monitory initial commit

gradio_app.py

@@ -1,5 +1,9 @@
+import os
+import csv
 import gradio
-
+from gradio import utils
+import huggingface_hub
+from pathlib import Path
 from src.models.bert import BERTClassifier
 from src.utils.utilities import Utility
 
@@ -7,11 +11,59 @@ model = BERTClassifier(model_name='jeevavijay10/nlp-goemotions-bert')
 
 classes = Utility().read_emotion_list()
 
+hf_token = os.getenv("HF_TOKEN")
+
+dataset_dir = "logs"
+
+headers = ["input", "output"]
+
+path_to_dataset_repo = huggingface_hub.create_repo(
+    name="jeevavijay10/senti-pred-gradio",
+    token=hf_token,
+    private=False,
+    repo_type="dataset",
+    exist_ok=True,
+)
+
+repo = huggingface_hub.Repository(
+    local_dir=dataset_dir, clone_from=path_to_dataset_repo, use_auth_token=hf_token
+)
+
+def log_record(input, output):
+    repo.git_pull(lfs=True)
+    
+    log_file = Path(dataset_dir) / "log.csv"
+    
+    is_new = not Path(log_file).exists()    
+    
+    with open(log_file, "a", newline="", encoding="utf-8") as csvfile:
+        writer = csv.writer(csvfile)
+
+    if is_new:
+        writer.writerow(utils.sanitize_list_for_csv(headers))
+
+    writer.writerow(utils.sanitize_list_for_csv([input, output]))
+    
+    with open(log_file, "r", encoding="utf-8") as csvfile:
+        line_count = len([None for _ in csv.reader(csvfile)]) - 1
+    
+    repo.push_to_hub(commit_message=f"Flagged sample #{line_count}")
+    
+
 def predict(sentence):
+    
     print(sentence)
+    
     predictions = model.evaluate([sentence])
+    
     print(f"Predictions: {predictions}")
-    return classes[predictions[0]]
+    
+    output = classes[predictions[0]]
+    
+    log_record(sentence, output)
+    
+    return output
+
 
 gradio.Interface(
     fn=predict,