Added hallucination detection to AI Recruiting Agent

app.py

@@ -120,6 +120,38 @@ def store_resumes(resume_files: List[tempfile._TemporaryFileWrapper]) -> str:
     resume_store.add_documents(all_docs)
     return f"Successfully stored {len(resume_files)} resumes"
 
+def verify_analysis(analysis_text: str, source_documents: List[str]) -> Dict:
+    """Verify analysis against source documents"""
+    verification_prompt = PromptTemplate(
+        input_variables=["analysis", "source_docs"],
+        template="""
+        Compare this analysis against source documents. Identify unsupported claims.
+        
+        Analysis: {analysis}
+        Source Documents: {source_docs}
+        
+        Provide:
+        UNSUPPORTED CLAIMS: [list or "None"]
+        FACTUALITY SCORE: [0-100]%
+        UNCERTAINTY: [areas or "None"]
+        """
+    )
+    
+    chain = LLMChain(llm=llm, prompt=verification_prompt)
+    result = chain.run({
+        "analysis": analysis_text,
+        "source_docs": "\n---\n".join(source_documents)
+    })
+    
+    # Parse score
+    try:
+        score_line = [line for line in result.split('\n') if 'FACTUALITY SCORE:' in line][0]
+        score = int(score_line.split(':')[1].strip().replace('%', '')) / 100
+    except:
+        score = 0.5
+    
+    return {"factuality_score": score, "verification_result": result}
+
 def analyze_candidates(job_description: str) -> str:
     # First extract required skills from job description
     skills_prompt = PromptTemplate(
@@ -225,6 +257,9 @@ def analyze_candidates(job_description: str) -> str:
                 "cultural_requirements": cultural_requirements
             })
             
+            # Verify culture analysis
+            culture_verification = verify_analysis(culture_fit, [resume_text, cultural_requirements])
+            
             # Now analyze technical skills match
             skills_analysis_prompt = PromptTemplate(
                 input_variables=["resume", "required_skills", "job_description"],
@@ -261,6 +296,9 @@ def analyze_candidates(job_description: str) -> str:
                 "job_description": job_description
             })
 
+            # Verify skills analysis
+            skills_verification = verify_analysis(skills_fit, [resume_text, skills, job_description])
+
             # Create final recommendation
             final_recommendation_prompt = PromptTemplate(
                 input_variables=["skills_analysis", "culture_analysis", "job_description"],
@@ -300,6 +338,11 @@ def analyze_candidates(job_description: str) -> str:
                 "job_description": job_description
             })
 
+            # Add verification warnings if factuality score < 0.8
+            verification_notes = ""
+            if culture_verification["factuality_score"] < 0.8 or skills_verification["factuality_score"] < 0.8:
+                verification_notes = f"\n\n⚠️ VERIFICATION: Some claims may be unsupported. Culture: {culture_verification['factuality_score']:.0%}, Skills: {skills_verification['factuality_score']:.0%}"
+
             # Append the analysis for this candidate to the consolidated analyses
             consolidated_analyses.append(f"""
             === Candidate Analysis (Resume ID: {resume_id}) ===
@@ -311,7 +354,7 @@ def analyze_candidates(job_description: str) -> str:
             {skills_fit}
 
             HIRING RECOMMENDATION:
-            {final_recommendation}
+            {final_recommendation}{verification_notes}
             
             ----------------------------------------
             """)
@@ -329,8 +372,6 @@ def analyze_candidates(job_description: str) -> str:
     return "\n".join(consolidated_analyses)
 
 
-
-
 def clear_databases():
     """Clear both resume and culture document databases"""
     global resume_store, culture_store