Upload gradio_app.py

gradio_app.py

@@ -0,0 +1,75 @@
+import os
+import time
+import csv
+import datetime
+import gradio
+import schedule
+from gradio import utils
+import huggingface_hub
+from pathlib import Path
+from src.models.bert import BERTClassifier
+from src.utils.utilities import Utility
+
+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", "timestamp", "elapsed"]
+
+
+repo = huggingface_hub.Repository(
+    local_dir=dataset_dir, 
+    clone_from="https://huggingface.co/datasets/jeevavijay10/senti-pred-gradio", 
+    token=hf_token,
+)
+repo.git_pull(lfs=True)
+
+def log_record(vals):    
+    log_file = Path(dataset_dir) / "data.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(vals))
+    schedule.run_pending()
+    print(f"Last Sync: {job.last_run}")   
+
+def predict(sentence):
+    
+    timestamp = datetime.datetime.now().isoformat()    
+    start_time = time.time()    
+    predictions = model.evaluate([sentence])    
+    elapsed_time = time.time() - start_time    
+    
+    output = classes[predictions[0]]
+    
+    print(f"Sentence: {sentence} \nPrediction: {predictions[0]} - {output}")    
+    log_record([sentence, output, timestamp, str(elapsed_time)])
+    
+    return output
+
+
+def sync_logs():
+    print(f"Repo Clean: {repo.is_repo_clean()}")
+    if not repo.is_repo_clean():
+        repo.git_add()
+        repo.git_commit()        
+        repo.git_pull(lfs=True)
+        result = repo.git_push()
+        # result = repo.push_to_hub()
+        print(result)
+    
+job = schedule.every(5).minutes.do(sync_logs)
+print("Scheduler engaged")
+
+gradio.Interface(
+    fn=predict,
+    inputs="text",
+    outputs="text",
+    allow_flagging='never'
+).launch()