Update app.py

app.py

@@ -5,39 +5,35 @@ import torch
 import os
 import gradio as gr
 
-# Load pre-trained GPT-2 model and tokenizer
+
+
 model_name = "gpt2"
 model = GPT2LMHeadModel.from_pretrained(model_name)
 tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 
-# Load your preprocessed data
+
 with open("normans_wikipedia.txt", "r", encoding="utf-8") as file:
     data = file.read()
 
-# Specify the output directory for fine-tuned model
 output_dir = "./normans_fine-tuned"
 os.makedirs(output_dir, exist_ok=True)
 
-# Tokenize and encode the data
-input_ids = tokenizer.encode(data, return_tensors="pt")
 
-# Create a dataset and data collator
+input_ids = tokenizer.encode(data, return_tensors="pt")
 dataset = TextDataset(
     tokenizer=tokenizer,
     file_path="normans_wikipedia.txt",
-    block_size=512,  # Adjust this value based on your requirements
+    block_size=512,  
 )
 data_collator = DataCollatorForLanguageModeling(
     tokenizer=tokenizer,
     mlm=False
 )
 
-# Fine-tune the model
-# Fine-tune the model
 training_args = TrainingArguments(
     output_dir=output_dir,
     overwrite_output_dir=True,
-    num_train_epochs=10,
+    num_train_epochs=20,
     per_device_train_batch_size=2,
     save_steps=10_000,
     save_total_limit=2,
@@ -54,25 +50,21 @@ trainer = Trainer(
     train_dataset=dataset,
 )
 
-# Training loop
+
 try:
     trainer.train()
 except KeyboardInterrupt:
     print("Training interrupted by user.")
 
-# Save the fine-tuned model
+
 model.save_pretrained(output_dir)
 tokenizer.save_pretrained(output_dir)
-
-# Load the fine-tuned model
 fine_tuned_model = GPT2LMHeadModel.from_pretrained(output_dir)
 
-# Function to generate responses from the fine-tuned model
+
 def generate_response(user_input):
-    # Tokenize and encode user input
     user_input_ids = tokenizer.encode(user_input, return_tensors="pt")
 
-    # Generate response from the fine-tuned model
     generated_output = fine_tuned_model.generate(
         user_input_ids,
         max_length=100,
@@ -83,12 +75,11 @@ def generate_response(user_input):
         temperature=0.9
     )
 
-    # Decode and return the generated response
     chatbot_response = tokenizer.decode(
         generated_output[0], skip_special_tokens=True)
     return "Chatbot: " + chatbot_response
 
-# Create a Gradio interface
+
 iface = gr.Interface(
     fn=generate_response,
     inputs="text",
@@ -96,5 +87,4 @@ iface = gr.Interface(
     live=True
 )
 
-# Launch the Gradio interface
 iface.launch()