Update app.py

app.py

@@ -5,7 +5,6 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Trainer, Training
 from transformers import pipeline
 from sklearn.model_selection import train_test_split
 
-Load and preprocess the dataset
 def load_and_preprocess_data():
     dataset = load_dataset('tahiryaqoob/BISELahore')
     train_dataset, val_dataset = train_test_split(dataset['train'], test_size=0.2, random_state=42)
@@ -14,14 +13,13 @@ def load_and_preprocess_data():
     print(f"Validation samples: {len(val_dataset)}")
     return train_dataset, val_dataset
 
-Preprocess the data to format for fine-tuning
+#Preprocess the data to format for fine-tunin
 def preprocess_function(examples, tokenizer):
     inputs = tokenizer(examples['question'], padding="max_length", truncation=True, max_length=128)
     targets = tokenizer(examples['answer'], padding="max_length", truncation=True, max_length=128)
     inputs['labels'] = targets['input_ids']
     return inputs
-
-Fine-tune the model using the preprocessed data
+    
 def fine_tune_model(train_dataset, val_dataset):
     model_name = "distilbert-base-uncased"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -57,7 +55,7 @@ def fine_tune_model(train_dataset, val_dataset):
     tokenizer.save_pretrained("./distilbert_finetuned")
     print("Model fine-tuned and saved successfully.")
 
-Create a chatbot inference pipeline using the fine-tuned model
+#Create a chatbot inference pipeline using the fine-tuned model
 def chatbot_inference():
     model_name = "./distilbert_finetuned"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -66,14 +64,14 @@ def chatbot_inference():
     chatbot = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
     return chatbot
 
-Run inference to test chatbot functionality
+#Run inference to test chatbot functionality
 def run_inference():
     chatbot = chatbot_inference()
     user_input = input("Ask a question: ")
     response = chatbot(user_input)
     print("Bot Response:", response[0]['generated_text'])
 
-Main function to train or serve the chatbot
+#Main function to train or serve the chatbot
 def main():
     train_dataset, val_dataset = load_and_preprocess_data()