Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from datasets import load_dataset
+import tempfile
+
+def combine_dataset_texts(dataset_name, split, text_column):
+    try:
+        # Load the dataset from Hugging Face Hub
+        dataset = load_dataset(dataset_name, split=split)
+        
+        # Verify the text column exists
+        if text_column not in dataset.column_names:
+            raise gr.Error(f"Column '{text_column}' not found in dataset")
+        
+        # Combine all texts
+        combined_text = "\n\n".join([example[text_column] for example in dataset])
+        
+        # Create a temporary file
+        with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f:
+            f.write(combined_text)
+            return f.name
+            
+    except Exception as e:
+        raise gr.Error(f"Error processing dataset: {str(e)}")
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Hugging Face Dataset Text Combiner")
+    gr.Markdown("Combine all text files from a Hugging Face dataset into a single file")
+    
+    with gr.Row():
+        dataset_input = gr.Textbox(label="Dataset Name", 
+                                 placeholder="username/dataset-name")
+        split_input = gr.Textbox(label="Split", value="train")
+        column_input = gr.Textbox(label="Text Column", value="text")
+    
+    submit_btn = gr.Button("Combine Texts")
+    
+    with gr.Row():
+        output_file = gr.File(label="Combined Text File")
+        error_out = gr.Textbox(label="Error Output", visible=False)
+
+    submit_btn.click(
+        fn=combine_dataset_texts,
+        inputs=[dataset_input, split_input, column_input],
+        outputs=output_file,
+        api_name="combine_texts"
+    )
+
+if __name__ == "__main__":
+    demo.launch()