Update app.py

app.py

@@ -10,14 +10,14 @@ model = BertForSequenceClassification.from_pretrained('huawei-noah/TinyBERT_Gene
 # Function to process the CSV file and generate predictions
 def process_csv(file):
     try:
-        # Read the CSV file
-        df = pd.read_csv(file.name)  # Use file.name to get the file path
+        # Read the CSV file using Pandas
+        df = pd.read_csv(file.name)  # Ensure correct file path
         
-        # Ensure the CSV has a 'text' column
+        # Check for 'text' column
         if 'text' not in df.columns:
             return "Error: The CSV file must contain a 'text' column."
         
-        # Tokenize the input text
+        # Tokenize input text
         inputs = tokenizer(df['text'].tolist(), return_tensors='pt', padding=True, truncation=True)
         
         # Perform inference
@@ -27,18 +27,22 @@ def process_csv(file):
         # Get predicted classes
         _, predicted_classes = torch.max(outputs.logits, dim=1)
         
-        # Add predicted classes to the DataFrame
+        # Add predictions to DataFrame
         df['predicted_class'] = predicted_classes.numpy()
         
-        # Return the processed DataFrame as a CSV string
-        output_csv = df.to_csv(index=False)
-        
-        return output_csv
+        # Return processed DataFrame as CSV string
+        return df.to_csv(index=False)
     
+    except FileNotFoundError:
+        return "Error: The specified file was not found. Please check your upload."
+    except pd.errors.EmptyDataError:
+        return "Error: The uploaded file is empty."
+    except pd.errors.ParserError:
+        return "Error: There was an issue parsing the CSV file."
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"An unexpected error occurred: {str(e)}"
 
-# Create the Gradio interface
+# Create Gradio interface
 input_csv = gr.File(label="Upload CSV File")
 output_csv = gr.File(label="Download Processed CSV")
 
@@ -50,5 +54,5 @@ demo = gr.Interface(
     description="Upload a CSV file with a 'text' column, and the model will process the data and provide predictions."
 )
 
-# Launch the Gradio interface
+# Launch Gradio interface
 demo.launch()