Delete gradio_app.py

gradio_app.py

@@ -1,75 +0,0 @@
-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.utils.utilities import Utility
-from transformers import RobertaTokenizer, T5ForConditionalGeneration
-
-dataset_dir = "logs"
-headers = ["input", "output", "timestamp", "elapsed"]
-repo = huggingface_hub.Repository(
-    local_dir=dataset_dir, 
-    clone_from="https://huggingface.co/spaces/priyasaravana/CodeSummarization", 
-    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 evaluate(sentence):
-    tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base')
-    model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-base-multi-sum')
-
-    # Prepare the input text
-    input_text = code_snippet.strip()
-    input_ids = tokenizer.encode(input_text, return_tensors='pt')
-    # Generate a summary
-    generated_ids = model.generate(input_ids, max_length=20)
-    summary = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
-
-    return summary
-
-def predict(sentence):    
-    timestamp = datetime.datetime.now().isoformat()    
-    start_time = time.time()    
-    predictions = evaluate([sentence])    
-    elapsed_time = time.time() - start_time      
-    output = predictions
-    print(f"Sentence: {sentence} \nPrediction: {predictions}")    
-    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()