Create utils.py

utils.py

@@ -0,0 +1,64 @@
+# utils.py
+import pandas as pd
+import glob
+import re
+import os
+
+def extract_employee_data(employee_name):
+    """Extract employee details using name from the Markdown file."""
+    details = {}
+    directory = "hrdataset/employees"
+    
+    # Search for employee markdown file
+    search_pattern = f"{directory}/*_{employee_name.replace(' ', '_')}.md"
+    matching_files = glob.glob(search_pattern)
+    
+    if not matching_files:
+        return f"Error: Employee file for {employee_name} not found."
+    
+    employee_path = matching_files[0]
+    
+    # Read the employee markdown file
+    with open(employee_path, "r", encoding="utf-8") as file:
+        content = file.read()
+
+    # Extract employee details using regex
+    details["name"] = re.search(r"\*\*Name:\*\* (.+)", content).group(1)
+    details["role"] = re.search(r"\*\*Role:\*\* (.+)", content).group(1)
+    details["joining_date"] = re.search(r"\*\*Joining Date:\*\* (\d{4}-\d{2}-\d{2})", content).group(1)
+    
+    # Calculate experience
+    joining_year = int(details["joining_date"].split("-")[0])
+    details["experience"] = 2024 - joining_year
+
+    # Extract performance ratings
+    rating_match = re.findall(r"\*\*(\d{4}):\*\* ([\d.]+)", content)
+    if rating_match:
+        latest_rating = sorted(rating_match, key=lambda x: int(x[0]))[-1]
+        details["rating"] = float(latest_rating[1])
+
+    return details
+
+def get_survey_sentiment(employee_name):
+    """Retrieve sentiment analysis for an employee from survey data."""
+    survey_file_path = "hrdataset/surveys/Employee_Culture_Survey_Responses.csv"
+    
+    if not os.path.exists(survey_file_path):
+        return "Error: Survey data not found."
+    
+    survey_df = pd.read_csv(survey_file_path)
+    filtered_df = survey_df[survey_df['Employee'].str.strip().str.lower() == employee_name.strip().lower()]
+    
+    if filtered_df.empty:
+        return "Error: No survey data found for this employee."
+    
+    sentiment_counts = filtered_df['Sentiment'].str.strip().str.lower().value_counts()
+    positive_count = sentiment_counts.get('positive', 0)
+    negative_count = sentiment_counts.get('negative', 0)
+
+    if positive_count > negative_count:
+        return "Positive"
+    elif negative_count > positive_count:
+        return "Negative"
+    else:
+        return "Neutral"