File size: 4,488 Bytes
bbaef5b a64ede5 bbaef5b a64ede5 bbaef5b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
import json
import os
import gradio as gr
import pandas as pd
from openai import OpenAI
from transformers import pipeline
from dotenv import load_dotenv
# Load API key
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
client = OpenAI(api_key=api_key)
# Load sentiment analysis model
pipe = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
# Function to process CSV file and extract sentiment
def process_csv(file):
df = pd.read_csv(file)
if "Feedback" not in df.columns or "Employee" not in df.columns:
return None, "❌ Error: CSV must contain 'Employee' and 'Feedback' columns."
# Apply sentiment analysis
df["Sentiment"] = df["Feedback"].apply(lambda x: pipe(x)[0]["label"])
return {"df": df}, "✅ CSV file processed successfully!"
# Function to predict attrition risk based on sentiment
def predict_attrition_risk(employee_name: str, sentiment: str):
"""Predicts attrition risk based on employee sentiment."""
risk_level = {
"positive": "Low Risk - Positive sentiment detected.",
"neutral": "Medium Risk - Neutral sentiment detected.",
"negative": "High Risk - Negative sentiment detected."
}
return f"{employee_name}: {risk_level.get(sentiment.lower(), 'Unknown Sentiment')}"
# Function Calling Example - HR Query Analysis
def analyze_attrition_with_llm(df_dict, hr_query):
if df_dict is None or "df" not in df_dict:
return "❌ Error: No processed employee data available. Please upload a CSV file first."
df = df_dict["df"]
employees_data = [
{"employee_name": row["Employee"], "sentiment": row["Sentiment"], "feedback": row["Feedback"]}
for _, row in df.iterrows()
]
prompt = f"HR asked: '{hr_query}'. Here is the employee sentiment data:\n{json.dumps(employees_data, indent=2)}\n" \
"Based on sentiment, determine attrition risk and call the function predict_attrition_risk for specific employees."
# Call OpenAI API with function calling enabled
response = client.chat.completions.create(
model="gpt-4-turbo",
messages=[{"role": "user", "content": prompt}],
functions=[
{
"name": "predict_attrition_risk",
"description": "Predicts attrition risk based on sentiment.",
"parameters": {
"type": "object",
"properties": {
"employee_name": {"type": "string", "description": "Employee's name"},
"sentiment": {"type": "string", "description": "Extracted sentiment"}
},
"required": ["employee_name", "sentiment"]
}
}
],
function_call="auto" # Let GPT decide when to call the function
)
# Debug: Print the full LLM response for function calling demonstration
print("🔍 LLM Response:", response)
# Extract function call from GPT response
message = response.choices[0].message
if hasattr(message, "function_call") and message.function_call is not None:
try:
function_call = json.loads(message.function_call.arguments)
employee_name = function_call.get("employee_name")
sentiment = function_call.get("sentiment")
if employee_name and sentiment:
return f"🤖 **LLM Called Function:** `predict_attrition_risk({employee_name}, {sentiment})`\n\n" + predict_attrition_risk(employee_name, sentiment)
except Exception as e:
return f"❌ Error processing LLM function call: {str(e)}"
return "🤖 No specific attrition risk prediction was made."
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("<h1 style='text-align: center;'>AI-Driven Employee Attrition Risk Analysis</h1>")
file_input = gr.File(label="Upload Employee Feedback CSV", file_types=[".csv"])
process_button = gr.Button("Process CSV")
process_message = gr.Markdown(label="Processing Status")
hr_input = gr.Textbox(label="HR Query (e.g., 'Which employees are at high risk?')")
analyze_button = gr.Button("Ask HR Query")
output_text = gr.Markdown(label="Attrition Risk Prediction")
df_state = gr.State()
process_button.click(process_csv, inputs=file_input, outputs=[df_state, process_message])
analyze_button.click(analyze_attrition_with_llm, inputs=[df_state, hr_input], outputs=output_text)
demo.launch(share=True)
|