Change to analyze candidates

app.py

@@ -98,23 +98,24 @@ def store_resumes(resume_files: List[tempfile._TemporaryFileWrapper]) -> str:
     return f"Successfully stored {len(all_docs)} resume chunks"
 
 def analyze_candidates(job_description: str) -> str:
-    """Analyze candidates against job description and culture fit"""
-    
-    # Extract skills from job description
-    skills_prompt = """
-    Extract the key technical skills and requirements from this job description:
-    
-    {job_description}
-    
-    Return the skills as a comma-separated list.
-    """
+    # Extract skills prompt template
+    skills_prompt = PromptTemplate(
+        input_variables=["job_description"],
+        template="""
+        Extract the key technical skills and requirements from this job description:
+        
+        {job_description}
+        
+        Return the skills as a comma-separated list.
+        """
+    )
     
     skills_chain = LLMChain(
         llm=llm,
         prompt=skills_prompt
     )
     
-    skills = skills_chain.run(job_description=job_description)
+    skills = skills_chain.run({"job_description": job_description})
     
     # Query vector store for matching resumes
     results = vector_store.similarity_search(
@@ -130,43 +131,47 @@ def analyze_candidates(job_description: str) -> str:
         collection_name="culture_docs"
     )
     
-    # Analysis prompt
-    analysis_prompt = """
-    Analyze these candidates for the job position and culture fit.
-    
-    Job Description:
-    {job_description}
-    
-    Required Skills:
-    {skills}
-    
-    Company Culture Context:
-    {culture_docs}
-    
-    Candidate Resumes:
-    {resumes}
-    
-    For each candidate, provide:
-    1. Skills match (percentage)
-    2. Culture fit assessment
-    3. Recommendation (move forward/reject)
-    4. Brief explanation
-    """
+    # Analysis prompt template
+    analysis_prompt = PromptTemplate(
+        input_variables=["job_description", "skills", "culture_docs", "resumes"],
+        template="""
+        Analyze these candidates for the job position and culture fit.
+        
+        Job Description:
+        {job_description}
+        
+        Required Skills:
+        {skills}
+        
+        Company Culture Context:
+        {culture_docs}
+        
+        Candidate Resumes:
+        {resumes}
+        
+        For each candidate, provide:
+        1. Skills match (percentage)
+        2. Culture fit assessment
+        3. Recommendation (move forward/reject)
+        4. Brief explanation
+        """
+    )
     
     analysis_chain = LLMChain(
         llm=llm,
         prompt=analysis_prompt
     )
     
-    analysis = analysis_chain.run(
-        job_description=job_description,
-        skills=skills,
-        culture_docs="\n".join([doc.page_content for doc in culture_docs]),
-        resumes="\n".join([doc.page_content for doc in results])
-    )
+    analysis = analysis_chain.run({
+        "job_description": job_description,
+        "skills": skills,
+        "culture_docs": "\n".join([doc.page_content for doc in culture_docs]),
+        "resumes": "\n".join([doc.page_content for doc in results])
+    })
     
     return analysis
 
+
 def create_interface():
     with gr.Blocks() as app:
         gr.Markdown("# AI Recruiter Assistant")