Update app.py

app.py

@@ -17,11 +17,11 @@ import openai
 # Path to the CSV file in the environment
 CSV_PATH = 'FI_Transactions.csv'
 
-def detect_anomalies(api_key, nu_value, n_clusters):
-    # Set OpenAI API Key
-    os.environ["OPENAI_API_KEY"] = api_key
-    openai.api_key = api_key
-    
+# Get OpenAI API key from environment variables
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
+openai.api_key = OPENAI_API_KEY
+
+def detect_anomalies(nu_value, n_clusters):
     # Read the CSV file from the environment
     df = pd.read_csv(CSV_PATH)
     
@@ -109,6 +109,9 @@ def detect_anomalies(api_key, nu_value, n_clusters):
 def get_ai_insights(df, anomalies_df):
     """Get insights about the anomalies using OpenAI API"""
     try:
+        if not OPENAI_API_KEY:
+            return "OpenAI API key not found in environment variables. AI insights are unavailable."
+            
         # Prepare information about the dataset and anomalies
         df_info = df.describe().to_string()
         anomaly_info = anomalies_df.head(5).to_string() if not anomalies_df.empty else "No anomalies detected"
@@ -145,7 +148,7 @@ def get_ai_insights(df, anomalies_df):
         return response.choices[0].message.content
     
     except Exception as e:
-        return f"Could not generate AI insights. Please check your API key and try again. Error: {str(e)}"
+        return f"Could not generate AI insights. Error: {str(e)}"
 
 def plt_to_img():
     buf = io.BytesIO()
@@ -161,7 +164,6 @@ with gr.Blocks(title="Financial Transaction Anomaly Detection") as demo:
     
     with gr.Row():
         with gr.Column():
-            api_key = gr.Textbox(type="password", label="OpenAI API Key", placeholder="Enter your OpenAI API key")
             nu_slider = gr.Slider(0.01, 0.2, value=0.05, step=0.01, label="SVM nu parameter (controls anomaly threshold)")
             cluster_slider = gr.Slider(2, 10, value=2, step=1, label="Number of KMeans clusters")
             detect_button = gr.Button("Detect Anomalies")
@@ -181,16 +183,15 @@ with gr.Blocks(title="Financial Transaction Anomaly Detection") as demo:
     
     detect_button.click(
         detect_anomalies,
-        inputs=[api_key, nu_slider, cluster_slider],
+        inputs=[nu_slider, cluster_slider],
         outputs=[pie_output, kmeans_output, svm_output, summary_output, anomalies_output]
     )
     
     gr.Markdown("""
     ## How to Use
-    1. Enter your OpenAI API key for AI-powered insights
-    2. Adjust the SVM nu parameter (controls anomaly detection sensitivity)
-    3. Choose the number of clusters for KMeans
-    4. Click 'Detect Anomalies' to analyze the data
+    1. Adjust the SVM nu parameter (controls anomaly detection sensitivity)
+    2. Choose the number of clusters for KMeans
+    3. Click 'Detect Anomalies' to analyze the data
     
     ## Interpretation
     - The pie chart shows the proportion of normal vs anomalous transactions