Update app.py

app.py

@@ -12,35 +12,42 @@ import torch
 from transformers import pipeline
 
 # Initialize Models once at startup
-print("🚀 Initializing SETHU AI Intelligence Engine...")
+print("🚀 [1/4] Initializing Semantic Matcher (all-MiniLM-L6-v2)...")
 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")
-    nlp = spacy.load("en_core_web_sm")
-
-# 1. Semantic Engine (Embeddings)
-print("🧠 Loading Semantic Matcher (all-MiniLM-L6-v2)...")
-model = SentenceTransformer('all-MiniLM-L6-v2')
+    model = SentenceTransformer('all-MiniLM-L6-v2', device='cpu')
+except Exception as e:
+    print(f"❌ Semantic Matcher failed: {e}")
+    model = None
 
-# 2. Strategic Reasoner (Analysis)
-print("🤖 Loading Strategic Reasoner (FLAN-T5-Base)...")
+print("🚀 [2/4] Initializing Reasoning LLM (FLAN-T5-Base)...")
 try:
-    # Upgraded from 'small' to 'base' for better logic
-    llm_reasoner = pipeline("text2text-generation", model="google/flan-t5-base", device_map="auto")
+    # Use CPU by default for stability on Windows unless explicitly requested
+    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
 
-# 3. Interview Coach (Conversational)
-print("🎤 Loading Interview Coach (LaMini-GPT-124M)...")
+print("🚀 [3/4] Initializing Interview Coach (LaMini-GPT)...")
 try:
-    llm_coach = pipeline("text-generation", model="MBZUAI/LaMini-GPT-124M", device_map="auto")
+    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", 
@@ -194,8 +201,12 @@ def main_process(resume_file, jd_text, progress=gr.Progress()):
         gap_skills = sorted(list(j_skills - r_skills))
         print(f"   - Identified {len(match_skills)} matches and {len(gap_skills)} gaps.")
 
-        progress(0.4, desc="📐 Printing Semantic Distance...")
+        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)
@@ -203,28 +214,37 @@ def main_process(resume_file, jd_text, progress=gr.Progress()):
 
         progress(0.7, desc="🤖 Syncing Neural Consensus...")
         print("Stage 4: LLM Ensemble Reasoning...")
-        ensemble_insight = ""
+        ensemble_insight = []
+        
         # Model 1: Reasoner (FLAN-T5)
         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 += f"**Reasoner**: {t5_out}\n\n"
+                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.")
 
         # Model 2: Coach (LaMini)
         if llm_coach:
             try:
                 print("   - Querying Coach (LaMini)...")
-                prompt_lamini = f"As a career coach, give advice for this profile matching a role at {score}% match."
-                lamini_out = llm_coach(prompt_lamini, max_length=100)[0]['generated_text']
-                ensemble_insight += f"**Coach**: {lamini_out}"
+                prompt_lamini = f"Career coach advice for {score}% match."
+                lamini_out = llm_coach(prompt_lamini, max_length=100, truncation=True)[0]['generated_text']
+                # Clean up if output contains prompt
+                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 = ensemble_insight if ensemble_insight else f"Neural consensus reached alignment at {score}%."
+        ai_analysis = "\n\n".join(ensemble_insight) if ensemble_insight else f"Neural consensus reached alignment at {score}%."
 
         # Heuristic metrics
         print("Stage 5: Calculating Dashboard Metrics...")