Update app.py

app.py

@@ -10,17 +10,26 @@ from huggingface_hub import HfApi, HfFolder
 # CONFIG
 MODEL_NAME = "distilbert-base-uncased-finetuned-sst-2-english"  # Change if needed
 HF_DATASET_REPO = "your-username/your-logging-dataset"  # Must be created beforehand
-
 # Token from environment in Spaces
 HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
-
 # Load model + tokenizer
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
-
 # Log entries
 log_entries = []
 
+def setup_hf_dataset():
+    global DATASET_CREATED
+    if not DATASET_CREATED and HF_TOKEN:
+        try:
+            api = HfApi()
+            create_repo(DATASET_NAME, repo_type="dataset", token=HF_TOKEN, exist_ok=True)
+            DATASET_CREATED = True
+            print(f"Dataset {DATASET_NAME} is ready")
+        except Exception as e: print(f"Error setting up dataset: {e}")
+    elif not HF_TOKEN:
+        print("Warning: HF_TOKEN not set. Data will be stored locally only.")
+
 def infer_and_log(text_input):
     inputs = tokenizer(text_input, return_tensors="pt", truncation=True)
     with torch.no_grad():
@@ -55,22 +64,18 @@ def save_to_hf():
     return f"Pushed {len(df)} logs to {HF_DATASET_REPO}!"
 
 with gr.Blocks() as demo:
-    gr.Markdown("## 🤖 Text Classification with Logging")
+    gr.Markdown("## AI-generated text detector")
 
     with gr.Row():
-        input_box = gr.Textbox(label="Input Text", lines=4, interactive=True)
-        output_box = gr.Textbox(label="Predicted Label", lines=2)
+        input_box = gr.Textbox(label="Input Text", lines=6, interactive=True)
+        output_box = gr.Textbox(label="Predicted Label", lines=6)
 
     with gr.Row():
         submit_btn = gr.Button("Submit")
         clear_btn = gr.Button("Clear")
 
-    status_box = gr.Textbox(label="Status", interactive=False)
-
     submit_btn.click(fn=infer_and_log, inputs=input_box, outputs=output_box)
     clear_btn.click(fn=clear_fields, outputs=[input_box, output_box])
-    
-    gr.Button("Save Logs to HF Dataset").click(fn=save_to_hf, outputs=status_box)
 
 if __name__ == "__main__":
     demo.launch()