Update app.py

app.py

@@ -1,4 +1,34 @@
 import gradio
+import os
+import time
+import csv
+import datetime
+from transformers import RobertaTokenizer, T5ForConditionalGeneration
+
+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
+    
 gradio.Interface(
     fn=predict,
     inputs="text",