Create app.py

app.py

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+import gradio as gr
+import datetime
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from datasets import load_dataset, Dataset, DatasetDict
+import huggingface_hub
+import pandas as pd
+
+# CONFIG
+MODEL_NAME = "distilbert-base-uncased-finetuned-sst-2-english"  # replace with your own
+HF_DATASET_REPO = "your-username/your-logging-dataset"  # create on HF Hub
+HF_TOKEN = "hf_..."  # your Hugging Face token with write access
+
+# Load model + tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+# Setup dataset pushing
+huggingface_hub.login(token=HF_TOKEN)
+
+# Store session logs
+log_entries = []
+
+def infer_and_log(text_input):
+    inputs = tokenizer(text_input, return_tensors="pt", truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits.tolist()
+        predicted = torch.argmax(outputs.logits, dim=-1).item()
+        output_label = model.config.id2label[predicted]
+
+    # Create log entry
+    log_entries.append({
+        "timestamp": datetime.datetime.now().isoformat(),
+        "input": text_input,
+        "logits": logits,
+    })
+
+    return output_label
+
+def clear_fields():
+    return "", ""
+
+def save_to_hf():
+    if not log_entries:
+        return "Nothing to save."
+    
+    dataset = Dataset.from_pandas(pd.DataFrame(log_entries))
+    dataset.push_to_hub(HF_DATASET_REPO)
+    log_entries.clear()
+    return "Data saved to Hugging Face!"
+
+with gr.Blocks() as demo:
+    gr.Markdown("### 🔤 Text Classification Demo")
+
+    with gr.Row():
+        input_box = gr.Textbox(label="Input Text", lines=5, interactive=True)
+        output_box = gr.Textbox(label="Predicted Label", lines=5)
+
+    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 Hub").click(fn=save_to_hf, outputs=status_box)
+
+if __name__ == "__main__":
+    demo.launch()