Update app.py

app.py

@@ -5,16 +5,21 @@ from docx import Document
 from sentence_transformers import SentenceTransformer, util
 import re
 import plotly.graph_objects as go
+import sys
+import os
+import torch
 
 # Initialize Models once at startup
 print("🚀 Initializing SETHU AI Engine...")
 try:
     nlp = spacy.load("en_core_web_sm")
 except:
-    import os
-    os.system("python -m spacy download en_core_web_sm")
+    print("📥 Downloading spaCy model...")
+    os.system(f"{sys.executable} -m spacy download en_core_web_sm")
     nlp = spacy.load("en_core_web_sm")
 
+# Use a high-quality Hugging Face model for embeddings
+# 'all-MiniLM-L6-v2' is fast and efficient for local use
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
 TECH_SKILLS = [
@@ -45,7 +50,6 @@ def extract_text(file_obj):
     if file_obj is None:
         return ""
     
-    # Gradio might pass a file-like object or a string path
     file_path = file_obj.name if hasattr(file_obj, 'name') else str(file_obj)
     
     try:
@@ -55,6 +59,10 @@ def extract_text(file_obj):
         elif file_path.lower().endswith('.docx'):
             doc = Document(file_path)
             return "\n".join([p.text for p in doc.paragraphs])
+        else:
+            # Try reading as plain text
+            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 ""
@@ -68,89 +76,203 @@ def discover_skills(text):
             found.add(skill)
     return found
 
-def create_gauge(score):
-    fig = go.Figure(go.Indicator(
-        mode = "gauge+number",
-        value = score,
-        domain = {'x': [0, 1], 'y': [0, 1]},
-        gauge = {
-            'axis': {'range': [0, 100]},
-            'bar': {'color': "#003366"},
-            'steps': [
-                {'range': [0, 50], 'color': "#ffcccc"},
-                {'range': [50, 80], 'color': "#fff3cd"},
-                {'range': [80, 100], 'color': "#d4edda"}
-            ],
-        }
+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"))
+    # In a real dashboard we'd have 4 separate plots, but for simplicity we'll show the main one
+    # and use different layout for others or just focus on the primary one.
+    
+    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(height=250, margin=dict(l=30, r=30, t=30, b=30), paper_bgcolor="rgba(0,0,0,0)")
+
+    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 main_process(resume_file, jd_text):
+def estimate_salary(score, skills):
+    base = 70
+    multiplier = 1 + (score / 100)
+    skill_bonus = len(skills) * 2
+    low = round(base * multiplier + skill_bonus)
+    high = round(low * 1.5)
+    return f"${low}k - ${high}k"
+
+def main_process(resume_file, jd_text, progress=gr.Progress()):
     print("--- New Analysis Request ---")
     if not resume_file or not jd_text.strip():
-        return "⚠️ Error: Please upload a resume and paste the JD.", "", None, [], gr.update(visible=False)
+        return [
+            "⚠️ Please upload resume/JD.", "", None, None, 
+            "Waiting for analysis...", "N/A", [], gr.update(visible=False)
+        ]
 
-    # 1. Extraction
+    progress(0.1, desc="Extracting Resume Content...")
     resume_text = extract_text(resume_file)
     if not resume_text.strip():
-        return "⚠️ Error: Failed to extract text from resume. Ensure it's not and image-only PDF.", "", None, [], gr.update(visible=False)
+        return [
+            "❌ Extraction failed.", "", None, None, 
+            "Failed to read resume.", "N/A", [], gr.update(visible=False)
+        ]
 
-    # 2. Skill Matching
+    progress(0.3, desc="Analyzing Skills...")
     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))
 
-    # 3. AI scoring
+    progress(0.6, desc="Calculating Match Intelligence...")
     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)
 
-    # 4. Results Formatting
-    present_str = ", ".join([s.upper() for s in match_skills]) if match_skills else "No direct skill matches found."
-    gap_str = ", ".join([s.upper() for s in gap_skills]) if gap_skills else "No major skill gaps detected!"
-    plot = create_gauge(score)
+    # Heuristic metrics for the dashboard
+    content_score = min(100, len(resume_text.split()) / 5) 
+    search_score = min(100, len(r_skills) * 10)
+    ats_score = round(score * 0.9, 1)
+    
+    progress(0.8, desc="Generating Visualizations...")
+    gauge_plot = create_score_gauges(score, content_score, search_score, ats_score)
+    radar_plot = create_radar_chart(len(match_skills)*10, 75, 80, score, search_score)
+    
+    salary_range = estimate_salary(score, match_skills)
+    
+    ai_analysis = f"Based on our AI alignment, your profile shows {score}% match for this role. "
+    if score > 80:
+        ai_analysis += "Excellent match! You are a top-tier candidate."
+    elif score > 50:
+        ai_analysis += "Strong foundation, but some gaps in technical stacks were detected."
+    else:
+        ai_analysis += "Needs improvement. Focus on the learning roadmap to bridge gaps."
+
+    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 gaps detected!"
 
     print(f"Analysis Complete. Score: {score}")
-    return present_str, gap_str, plot, gap_skills, gr.update(visible=True)
+    return [
+        present_str, gap_str, gauge_plot, radar_plot, 
+        ai_analysis, salary_range, gap_skills, gr.update(visible=True)
+    ]
 
 def generate_roadmap(gap_skills):
     if not gap_skills:
-        return "### 🌟 Career Ready!\nYour profile is an excellent match for this role. Focus on practicing behavioral interview questions."
+        return "### 🌟 Career Ready!\nYour profile is an excellent match. Focus on practice."
     
     roadmap = "### 🛤️ Personalized Readiness Roadmap\n\n"
     for s in gap_skills:
-        res = ROADMAP_DB.get(s.lower(), f"Learn **{s.upper()}** through hands-on projects and documentation.")
+        res = ROADMAP_DB.get(s.lower(), f"Learn **{s.upper()}** through hands-on projects.")
         roadmap += f"- **{s.upper()}**: {res}\n"
     return roadmap
 
-# UI Layout
-with gr.Blocks(theme=gr.themes.Soft(), title="SETHU AI") as demo:
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Image("logo.png", show_label=False, height=120, container=False)
-        with gr.Column(scale=4):
-            gr.Markdown("# SETHU AI - Career Intelligence Hub")
-            gr.Markdown("### From Resume to Career Readiness | Powered by SASTRA DEEMED UNIVERSITY")
+# Premium CSS for Glassmorphism
+STYLE = """
+.gradio-container {
+    background-color: #0d1117 !important;
+    color: white !important;
+}
+.glass-panel {
+    background: rgba(255, 255, 255, 0.05) !important;
+    border-radius: 15px !important;
+    border: 1px solid rgba(255, 255, 255, 0.1) !important;
+    padding: 20px !important;
+    backdrop-filter: blur(10px);
+}
+.stat-card {
+    text-align: center;
+    padding: 15px;
+    background: rgba(0, 223, 216, 0.1);
+    border-radius: 10px;
+    border: 1px solid #00dfd8;
+}
+"""
 
-    gr.Markdown("---")
+with gr.Blocks(theme=gr.themes.Soft(), css=STYLE, title="Career Compass AI") as demo:
+    gr.HTML("""
+        <div style="text-align: center; padding: 20px;">
+            <h1 style="color: #00dfd8; margin-bottom: 0;">🧭 Career Compass</h1>
+            <p style="color: #8b949e;">AI-Powered Resume Analysis & Career Readiness Hub</p>
+        </div>
+    """)
 
     with gr.Row():
-        with gr.Column(scale=1):
+        with gr.Column(scale=1, variant="panel"):
             gr.Markdown("### 📥 1. Upload Requirements")
-            resume_input = gr.File(label="Upload Resume (PDF/DOCX)")
-            jd_input = gr.Textbox(label="Job Description", lines=12, placeholder="Paste the job requirements here...")
-            run_btn = gr.Button("🔍 Run AI Analysis", variant="primary")
+            resume_input = gr.File(label="Resume (PDF/DOCX)")
+            jd_input = gr.Textbox(label="Job Description", lines=10, placeholder="Paste JD here...")
+            run_btn = gr.Button("🔍 Run AI Analysis", variant="primary", scale=1)
         
-        with gr.Column(scale=1):
-            gr.Markdown("### 📊 2. Match Intelligence")
-            gauge_plot = gr.Plot()
+        with gr.Column(scale=2):
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### 📊 Analysis Results")
+                    gauge_plot = gr.Plot(label="Match Score")
+                with gr.Column():
+                    gr.Markdown("### 🕸️ Competency Profile")
+                    radar_plot = gr.Plot()
+            
+            with gr.Row():
+                with gr.Column(elem_classes=["glass-panel"]):
+                    gr.Markdown("### 💡 Strategic AI Analysis")
+                    analysis_text = gr.Markdown("Waiting for analysis...")
+                with gr.Column(elem_classes=["glass-panel"]):
+                    gr.Markdown("### 💰 Salary Insight")
+                    salary_display = gr.Text(label="Estimated Range", interactive=False)
+
+    with gr.Row():
+        with gr.Column():
             match_display = gr.Textbox(label="Identified Matching Skills", interactive=False)
+        with gr.Column():
             gap_display = gr.Textbox(label="Identified Skill Gaps", interactive=False)
-    
+
     with gr.Row(visible=False) as roadmap_container:
-        with gr.Column():
+        with gr.Column(elem_classes=["glass-panel"]):
             gr.Markdown("---")
             roadmap_btn = gr.Button("🚀 Generate Knowledge Upgrade Roadmap", variant="secondary")
             roadmap_output = gr.Markdown()
@@ -162,7 +284,10 @@ with gr.Blocks(theme=gr.themes.Soft(), title="SETHU AI") as demo:
     run_btn.click(
         fn=main_process,
         inputs=[resume_input, jd_input],
-        outputs=[match_display, gap_display, gauge_plot, gap_state, roadmap_container]
+        outputs=[
+            match_display, gap_display, gauge_plot, radar_plot, 
+            analysis_text, salary_display, gap_state, roadmap_container
+        ]
     )
 
     roadmap_btn.click(
@@ -173,6 +298,3 @@ with gr.Blocks(theme=gr.themes.Soft(), title="SETHU AI") as demo:
 
 if __name__ == "__main__":
     demo.launch()
-
-if __name__ == "__main__":
-    demo.launch()