Create app.py

app.py

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+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_dotenv()
+api_key = os.getenv("OPENAI_API_KEY")
+client = OpenAI(api_key=api_key)
+
+pipe = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
+
+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."
+    df["Sentiment"] = df["Feedback"].apply(lambda x: pipe(x)[0]["label"])
+    return {"df": df}, "✅ CSV processed!"
+
+def predict_attrition_risk(employee_name: str, sentiment: str):
+    risk_mapping = {"positive": "Low Risk", "neutral": "Medium Risk", "negative": "High Risk"}
+    return f"{employee_name}: {risk_mapping.get(sentiment.lower(), 'Unknown Sentiment')}"
+
+def analyze_attrition_with_llm(df_dict, hr_query):
+    if df_dict is None or "df" not in df_dict:
+        return "❌ Error: No processed data. Upload a CSV first."
+    
+    df = df_dict["df"]
+    employees_data = {row["Employee"].strip(): row["Sentiment"] for _, row in df.iterrows()}
+    
+    employee_name = hr_query.strip()
+    sentiment = employees_data.get(employee_name)
+    if sentiment:
+        return predict_attrition_risk(employee_name, sentiment)
+    else:
+        return f"{employee_name}: No records found for this employee."
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1>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()
+    hr_input = gr.Textbox(label="Employee Name")
+    analyze_button = gr.Button("Check Attrition Risk")
+    output_text = gr.Markdown()
+    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)