Update src/app_job_copy_1.py

src/app_job_copy_1.py

@@ -1,648 +1,3 @@
-# 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
-# import tempfile
-# from google.oauth2 import service_account
-# import tiktoken
-
-# st.set_page_config(
-#     page_title="Candidate Matching App",
-#     page_icon="👨‍💻🎯",
-#     layout="wide"
-# )
-# os.environ["STREAMLIT_HOME"] = tempfile.gettempdir()
-# os.environ["STREAMLIT_DISABLE_TELEMETRY"] = "1"
-
-# # 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 upto 3 decimal points.")
-#     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 calculate tokens
-# def calculate_tokens(text, model="gpt-4o-mini"):
-#     """Calculate the number of tokens in a given text for a specific model"""
-#     try:
-#         # Get the encoding for the model
-#         if "gpt-4" in model:
-#             encoding = tiktoken.encoding_for_model("gpt-4o-mini")
-#         elif "gpt-3.5" in model:
-#             encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
-#         else:
-#             encoding = tiktoken.get_encoding("cl100k_base")  # Default for newer models
-            
-#         # Encode the text and return the token count
-#         return len(encoding.encode(text))
-#     except Exception as e:
-#         # If there's an error, make a rough estimate (1 token ≈ 4 chars)
-#         return len(text) // 4
-
-# # Function to display token usage
-# def display_token_usage():
-#     """Display token usage statistics"""
-#     if 'total_input_tokens' not in st.session_state:
-#         st.session_state.total_input_tokens = 0
-#     if 'total_output_tokens' not in st.session_state:
-#         st.session_state.total_output_tokens = 0
-    
-#     total_input = st.session_state.total_input_tokens
-#     total_output = st.session_state.total_output_tokens
-#     total_tokens = total_input + total_output
-    
-#     # Estimate cost based on model
-#     if st.session_state.model_name == "gpt-4o-mini":
-#         input_cost_per_1k = 0.0003  # $0.0003 per 1K input tokens
-#         output_cost_per_1k = 0.0006  # $$0.0006 per 1K output tokens
-#     elif "gpt-4" in st.session_state.model_name:
-#         input_cost_per_1k = 0.005 # $0.30 per 1K input tokens
-#         output_cost_per_1k = 0.60  # $0.60 per 1K output tokens
-#     else:  # Assume gpt-3.5-turbo pricing
-#         input_cost_per_1k = 0.0015  # $0.0015 per 1K input tokens
-#         output_cost_per_1k = 0.015   # $0.002 per 1K output tokens
-    
-#     estimated_cost = (total_input / 1000 * input_cost_per_1k) + (total_output / 1000 * output_cost_per_1k)
-    
-#     st.subheader("📊 Token Usage Statistics")
-    
-#     col1, col2, col3 = st.columns(3)
-    
-#     with col1:
-#         st.metric("Input Tokens", f"{total_input:,}")
-    
-#     with col2:
-#         st.metric("Output Tokens", f"{total_output:,}")
-    
-#     with col3:
-#         st.metric("Total Tokens", f"{total_tokens:,}")
-    
-#     st.markdown(f"**Estimated Cost:** ${estimated_cost:.4f}")
-    
-#     return total_tokens
-
-# # 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"""
-#     # Define the model to use
-#     model_name = "gpt-4o-mini"
-    
-#     # Store model name in session state for token calculation
-#     if 'model_name' not in st.session_state:
-#         st.session_state.model_name = model_name
-    
-#     # Create LLM instance
-#     llm = ChatOpenAI(
-#         model=model_name,
-#         temperature=0.3,
-#         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 Tech 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. 
-
-# Apart from this the candidate must reside near or on the job location. If it is not immediately give a fit score below 5.
-
-#     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
-
-#     Each candidate's fit score should be calculated based on a weighted evaluation of their background and must be distinct even if candidates have similar profiles.
-#     """
-    
-#     # Create query prompt
-#     query_prompt = ChatPromptTemplate.from_messages([
-#         ("system", system),
-#         ("human", """
-#     You are an expert Recruitor. Your task is to determine if the candidate matches the given job. 
-# Provide the score as a `float` rounded to exactly **three decimal places** (e.g., 8.943, 9.211, etc.). 
-# Avoid rounding to whole or one-decimal numbers. Every candidate should have a **unique** fit score.
-#           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.
-#     The `fit_score` must be a float with **exactly three decimal digits** (e.g. 8.812, 9.006). Do not round to 1 or 2 decimals.
-#     """),
-#     ])
-    
-#     # 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", "")
-#         }
-        
-#         # Convert payload to a string for token calculation
-#         payload_str = json.dumps(payload)
-        
-#         # Calculate input tokens
-#         input_tokens = calculate_tokens(payload_str, st.session_state.model_name)
-        
-#         # Call LLM
-#         response = llm_chain.invoke(payload)
-#         print(candidate_data.get("Experience", ""))
-        
-#         # Convert response to string for token calculation
-#         response_str = f"""
-#         candidate_name: {response.candidate_name}
-#         candidate_url: {response.candidate_url}
-#         candidate_summary: {response.candidate_summary}
-#         candidate_location: {response.candidate_location}
-#         fit_score: {float(f"{response.fit_score:.3f}")}
-#         justification: {response.justification}
-#         """
-        
-#         # Calculate output tokens
-#         output_tokens = calculate_tokens(response_str, st.session_state.model_name)
-        
-#         # Update token counts in session state
-#         if 'total_input_tokens' not in st.session_state:
-#             st.session_state.total_input_tokens = 0
-#         if 'total_output_tokens' not in st.session_state:
-#             st.session_state.total_output_tokens = 0
-        
-#         st.session_state.total_input_tokens += input_tokens
-#         st.session_state.total_output_tokens += output_tokens
-        
-#         # 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"""
-#     # Reset token counters for this job
-#     st.session_state.total_input_tokens = 0
-#     st.session_state.total_output_tokens = 0
-    
-#     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": float(f"{response['fit_score']:.3f}"),
-#                 "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"] >= 8.800:
-#                 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.")
-        
-#         # Token usage is now displayed in display_job_selection when showing results
-#         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 = 'src/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])
-#             jobs_df = jobs_df.drop(["Link"],axis = 1)
-#             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, job_sheet)
-            
-#         except Exception as e:
-#             st.error(f"Error processing files: {e}")
-    
-#     st.divider()
-    
-
-# def display_job_selection(jobs_df, candidates_df, sh):
-#     # Initialize session state variables if they don't exist
-#     if 'Selected_Candidates' not in st.session_state:
-#         st.session_state.Selected_Candidates = {}
-#     if 'llm_chain' not in st.session_state:
-#         st.session_state.llm_chain = setup_llm()
-    
-#     st.subheader("Select a job to view potential matches")
-    
-#     # Create job options
-#     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
-        
-#         # Create worksheet name
-#         sheet_name = f"{job_row['Role']} at {job_row['Company']}".strip()[:100]
-        
-#         # Check if worksheet exists and has data
-#         worksheet_exists = False
-#         existing_candidates = []
-        
-#         try:
-#             cand_worksheet = sh.worksheet(sheet_name)
-#             worksheet_exists = True
-#             # Get existing data if worksheet exists
-#             existing_data = cand_worksheet.get_all_values()
-#             if len(existing_data) > 1:  # Has data beyond header
-#                 existing_candidates = existing_data[1:]
-#                 st.session_state[job_key] = True
-#                 # Don't show the info message about existing data
-#         except gspread.exceptions.WorksheetNotFound:
-#             pass
-        
-#         # 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)
-#                     with st.spinner("Processing candidates..."):
-#                         selected_candidates = process_candidates_for_job(
-#                             job_row, 
-#                             candidates_df,
-#                             st.session_state.llm_chain
-#                         )
-#                         selected_candidates.sort(key=lambda x: x["Fit Score"], reverse=True)
-                    
-#                         # Only create worksheet if we have candidates
-#                         if selected_candidates:
-#                             try:
-#                                 if not worksheet_exists:
-#                                     cand_worksheet = sh.add_worksheet(title=sheet_name, rows=10000, cols=50)
-                                
-#                                 # Prepare data for Google Sheet
-#                                 headers = list(selected_candidates[0].keys())
-#                                 rows = [headers] + [list(candidate.values()) for candidate in selected_candidates]
-                                
-#                                 # Clear existing data if any
-#                                 cand_worksheet.clear()
-                                
-#                                 # Write data to the worksheet
-#                                 cand_worksheet.update('A1', rows)
-                                
-#                                 st.success(f"Successfully processed {len(selected_candidates)} candidates")
-#                             except Exception as e:
-#                                 st.error(f"Error writing to Google Sheet: {e}")
-                        
-#                         # Store the results and set as processed
-#                         st.session_state.Selected_Candidates[selected_job_index] = selected_candidates
-#                         st.session_state[job_key] = True
-                        
-#                         # Force refresh
-#                         st.rerun()
-        
-#         # Display selected candidates if already processed
-#         if st.session_state[job_key]:
-#             if existing_candidates:
-#                 # Convert existing worksheet data to our format
-#                 headers = existing_data[0]
-#                 selected_candidates = []
-#                 for row in existing_data[1:]:
-#                     candidate = dict(zip(headers, row))
-#                     selected_candidates.append(candidate)
-#                 st.session_state.Selected_Candidates[selected_job_index] = selected_candidates
-#             elif 'Selected_Candidates' in st.session_state:
-#                 selected_candidates = st.session_state.Selected_Candidates.get(selected_job_index, [])
-#             else:
-#                 selected_candidates = []
-            
-#             # Display selected candidates
-#             st.subheader("Selected Candidates")
-            
-#             # Display token usage statistics (only if we processed with LLM)
-#             if not existing_candidates and 'total_input_tokens' in st.session_state and 'total_output_tokens' in st.session_state:
-#                 display_token_usage()
-            
-#             if len(selected_candidates) > 0:
-#                 for i, candidate in enumerate(selected_candidates):
-#                     with st.expander(f"{i+1}. {candidate['Name']} (Score: {candidate.get('Fit Score', 'N/A')})"):
-#                         col1, col2 = st.columns([3, 1])
-                        
-#                         with col1:
-#                             st.markdown(f"**Summary:** {candidate.get('summary', 'N/A')}")
-#                             st.markdown(f"**Current:** {candidate.get('Current Title & Company', 'N/A')}")
-#                             st.markdown(f"**Education:** {candidate.get('Educational Background', 'N/A')}")
-#                             st.markdown(f"**Experience:** {candidate.get('Years of Experience', 'N/A')}")
-#                             st.markdown(f"**Location:** {candidate.get('Location', 'N/A')}")
-#                             if 'LinkedIn' in candidate:
-#                                 st.markdown(f"**[LinkedIn Profile]({candidate['LinkedIn']})**")
-                        
-#                         with col2:
-#                             if 'Fit Score' in candidate:
-#                                 st.markdown(f"**Fit Score:** {candidate['Fit Score']}")
-                        
-#                         if 'justification' in candidate:
-#                             st.markdown("**Justification:**")
-#                             st.info(candidate['justification'])
-#             else:
-#                 st.info("No candidates found for this job.")
-            
-#             # Add a reset button to start over
-#             if st.button("Reset and Process Again"):
-#                 # Reset this job's processing state
-#                 st.session_state[job_key] = False
-#                 if 'Selected_Candidates' in st.session_state and selected_job_index in st.session_state.Selected_Candidates:
-#                     del st.session_state.Selected_Candidates[selected_job_index]
-#                 st.rerun()
-
-
-# if __name__ == "__main__":
-#     main()
-
-
-
 import streamlit as st
 import pandas as pd
 import json