Update app.py

app.py

@@ -9,6 +9,8 @@ import sys
 import os
 import torch
 
+from transformers import pipeline
+
 # Initialize Models once at startup
 print("🚀 Initializing SETHU AI Engine...")
 try:
@@ -19,9 +21,16 @@ except:
     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')
 
+# Initialize LLM for Reasoning (using a lightweight model for local speed)
+print("🤖 Loading Reasoning LLM (FLAN-T5)...")
+try:
+    llm_reasoner = pipeline("text2text-generation", model="google/flan-t5-small", device_map="auto")
+except Exception as e:
+    print(f"⚠️ LLM Load failed: {e}. Falling back to heuristic reasoning.")
+    llm_reasoner = None
+
 TECH_SKILLS = [
     "python", "javascript", "react", "fastapi", "aws", "docker", "kubernetes", "sql", 
     "git", "ml", "nlp", "tensorflow", "pytorch", "java", "golang", "postgresql", 
@@ -99,8 +108,6 @@ def create_score_gauges(match_score, content_score, search_score, ats_score):
 
     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)',
@@ -147,7 +154,7 @@ def estimate_salary(score, skills):
     return f"${low}k - ${high}k"
 
 def main_process(resume_file, jd_text, progress=gr.Progress()):
-    print("--- New Analysis Request ---")
+    print("--- New Analysis Request (LLM Enabled) ---")
     if not resume_file or not jd_text.strip():
         return [
             "⚠️ Please upload resume/JD.", "", None, None, 
@@ -168,30 +175,31 @@ def main_process(resume_file, jd_text, progress=gr.Progress()):
     match_skills = sorted(list(r_skills.intersection(j_skills)))
     gap_skills = sorted(list(j_skills - r_skills))
 
-    progress(0.6, desc="Calculating Match Intelligence...")
+    progress(0.5, 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)
 
-    # Heuristic metrics for the dashboard
+    progress(0.7, desc="Generating AI Analysis with LLM...")
+    if llm_reasoner:
+        prompt = f"Analyze resume match for Job Description. Match Score: {score}%. Gaps: {', '.join(gap_skills)}. Summarize fit in 2 sentences."
+        ai_response = llm_reasoner(prompt, max_length=100)[0]['generated_text']
+        ai_analysis = f"**AI Verdict**: {ai_response}"
+    else:
+        ai_analysis = f"Based on our AI alignment, your profile shows {score}% match. "
+        ai_analysis += "Strong foundation!" if score > 50 else "Focus on the gaps."
+
+    # Heuristic metrics
     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...")
+    progress(0.9, desc="Finalizing Dashboard...")
     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!"
 
@@ -206,13 +214,13 @@ def generate_roadmap(gap_skills):
         return """
         <div style='text-align: center; padding: 40px; background: rgba(0, 223, 216, 0.1); border-radius: 15px;'>
             <h2 style='color: #00dfd8;'>🌟 Profile is 100% Ready!</h2>
-            <p>Your skills perfectly align with this role. Focus on interview storytelling and behavioral prep.</p>
+            <p>Your skills perfectly align with this role. Focus on interview storytelling.</p>
         </div>
         """
     
     cards_html = "<div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(280px, 1fr)); gap: 20px; margin-top: 20px;'>"
     for s in gap_skills:
-        res = ROADMAP_DB.get(s.lower(), f"Master {s.upper()} via hands-on projects & documentation.")
+        res = ROADMAP_DB.get(s.lower(), f"Master {s.upper()} via hands-on projects.")
         cards_html += f"""
         <div style='background: rgba(255, 255, 255, 0.05); border: 1px solid rgba(0, 223, 216, 0.3); border-radius: 12px; padding: 20px; border-left: 5px solid #00dfd8;'>
             <div style='display: flex; justify-content: space-between; align-items: start;'>
@@ -233,6 +241,19 @@ def generate_roadmap(gap_skills):
     cards_html += "</div>"
     return f"### 🚀 Learning Pathfinder\n{cards_html}"
 
+def generate_interview_questions(gaps):
+    if not llm_reasoner:
+        return "1. Can you walk us through your most successful project?\n2. How do you stay updated with tech trends?"
+    
+    print("🤖 Generating questions with LLM...")
+    context = ", ".join(gaps[:3]) if gaps else "general tech"
+    prompt = f"Generate 3 technical interview questions for a candidate missing: {context}. Be specific."
+    try:
+        q_gen = llm_reasoner(prompt, max_length=150)[0]['generated_text']
+        return f"### 🎤 AI-Generated Questions\n{q_gen}\n\n*Tip: Leverage your adjacent skills to bridge these gaps.*"
+    except:
+        return "1. Describe a complex technical problem you solved.\n2. How do you handle deadlines?"
+
 # Premium CSS for Glassmorphism
 STYLE = """
 .gradio-container {