Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import json
+
+from datetime import datetime
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+from transformers import pipeline
+from uuid import uuid4
+
+#based on https://huggingface.co/spaces/Wauplin/space_to_dataset_saver/blob/main/app_json.py
+#data is saved at https://huggingface.co/datasets/MR17u/tweeteval-irony-mcc/tree/main
+
+JSON_DATASET_DIR = Path("json_dataset")
+JSON_DATASET_DIR.mkdir(parents=True, exist_ok=True)
+
+JSON_DATASET_PATH = JSON_DATASET_DIR / f"data-{uuid4()}.json"
+
+CLS_MODEL_NAME = "PierreEpron/tweeteval-irony-mcc"
+
+scheduler = CommitScheduler(
+    repo_id="tweeteval-irony-mcc",
+    repo_type="dataset",
+    folder_path=JSON_DATASET_DIR,
+    path_in_repo="data",
+)
+
+classifier = pipeline(model = CLS_MODEL_NAME, tokenizer = 'cardiffnlp/twitter-roberta-large-2022-154m')
+
+def save_json(entry: str, result) -> None:
+    with scheduler.lock:
+        with JSON_DATASET_PATH.open("a") as f:
+            result = json.loads(result.replace("'",'"'))[0]
+            json.dump({"entry": entry, "label": result['label'], "score": result['score'], "datetime": datetime.now().isoformat()}, f)
+            f.write("\n")
+
+def classif(text: str):
+    return classifier(text)
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        entry = gr.Textbox(label="Input")
+        result = gr.Textbox(label="Classification")
+    input_btn = gr.Button("Submit")
+    input_btn.click(fn=classif, inputs=entry, outputs=result).success(
+        fn=save_json,
+        inputs=[entry, result],
+        outputs=None
+        )
+
+demo.launch()