demo.launch() |
| |
|
