Update app_job_copy_1.py

app_job_copy_1.py

@@ -1,473 +1,475 @@
-import streamlit as st
-import pandas as pd
-import json
-import os
-from pydantic import BaseModel, Field
-from typing import List, Set, Dict, Any, Optional
-import time
-from langchain_openai import ChatOpenAI
-from langchain_core.messages import HumanMessage
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import PromptTemplate
-import gspread
-from google.oauth2 import service_account
-
-st.set_page_config(
-    page_title="Candidate Matching App",
-    page_icon="👨‍💻🎯",
-    layout="wide"
-)
-
-# Define pydantic model for structured output
-class Shortlist(BaseModel):
-    fit_score: float = Field(description="A score between 0 and 10 indicating how closely the candidate profile matches the job requirements.")
-    candidate_name: str = Field(description="The name of the candidate.")
-    candidate_url: str = Field(description="The URL of the candidate's LinkedIn profile.")
-    candidate_summary: str = Field(description="A brief summary of the candidate's skills and experience along with its educational background.")
-    candidate_location: str = Field(description="The location of the candidate.")
-    justification: str = Field(description="Justification for the shortlisted candidate with the fit score")
-
-# Function to parse and normalize tech stacks
-def parse_tech_stack(stack):
-    if pd.isna(stack) or stack == "" or stack is None:
-        return set()
-    if isinstance(stack, set):
-        return stack
-    try:
-        # Handle potential string representation of sets
-        if isinstance(stack, str) and stack.startswith("{") and stack.endswith("}"):
-            # This could be a string representation of a set
-            items = stack.strip("{}").split(",")
-            return set(item.strip().strip("'\"") for item in items if item.strip())
-        return set(map(lambda x: x.strip().lower(), str(stack).split(',')))
-    except Exception as e:
-        st.error(f"Error parsing tech stack: {e}")
-        return set()
-
-def display_tech_stack(stack_set):
-    if isinstance(stack_set, set):
-        return ", ".join(sorted(stack_set))
-    return str(stack_set)
-
-def get_matching_candidates(job_stack, candidates_df):
-    """Find candidates with matching tech stack for a specific job"""
-    matched = []
-    job_stack_set = parse_tech_stack(job_stack)
-    
-    for _, candidate in candidates_df.iterrows():
-        candidate_stack = parse_tech_stack(candidate['Key Tech Stack'])
-        common = job_stack_set & candidate_stack
-        if len(common) >= 2:
-            matched.append({
-                "Name": candidate["Full Name"],
-                "URL": candidate["LinkedIn URL"],
-                "Degree & Education": candidate["Degree & University"],
-                "Years of Experience": candidate["Years of Experience"],
-                "Current Title & Company": candidate['Current Title & Company'],
-                "Key Highlights": candidate["Key Highlights"],
-                "Location": candidate["Location (from most recent experience)"],
-                "Experience": str(candidate["Experience"]),
-                "Tech Stack": candidate_stack
-            })
-    return matched
-
-def setup_llm():
-    """Set up the LangChain LLM with structured output"""
-    # Create LLM instance
-    llm = ChatOpenAI(
-        model="gpt-4o-mini",
-        temperature=0,
-        max_tokens=None,
-        timeout=None,
-        max_retries=2,
-    )
-    
-    # Create structured output
-    sum_llm = llm.with_structured_output(Shortlist)
-    
-    # Create system prompt
-    system = """You are an expert Recruitor, your task is to analyse the Candidate profile and determine if it matches with the job details and provide a score(out of 10) indicating how compatible the
-    the profile is according to job.
-Try to ensure following points while estimating the candidate's fit score:
-For education:
-Tier1 - MIT, Stanford, CMU, UC Berkeley, Caltech, Harvard, IIT Bombay, IIT Delhi, Princeton, UIUC, University of Washington, Columbia, University of Chicago, Cornell, University of Michigan (Ann Arbor), UT Austin - Maximum points
-Tier2 - UC Davis, Georgia Tech, Purdue, UMass Amherst,etc - Moderate points
-Tier3 - Unknown or unranked institutions - Lower points or reject
-
-Startup Experience Requirement:
-Candidates must have worked  as a direct employee at a VC-backed startup (Seed to series C/D)
-preferred - Y Combinator, Sequoia,a16z,Accel,Founders Fund,LightSpeed,Greylock,Benchmark,Index Ventures,etc.   
-
-    The fit score signifies based on following metrics:
-    1–5 - Poor Fit - Auto-reject
-    6–7 - Weak Fit - Auto-reject
-    8.0–8.7 - Moderate Fit - Auto-reject
-    8.8–10 - STRONG Fit - Include in results
-    """
-    
-    # Create query prompt
-    query_prompt = ChatPromptTemplate.from_messages([
-        ("system", system),
-        ("human", """
-    You are an expert Recruitor, your task is to determine if the user is a correct match for the given job or not.
-          For this you will be provided with the follwing inputs of job and candidates:
-    Job Details
-    Company: {Company}
-    Role: {Role}
-    About Company: {desc}
-    Locations: {Locations}
-    Tech Stack: {Tech_Stack}
-    Industry: {Industry}
-    
-
-    Candidate Details:
-    Full Name: {Full_Name}
-    LinkedIn URL: {LinkedIn_URL}
-    Current Title & Company: {Current_Title_Company}
-    Years of Experience: {Years_of_Experience}
-    Degree & University: {Degree_University}
-    Key Tech Stack: {Key_Tech_Stack}
-    Key Highlights: {Key_Highlights}
-    Location (from most recent experience): {cand_Location}
-    Past_Experience: {Experience}
-
-
-    Answer in the structured manner as per the schema.
-    If any parameter is Unknown try not to include in the summary, only include those parameters which are known.
-    """),
-    ])
-    
-    # Chain the prompt and LLM
-    cat_class = query_prompt | sum_llm
-    
-    return cat_class
-
-def call_llm(candidate_data, job_data, llm_chain):
-    """Call the actual LLM to evaluate the candidate"""
-    try:
-        # Convert tech stacks to strings for the LLM payload
-        job_tech_stack = job_data.get("Tech_Stack", set())
-        candidate_tech_stack = candidate_data.get("Tech Stack", set())
-        
-        if isinstance(job_tech_stack, set):
-            job_tech_stack = ", ".join(sorted(job_tech_stack))
-            
-        if isinstance(candidate_tech_stack, set):
-            candidate_tech_stack = ", ".join(sorted(candidate_tech_stack))
-        
-        # Prepare payload for LLM
-        payload = {
-            "Company": job_data.get("Company", ""),
-            "Role": job_data.get("Role", ""),
-            "desc": job_data.get("desc", ""),
-            "Locations": job_data.get("Locations", ""),
-            "Tech_Stack": job_tech_stack,
-            "Industry": job_data.get("Industry", ""),
-
-            "Full_Name": candidate_data.get("Name", ""),
-            "LinkedIn_URL": candidate_data.get("URL", ""),
-            "Current_Title_Company": candidate_data.get("Current Title & Company", ""),
-            "Years_of_Experience": candidate_data.get("Years of Experience", ""),
-            "Degree_University": candidate_data.get("Degree & Education", ""),
-            "Key_Tech_Stack": candidate_tech_stack,
-            "Key_Highlights": candidate_data.get("Key Highlights", ""),
-            "cand_Location": candidate_data.get("Location", ""),
-            "Experience": candidate_data.get("Experience", "")
-        }
-        
-        # Call LLM
-        response = llm_chain.invoke(payload)
-        print(candidate_data.get("Experience", ""))
-        
-        # Return response in expected format
-        return {
-            "candidate_name": response.candidate_name,
-            "candidate_url": response.candidate_url,
-            "candidate_summary": response.candidate_summary,
-            "candidate_location": response.candidate_location,
-            "fit_score": response.fit_score,
-            "justification": response.justification
-        }
-    except Exception as e:
-        st.error(f"Error calling LLM: {e}")
-        # Fallback to a default response
-        return {
-            "candidate_name": candidate_data.get("Name", "Unknown"),
-            "candidate_url": candidate_data.get("URL", ""),
-            "candidate_summary": "Error processing candidate profile",
-            "candidate_location": candidate_data.get("Location", "Unknown"),
-            "fit_score": 0.0,
-            "justification": f"Error in LLM processing: {str(e)}"
-        }
-
-def process_candidates_for_job(job_row, candidates_df, llm_chain=None):
-    """Process candidates for a specific job using the LLM"""
-    if llm_chain is None:
-        with st.spinner("Setting up LLM..."):
-            llm_chain = setup_llm()
-    
-    selected_candidates = []
-    
-    try:
-        # Get job-specific data
-        job_data = {
-            "Company": job_row["Company"],
-            "Role": job_row["Role"],
-            "desc": job_row.get("One liner", ""),
-            "Locations": job_row.get("Locations", ""),
-            "Tech_Stack": job_row["Tech Stack"],
-            "Industry": job_row.get("Industry", "")
-        }
-        
-        # Find matching candidates for this job
-        with st.spinner("Finding matching candidates based on tech stack..."):
-            matching_candidates = get_matching_candidates(job_row["Tech Stack"], candidates_df)
-        
-        if not matching_candidates:
-            st.warning("No candidates with matching tech stack found for this job.")
-            return []
-        
-        st.success(f"Found {len(matching_candidates)} candidates with matching tech stack.")
-        
-        # Create progress elements
-        candidates_progress = st.progress(0)
-        candidate_status = st.empty()
-        
-        # Process each candidate
-        for i, candidate_data in enumerate(matching_candidates):
-            # Update progress
-            candidates_progress.progress((i + 1) / len(matching_candidates))
-            candidate_status.text(f"Evaluating candidate {i+1}/{len(matching_candidates)}: {candidate_data.get('Name', 'Unknown')}")
-            
-            # Process the candidate with the LLM
-            response = call_llm(candidate_data, job_data, llm_chain)
-            
-            response_dict = {
-                "Name": response["candidate_name"],
-                "LinkedIn": response["candidate_url"],
-                "summary": response["candidate_summary"],
-                "Location": response["candidate_location"],
-                "Fit Score": response["fit_score"],
-                "justification": response["justification"],
-                # Add back original candidate data for context
-                "Educational Background": candidate_data.get("Degree & Education", ""),
-                "Years of Experience": candidate_data.get("Years of Experience", ""),
-                "Current Title & Company": candidate_data.get("Current Title & Company", "")
-            }
-            
-            # Add to selected candidates if score is high enough
-            if response["fit_score"] >= 8.8:
-                selected_candidates.append(response_dict)
-                st.markdown(response_dict)
-            else:
-                st.write(f"Rejected candidate: {response_dict['Name']} with score: {response['fit_score']}")
-        
-        # Clear progress indicators
-        candidates_progress.empty()
-        candidate_status.empty()
-        
-        # Show results
-        if selected_candidates:
-            st.success(f"✅ Found {len(selected_candidates)} suitable candidates for this job!")
-        else:
-            st.info("No candidates met the minimum fit score threshold for this job.")
-        
-        return selected_candidates
-        
-    except Exception as e:
-        st.error(f"Error processing job: {e}")
-        return []
-
-def main():
-    st.title("👨‍💻 Candidate Matching App")
-    
-    # Initialize session state
-    if 'processed_jobs' not in st.session_state:
-        st.session_state.processed_jobs = {}
-    
-    st.write("""
-    This app matches job listings with candidate profiles based on tech stack and other criteria.
-    Select a job to find matching candidates.
-    """)
-    
-    # API Key input
-    with st.sidebar:
-        st.header("API Configuration")
-        api_key = st.text_input("Enter OpenAI API Key", type="password")
-        if api_key:
-            os.environ["OPENAI_API_KEY"] = api_key
-            st.success("API Key set!")
-        else:
-            st.warning("Please enter OpenAI API Key to use LLM features")
-            
-    # Show API key warning if not set
-    SERVICE_ACCOUNT_FILE = 'synapse-recruitment-e94255ca76fd.json'
-    SCOPES = ['https://www.googleapis.com/auth/spreadsheets']
-    creds = service_account.Credentials.from_service_account_file(SERVICE_ACCOUNT_FILE, scopes=SCOPES)
-    gc = gspread.authorize(creds)
-    job_sheet = gc.open_by_key('1BZlvbtFyiQ9Pgr_lpepDJua1ZeVEqrCLjssNd6OiG9k')
-    candidates_sheet = gc.open_by_key('1u_9o5f0MPHFUSScjEcnA8Lojm4Y9m9LuWhvjYm6ytF4')
-
-    if not api_key:
-        st.warning("⚠️ You need to provide an OpenAI API key in the sidebar to use this app.")
-
-    if api_key:
-        try:
-            # Load data from Google Sheets
-            job_worksheet = job_sheet.worksheet('paraform_jobs_formatted')
-            job_data = job_worksheet.get_all_values()
-            candidate_worksheet = candidates_sheet.worksheet('transformed_candidates_updated')
-            candidate_data = candidate_worksheet.get_all_values()
-            
-            # Convert to DataFrames
-            jobs_df = pd.DataFrame(job_data[1:], columns=job_data[0])
-            candidates_df = pd.DataFrame(candidate_data[1:], columns=candidate_data[0])
-            candidates_df = candidates_df.fillna("Unknown")
-            
-            # Display data preview
-            with st.expander("Preview uploaded data"):
-                st.subheader("Jobs Data Preview")
-                st.dataframe(jobs_df.head(3))
-                
-                st.subheader("Candidates Data Preview")
-                st.dataframe(candidates_df.head(3))
-            
-            # Map column names if needed
-            column_mapping = {
-                "Full Name": "Full Name",
-                "LinkedIn URL": "LinkedIn URL",
-                "Current Title & Company": "Current Title & Company",
-                "Years of Experience": "Years of Experience",
-                "Degree & University": "Degree & University",
-                "Key Tech Stack": "Key Tech Stack", 
-                "Key Highlights": "Key Highlights",
-                "Location (from most recent experience)": "Location (from most recent experience)"
-            }
-            
-            # Rename columns if they don't match expected
-            candidates_df = candidates_df.rename(columns={
-                col: mapping for col, mapping in column_mapping.items() 
-                if col in candidates_df.columns and col != mapping
-            })
-            
-            # Now, instead of processing all jobs upfront, we'll display job selection
-            # and only process the selected job when the user chooses it
-            display_job_selection(jobs_df, candidates_df)
-            
-        except Exception as e:
-            st.error(f"Error processing files: {e}")
-    
-    st.divider()
-    
-
-def display_job_selection(jobs_df, candidates_df):
-    # Store the LLM chain as a session state to avoid recreating it
-    if 'llm_chain' not in st.session_state:
-        st.session_state.llm_chain = None
-    
-    st.subheader("Select a job to view potential matches")
-    
-    # Create job options - but don't compute matches yet
-    job_options = []
-    for i, row in jobs_df.iterrows():
-        job_options.append(f"{row['Role']} at {row['Company']}")
-    
-    if job_options:
-        selected_job_index = st.selectbox("Jobs:", 
-                                      range(len(job_options)),
-                                      format_func=lambda x: job_options[x])
-        
-        # Display job details
-        job_row = jobs_df.iloc[selected_job_index]
-        
-        # Parse tech stack for display
-        job_row_stack = parse_tech_stack(job_row["Tech Stack"])
-        
-        col1, col2 = st.columns([2, 1])
-        
-        with col1:
-            st.subheader(f"Job Details: {job_row['Role']}")
-            
-            job_details = {
-                "Company": job_row["Company"],
-                "Role": job_row["Role"],
-                "Description": job_row.get("One liner", "N/A"),
-                "Locations": job_row.get("Locations", "N/A"),
-                "Industry": job_row.get("Industry", "N/A"),
-                "Tech Stack": display_tech_stack(job_row_stack)
-            }
-            
-            for key, value in job_details.items():
-                st.markdown(f"**{key}:** {value}")
-        
-        # Create a key for this job in session state
-        job_key = f"job_{selected_job_index}_processed"
-        
-        if job_key not in st.session_state:
-            st.session_state[job_key] = False
-        
-        # Add a process button for this job
-        if not st.session_state[job_key]:
-            if st.button(f"Find Matching Candidates for this Job"):
-                if "OPENAI_API_KEY" not in os.environ or not os.environ["OPENAI_API_KEY"]:
-                    st.error("Please enter your OpenAI API key in the sidebar before processing")
-                else:
-                    # Process candidates for this job (only when requested)
-                    selected_candidates = process_candidates_for_job(
-                        job_row, 
-                        candidates_df,
-                        st.session_state.llm_chain
-                    )
-                    
-                    # Store the results and set as processed
-                    if 'Selected_Candidates' not in st.session_state:
-                        st.session_state.Selected_Candidates = {}
-                    st.session_state.Selected_Candidates[selected_job_index] = selected_candidates
-                    st.session_state[job_key] = True
-                    
-                    # Store the LLM chain for reuse
-                    if st.session_state.llm_chain is None:
-                        st.session_state.llm_chain = setup_llm()
-                    
-                    # Force refresh
-                    st.rerun()
-        
-        # Display selected candidates if already processed
-        if st.session_state[job_key] and 'Selected_Candidates' in st.session_state:
-            selected_candidates = st.session_state.Selected_Candidates.get(selected_job_index, [])
-            
-            # Display selected candidates
-            st.subheader("Selected Candidates")
-            
-            if len(selected_candidates) > 0:
-                for i, candidate in enumerate(selected_candidates):
-                    with st.expander(f"{i+1}. {candidate['Name']} (Score: {candidate['Fit Score']})"):
-                        col1, col2 = st.columns([3, 1])
-                        
-                        with col1:
-                            st.markdown(f"**Summary:** {candidate['summary']}")
-                            st.markdown(f"**Current:** {candidate['Current Title & Company']}")
-                            st.markdown(f"**Education:** {candidate['Educational Background']}")
-                            st.markdown(f"**Experience:** {candidate['Years of Experience']}")
-                            st.markdown(f"**Location:** {candidate['Location']}")
-                            st.markdown(f"**[LinkedIn Profile]({candidate['LinkedIn']})**")
-                        
-                        with col2:
-                            st.markdown(f"**Fit Score:** {candidate['Fit Score']}")
-                        
-                        st.markdown("**Justification:**")
-                        st.info(candidate['justification'])
-            else:
-                st.info("No candidates met the minimum score threshold (8.8) for this job.")
-                
-                # We don't show tech-matched candidates here since they are generated
-                # during the LLM matching process now
-            
-            # Add a reset button to start over
-            if st.button("Reset and Process Again"):
-                st.session_state[job_key] = False
-                st.rerun()
-
-if __name__ == "__main__":
+import streamlit as st
+import pandas as pd
+import json
+import os
+from pydantic import BaseModel, Field
+from typing import List, Set, Dict, Any, Optional
+import time
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
+import gspread
+from google.oauth2 import service_account
+
+st.set_page_config(
+    page_title="Candidate Matching App",
+    page_icon="👨‍💻🎯",
+    layout="wide"
+)
+
+# Define pydantic model for structured output
+class Shortlist(BaseModel):
+    fit_score: float = Field(description="A score between 0 and 10 indicating how closely the candidate profile matches the job requirements.")
+    candidate_name: str = Field(description="The name of the candidate.")
+    candidate_url: str = Field(description="The URL of the candidate's LinkedIn profile.")
+    candidate_summary: str = Field(description="A brief summary of the candidate's skills and experience along with its educational background.")
+    candidate_location: str = Field(description="The location of the candidate.")
+    justification: str = Field(description="Justification for the shortlisted candidate with the fit score")
+
+# Function to parse and normalize tech stacks
+def parse_tech_stack(stack):
+    if pd.isna(stack) or stack == "" or stack is None:
+        return set()
+    if isinstance(stack, set):
+        return stack
+    try:
+        # Handle potential string representation of sets
+        if isinstance(stack, str) and stack.startswith("{") and stack.endswith("}"):
+            # This could be a string representation of a set
+            items = stack.strip("{}").split(",")
+            return set(item.strip().strip("'\"") for item in items if item.strip())
+        return set(map(lambda x: x.strip().lower(), str(stack).split(',')))
+    except Exception as e:
+        st.error(f"Error parsing tech stack: {e}")
+        return set()
+
+def display_tech_stack(stack_set):
+    if isinstance(stack_set, set):
+        return ", ".join(sorted(stack_set))
+    return str(stack_set)
+
+def get_matching_candidates(job_stack, candidates_df):
+    """Find candidates with matching tech stack for a specific job"""
+    matched = []
+    job_stack_set = parse_tech_stack(job_stack)
+    
+    for _, candidate in candidates_df.iterrows():
+        candidate_stack = parse_tech_stack(candidate['Key Tech Stack'])
+        common = job_stack_set & candidate_stack
+        if len(common) >= 2:
+            matched.append({
+                "Name": candidate["Full Name"],
+                "URL": candidate["LinkedIn URL"],
+                "Degree & Education": candidate["Degree & University"],
+                "Years of Experience": candidate["Years of Experience"],
+                "Current Title & Company": candidate['Current Title & Company'],
+                "Key Highlights": candidate["Key Highlights"],
+                "Location": candidate["Location (from most recent experience)"],
+                "Experience": str(candidate["Experience"]),
+                "Tech Stack": candidate_stack
+            })
+    return matched
+
+def setup_llm():
+    """Set up the LangChain LLM with structured output"""
+    # Create LLM instance
+    llm = ChatOpenAI(
+        model="gpt-4o-mini",
+        temperature=0,
+        max_tokens=None,
+        timeout=None,
+        max_retries=2,
+    )
+    
+    # Create structured output
+    sum_llm = llm.with_structured_output(Shortlist)
+    
+    # Create system prompt
+    system = """You are an expert Recruitor, your task is to analyse the Candidate profile and determine if it matches with the job details and provide a score(out of 10) indicating how compatible the
+    the profile is according to job.
+Try to ensure following points while estimating the candidate's fit score:
+For education:
+Tier1 - MIT, Stanford, CMU, UC Berkeley, Caltech, Harvard, IIT Bombay, IIT Delhi, Princeton, UIUC, University of Washington, Columbia, University of Chicago, Cornell, University of Michigan (Ann Arbor), UT Austin - Maximum points
+Tier2 - UC Davis, Georgia Tech, Purdue, UMass Amherst,etc - Moderate points
+Tier3 - Unknown or unranked institutions - Lower points or reject
+
+Startup Experience Requirement:
+Candidates must have worked  as a direct employee at a VC-backed startup (Seed to series C/D)
+preferred - Y Combinator, Sequoia,a16z,Accel,Founders Fund,LightSpeed,Greylock,Benchmark,Index Ventures,etc.   
+
+    The fit score signifies based on following metrics:
+    1–5 - Poor Fit - Auto-reject
+    6–7 - Weak Fit - Auto-reject
+    8.0–8.7 - Moderate Fit - Auto-reject
+    8.8–10 - STRONG Fit - Include in results
+    """
+    
+    # Create query prompt
+    query_prompt = ChatPromptTemplate.from_messages([
+        ("system", system),
+        ("human", """
+    You are an expert Recruitor, your task is to determine if the user is a correct match for the given job or not.
+          For this you will be provided with the follwing inputs of job and candidates:
+    Job Details
+    Company: {Company}
+    Role: {Role}
+    About Company: {desc}
+    Locations: {Locations}
+    Tech Stack: {Tech_Stack}
+    Industry: {Industry}
+    
+
+    Candidate Details:
+    Full Name: {Full_Name}
+    LinkedIn URL: {LinkedIn_URL}
+    Current Title & Company: {Current_Title_Company}
+    Years of Experience: {Years_of_Experience}
+    Degree & University: {Degree_University}
+    Key Tech Stack: {Key_Tech_Stack}
+    Key Highlights: {Key_Highlights}
+    Location (from most recent experience): {cand_Location}
+    Past_Experience: {Experience}
+
+
+    Answer in the structured manner as per the schema.
+    If any parameter is Unknown try not to include in the summary, only include those parameters which are known.
+    """),
+    ])
+    
+    # Chain the prompt and LLM
+    cat_class = query_prompt | sum_llm
+    
+    return cat_class
+
+def call_llm(candidate_data, job_data, llm_chain):
+    """Call the actual LLM to evaluate the candidate"""
+    try:
+        # Convert tech stacks to strings for the LLM payload
+        job_tech_stack = job_data.get("Tech_Stack", set())
+        candidate_tech_stack = candidate_data.get("Tech Stack", set())
+        
+        if isinstance(job_tech_stack, set):
+            job_tech_stack = ", ".join(sorted(job_tech_stack))
+            
+        if isinstance(candidate_tech_stack, set):
+            candidate_tech_stack = ", ".join(sorted(candidate_tech_stack))
+        
+        # Prepare payload for LLM
+        payload = {
+            "Company": job_data.get("Company", ""),
+            "Role": job_data.get("Role", ""),
+            "desc": job_data.get("desc", ""),
+            "Locations": job_data.get("Locations", ""),
+            "Tech_Stack": job_tech_stack,
+            "Industry": job_data.get("Industry", ""),
+
+            "Full_Name": candidate_data.get("Name", ""),
+            "LinkedIn_URL": candidate_data.get("URL", ""),
+            "Current_Title_Company": candidate_data.get("Current Title & Company", ""),
+            "Years_of_Experience": candidate_data.get("Years of Experience", ""),
+            "Degree_University": candidate_data.get("Degree & Education", ""),
+            "Key_Tech_Stack": candidate_tech_stack,
+            "Key_Highlights": candidate_data.get("Key Highlights", ""),
+            "cand_Location": candidate_data.get("Location", ""),
+            "Experience": candidate_data.get("Experience", "")
+        }
+        
+        # Call LLM
+        response = llm_chain.invoke(payload)
+        print(candidate_data.get("Experience", ""))
+        
+        # Return response in expected format
+        return {
+            "candidate_name": response.candidate_name,
+            "candidate_url": response.candidate_url,
+            "candidate_summary": response.candidate_summary,
+            "candidate_location": response.candidate_location,
+            "fit_score": response.fit_score,
+            "justification": response.justification
+        }
+    except Exception as e:
+        st.error(f"Error calling LLM: {e}")
+        # Fallback to a default response
+        return {
+            "candidate_name": candidate_data.get("Name", "Unknown"),
+            "candidate_url": candidate_data.get("URL", ""),
+            "candidate_summary": "Error processing candidate profile",
+            "candidate_location": candidate_data.get("Location", "Unknown"),
+            "fit_score": 0.0,
+            "justification": f"Error in LLM processing: {str(e)}"
+        }
+
+def process_candidates_for_job(job_row, candidates_df, llm_chain=None):
+    """Process candidates for a specific job using the LLM"""
+    if llm_chain is None:
+        with st.spinner("Setting up LLM..."):
+            llm_chain = setup_llm()
+    
+    selected_candidates = []
+    
+    try:
+        # Get job-specific data
+        job_data = {
+            "Company": job_row["Company"],
+            "Role": job_row["Role"],
+            "desc": job_row.get("One liner", ""),
+            "Locations": job_row.get("Locations", ""),
+            "Tech_Stack": job_row["Tech Stack"],
+            "Industry": job_row.get("Industry", "")
+        }
+        
+        # Find matching candidates for this job
+        with st.spinner("Finding matching candidates based on tech stack..."):
+            matching_candidates = get_matching_candidates(job_row["Tech Stack"], candidates_df)
+        
+        if not matching_candidates:
+            st.warning("No candidates with matching tech stack found for this job.")
+            return []
+        
+        st.success(f"Found {len(matching_candidates)} candidates with matching tech stack.")
+        
+        # Create progress elements
+        candidates_progress = st.progress(0)
+        candidate_status = st.empty()
+        
+        # Process each candidate
+        for i, candidate_data in enumerate(matching_candidates):
+            # Update progress
+            candidates_progress.progress((i + 1) / len(matching_candidates))
+            candidate_status.text(f"Evaluating candidate {i+1}/{len(matching_candidates)}: {candidate_data.get('Name', 'Unknown')}")
+            
+            # Process the candidate with the LLM
+            response = call_llm(candidate_data, job_data, llm_chain)
+            
+            response_dict = {
+                "Name": response["candidate_name"],
+                "LinkedIn": response["candidate_url"],
+                "summary": response["candidate_summary"],
+                "Location": response["candidate_location"],
+                "Fit Score": response["fit_score"],
+                "justification": response["justification"],
+                # Add back original candidate data for context
+                "Educational Background": candidate_data.get("Degree & Education", ""),
+                "Years of Experience": candidate_data.get("Years of Experience", ""),
+                "Current Title & Company": candidate_data.get("Current Title & Company", "")
+            }
+            
+            # Add to selected candidates if score is high enough
+            if response["fit_score"] >= 8.8:
+                selected_candidates.append(response_dict)
+                st.markdown(response_dict)
+            else:
+                st.write(f"Rejected candidate: {response_dict['Name']} with score: {response['fit_score']}")
+        
+        # Clear progress indicators
+        candidates_progress.empty()
+        candidate_status.empty()
+        
+        # Show results
+        if selected_candidates:
+            st.success(f"✅ Found {len(selected_candidates)} suitable candidates for this job!")
+        else:
+            st.info("No candidates met the minimum fit score threshold for this job.")
+        
+        return selected_candidates
+        
+    except Exception as e:
+        st.error(f"Error processing job: {e}")
+        return []
+
+def main():
+    st.title("👨‍💻 Candidate Matching App")
+    
+    # Initialize session state
+    if 'processed_jobs' not in st.session_state:
+        st.session_state.processed_jobs = {}
+    
+    st.write("""
+    This app matches job listings with candidate profiles based on tech stack and other criteria.
+    Select a job to find matching candidates.
+    """)
+    
+    # API Key input
+    with st.sidebar:
+        st.header("API Configuration")
+        api_key = st.text_input("Enter OpenAI API Key", type="password")
+        if api_key:
+            os.environ["OPENAI_API_KEY"] = api_key
+            st.success("API Key set!")
+        else:
+            st.warning("Please enter OpenAI API Key to use LLM features")
+            
+    # Show API key warning if not set
+    secret_content = os.getenv("GCP_SERVICE_ACCOUNT")
+    secret_content = secret_content.replace("\n", "\\n")
+    secret_content = json.loads(secret_content)
+    SCOPES = ['https://www.googleapis.com/auth/spreadsheets']
+    creds = service_account.Credentials.from_service_account_info(secret_content, scopes=SCOPES)
+    gc = gspread.authorize(creds)
+    job_sheet = gc.open_by_key('1BZlvbtFyiQ9Pgr_lpepDJua1ZeVEqrCLjssNd6OiG9k')
+    candidates_sheet = gc.open_by_key('1u_9o5f0MPHFUSScjEcnA8Lojm4Y9m9LuWhvjYm6ytF4')
+
+    if not api_key:
+        st.warning("⚠️ You need to provide an OpenAI API key in the sidebar to use this app.")
+
+    if api_key:
+        try:
+            # Load data from Google Sheets
+            job_worksheet = job_sheet.worksheet('paraform_jobs_formatted')
+            job_data = job_worksheet.get_all_values()
+            candidate_worksheet = candidates_sheet.worksheet('transformed_candidates_updated')
+            candidate_data = candidate_worksheet.get_all_values()
+            
+            # Convert to DataFrames
+            jobs_df = pd.DataFrame(job_data[1:], columns=job_data[0])
+            candidates_df = pd.DataFrame(candidate_data[1:], columns=candidate_data[0])
+            candidates_df = candidates_df.fillna("Unknown")
+            
+            # Display data preview
+            with st.expander("Preview uploaded data"):
+                st.subheader("Jobs Data Preview")
+                st.dataframe(jobs_df.head(3))
+                
+                st.subheader("Candidates Data Preview")
+                st.dataframe(candidates_df.head(3))
+            
+            # Map column names if needed
+            column_mapping = {
+                "Full Name": "Full Name",
+                "LinkedIn URL": "LinkedIn URL",
+                "Current Title & Company": "Current Title & Company",
+                "Years of Experience": "Years of Experience",
+                "Degree & University": "Degree & University",
+                "Key Tech Stack": "Key Tech Stack", 
+                "Key Highlights": "Key Highlights",
+                "Location (from most recent experience)": "Location (from most recent experience)"
+            }
+            
+            # Rename columns if they don't match expected
+            candidates_df = candidates_df.rename(columns={
+                col: mapping for col, mapping in column_mapping.items() 
+                if col in candidates_df.columns and col != mapping
+            })
+            
+            # Now, instead of processing all jobs upfront, we'll display job selection
+            # and only process the selected job when the user chooses it
+            display_job_selection(jobs_df, candidates_df)
+            
+        except Exception as e:
+            st.error(f"Error processing files: {e}")
+    
+    st.divider()
+    
+
+def display_job_selection(jobs_df, candidates_df):
+    # Store the LLM chain as a session state to avoid recreating it
+    if 'llm_chain' not in st.session_state:
+        st.session_state.llm_chain = None
+    
+    st.subheader("Select a job to view potential matches")
+    
+    # Create job options - but don't compute matches yet
+    job_options = []
+    for i, row in jobs_df.iterrows():
+        job_options.append(f"{row['Role']} at {row['Company']}")
+    
+    if job_options:
+        selected_job_index = st.selectbox("Jobs:", 
+                                      range(len(job_options)),
+                                      format_func=lambda x: job_options[x])
+        
+        # Display job details
+        job_row = jobs_df.iloc[selected_job_index]
+        
+        # Parse tech stack for display
+        job_row_stack = parse_tech_stack(job_row["Tech Stack"])
+        
+        col1, col2 = st.columns([2, 1])
+        
+        with col1:
+            st.subheader(f"Job Details: {job_row['Role']}")
+            
+            job_details = {
+                "Company": job_row["Company"],
+                "Role": job_row["Role"],
+                "Description": job_row.get("One liner", "N/A"),
+                "Locations": job_row.get("Locations", "N/A"),
+                "Industry": job_row.get("Industry", "N/A"),
+                "Tech Stack": display_tech_stack(job_row_stack)
+            }
+            
+            for key, value in job_details.items():
+                st.markdown(f"**{key}:** {value}")
+        
+        # Create a key for this job in session state
+        job_key = f"job_{selected_job_index}_processed"
+        
+        if job_key not in st.session_state:
+            st.session_state[job_key] = False
+        
+        # Add a process button for this job
+        if not st.session_state[job_key]:
+            if st.button(f"Find Matching Candidates for this Job"):
+                if "OPENAI_API_KEY" not in os.environ or not os.environ["OPENAI_API_KEY"]:
+                    st.error("Please enter your OpenAI API key in the sidebar before processing")
+                else:
+                    # Process candidates for this job (only when requested)
+                    selected_candidates = process_candidates_for_job(
+                        job_row, 
+                        candidates_df,
+                        st.session_state.llm_chain
+                    )
+                    
+                    # Store the results and set as processed
+                    if 'Selected_Candidates' not in st.session_state:
+                        st.session_state.Selected_Candidates = {}
+                    st.session_state.Selected_Candidates[selected_job_index] = selected_candidates
+                    st.session_state[job_key] = True
+                    
+                    # Store the LLM chain for reuse
+                    if st.session_state.llm_chain is None:
+                        st.session_state.llm_chain = setup_llm()
+                    
+                    # Force refresh
+                    st.rerun()
+        
+        # Display selected candidates if already processed
+        if st.session_state[job_key] and 'Selected_Candidates' in st.session_state:
+            selected_candidates = st.session_state.Selected_Candidates.get(selected_job_index, [])
+            
+            # Display selected candidates
+            st.subheader("Selected Candidates")
+            
+            if len(selected_candidates) > 0:
+                for i, candidate in enumerate(selected_candidates):
+                    with st.expander(f"{i+1}. {candidate['Name']} (Score: {candidate['Fit Score']})"):
+                        col1, col2 = st.columns([3, 1])
+                        
+                        with col1:
+                            st.markdown(f"**Summary:** {candidate['summary']}")
+                            st.markdown(f"**Current:** {candidate['Current Title & Company']}")
+                            st.markdown(f"**Education:** {candidate['Educational Background']}")
+                            st.markdown(f"**Experience:** {candidate['Years of Experience']}")
+                            st.markdown(f"**Location:** {candidate['Location']}")
+                            st.markdown(f"**[LinkedIn Profile]({candidate['LinkedIn']})**")
+                        
+                        with col2:
+                            st.markdown(f"**Fit Score:** {candidate['Fit Score']}")
+                        
+                        st.markdown("**Justification:**")
+                        st.info(candidate['justification'])
+            else:
+                st.info("No candidates met the minimum score threshold (8.8) for this job.")
+                
+                # We don't show tech-matched candidates here since they are generated
+                # during the LLM matching process now
+            
+            # Add a reset button to start over
+            if st.button("Reset and Process Again"):
+                st.session_state[job_key] = False
+                st.rerun()
+
+if __name__ == "__main__":
     main()