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 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")
+    analyze_button = gr.Button("Ask HR Query")
+
+    hr_input = gr.Textbox(label="HR Query (e.g., 'Which employees are at high risk?')")
+
+    output_text = gr.Markdown(label="Attrition Risk Prediction")
+    process_message = gr.Markdown(label="Processing Status")
+
+    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)