Update app.py

app.py

@@ -63,47 +63,49 @@ class GANRiskAnalyzer:
 # Risk Analysis
 def analyze_financial_data(file):
     try:
-        # Attempt to read the CSV file
-        data = pd.read_csv(file, encoding="utf-8", error_bad_lines=False)
+        # Read the uploaded CSV file
+        data = pd.read_csv(file.name, encoding="utf-8", error_bad_lines=False)
     except UnicodeDecodeError:
-        # Fallback for files with non-UTF-8 encodings
-        data = pd.read_csv(file, encoding="ISO-8859-1", error_bad_lines=False)
+        # Fallback for non-UTF-8 encoding
+        try:
+            data = pd.read_csv(file.name, encoding="ISO-8859-1", error_bad_lines=False)
+        except Exception as e:
+            return {"error": f"Failed to read file: {str(e)}"}
     except Exception as e:
-        return f"An error occurred while reading the file: {str(e)}"
+        return {"error": f"An unexpected error occurred: {str(e)}"}
 
     # Handle empty or malformed data
     if data.empty:
-        return "The uploaded file is empty or has an invalid structure."
+        return {"error": "The uploaded file is empty or has an invalid structure."}
 
-    # Check required columns
+    # Validate required columns
     required_columns = ["Revenue", "Profit", "Loss", "Expenses", "Risk_Level"]
     if not all(column in data.columns for column in required_columns):
-        return "The uploaded CSV must include these columns: Revenue, Profit, Loss, Expenses, Risk_Level."
+        return {"error": f"The CSV must include these columns: {', '.join(required_columns)}"}
 
-    # Data Preprocessing
     try:
+        # Data Preprocessing
         X = data[["Revenue", "Profit", "Loss", "Expenses"]].dropna()
         y = data["Risk_Level"].dropna()
 
-        # Check for empty rows after cleaning
         if X.empty or y.empty:
-            return "The data has missing values or invalid rows after cleaning. Please check the file."
-        
+            return {"error": "The data contains missing values or invalid rows after cleaning."}
+
         scaler = StandardScaler()
         X_scaled = scaler.fit_transform(X)
-        
+
         # Dimensionality Reduction
         pca = PCA(n_components=2)
         X_pca = pca.fit_transform(X_scaled)
-        
+
         # Train-Test Split
         X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)
-        
+
         # Gradient Boosting Classifier
         model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=5)
         model.fit(X_train, y_train)
         y_pred = model.predict(X_test)
-        
+
         accuracy = accuracy_score(y_test, y_pred)
         report = classification_report(y_test, y_pred, output_dict=True)
 
@@ -112,12 +114,12 @@ 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])
 
-        # Analysis Insights
+        # Insights
         total_revenue = data["Revenue"].sum()
         total_profit = data["Profit"].sum()
         total_loss = data["Loss"].sum()
 
-        insights = {
+        return {
             "Accuracy": f"{accuracy * 100:.2f}%",
             "Classification Report": report,
             "Generated Anomalies (GAN)": anomalies.tolist(),
@@ -128,23 +130,22 @@ def analyze_financial_data(file):
                 "Net Balance": f"${(total_revenue - total_loss):,.2f}"
             }
         }
-        return insights
     except Exception as e:
-        return f"An error occurred during analysis: {str(e)}"
+        return {"error": f"An error occurred during analysis: {str(e)}"}
 
+# 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."
     )
-    
     with gr.Row():
         with gr.Column():
-            data_file = gr.File(label="Upload Financial Data (CSV)")
+            data_file = gr.File(label="Upload Financial Data (CSV)", file_types=[".csv"])
             submit_button = gr.Button("Analyze")
         with gr.Column():
             output = gr.JSON(label="Risk Analysis Insights")
 
-    submit_button.click(analyze_financial_data, inputs=[data_file], outputs=output)
+    submit_button.click(analyze_financial_data, inputs=data_file, outputs=output)
 
 interface.launch()