Update app.py

app.py

@@ -66,7 +66,6 @@ def analyze_financial_data(file):
         # Read the uploaded CSV file
         data = pd.read_csv(file.name, encoding="utf-8", on_bad_lines='skip')
     except UnicodeDecodeError:
-        # Fallback for non-UTF-8 encoding
         try:
             data = pd.read_csv(file.name, encoding="ISO-8859-1", on_bad_lines='skip')
         except Exception as e:
@@ -74,19 +73,29 @@ def analyze_financial_data(file):
     except Exception as e:
         return {"error": f"An unexpected error occurred: {str(e)}"}
 
-    # Handle empty or malformed data
     if data.empty:
         return {"error": "The uploaded file is empty or has an invalid structure."}
 
-    # Validate required columns
-    required_columns = ["Revenue", "Profit", "Loss", "Expenses", "Risk_Level"]
-    if not all(column in data.columns for column in required_columns):
-        return {"error": f"The CSV must include these columns: {', '.join(required_columns)}"}
+    # Dynamically map required columns
+    expected_columns = ["Revenue", "Profit", "Loss", "Expenses", "Risk_Level"]
+    available_columns = data.columns.tolist()
+    column_mapping = {}
+    
+    for expected_col in expected_columns:
+        for available_col in available_columns:
+            if expected_col.lower() in available_col.lower():
+                column_mapping[expected_col] = available_col
+                break
+    
+    if len(column_mapping) != len(expected_columns):
+        return {"error": f"The CSV must contain columns similar to: {', '.join(expected_columns)}"}
+    
+    data.rename(columns=column_mapping, inplace=True)
 
     try:
         # Data Preprocessing
-        X = data[["Revenue", "Profit", "Loss", "Expenses"]].dropna()
-        y = data["Risk_Level"].dropna()
+        X = data[[column_mapping["Revenue"], column_mapping["Profit"], column_mapping["Loss"], column_mapping["Expenses"]]].dropna()
+        y = data[column_mapping["Risk_Level"]].dropna()
 
         if X.empty or y.empty:
             return {"error": "The data contains missing values or invalid rows after cleaning."}
@@ -114,10 +123,9 @@ def analyze_financial_data(file):
         gan.train(torch.tensor(X_pca, dtype=torch.float32), epochs=200)
         anomalies = gan.generate(n_samples=5, input_dim=X_pca.shape[1])
 
-        # Insights
-        total_revenue = data["Revenue"].sum()
-        total_profit = data["Profit"].sum()
-        total_loss = data["Loss"].sum()
+        total_revenue = data[column_mapping["Revenue"]].sum()
+        total_profit = data[column_mapping["Profit"]].sum()
+        total_loss = data[column_mapping["Loss"]].sum()
 
         return {
             "Accuracy": f"{accuracy * 100:.2f}%",
@@ -136,9 +144,7 @@ def analyze_financial_data(file):
 # Gradio Interface
 with gr.Blocks(theme=gr.themes.Monochrome()) as interface:
     gr.Markdown("# **AI Risk Analyst Agent**")
-    gr.Markdown(
-        "Analyze your financial risks and identify anomalies using advanced AI models. Upload financial data in CSV format to get started."
-    )
+    gr.Markdown("Analyze your financial risks and identify anomalies using AI models.")
     with gr.Row():
         with gr.Column():
             data_file = gr.File(label="Upload Financial Data (CSV)", file_types=[".csv"])