Create app.py

app.py

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+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from transformers import TextDataset, DataCollatorForLanguageModeling
+from transformers import Trainer, TrainingArguments
+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
+dataset = TextDataset(
+    tokenizer=tokenizer,
+    file_path="normans_wikipedia.txt",
+    block_size=512,  # Adjust this value based on your requirements
+)
+data_collator = DataCollatorForLanguageModeling(
+    tokenizer=tokenizer,
+    mlm=False
+)
+
+# Fine-tune the model
+training_args = TrainingArguments(
+    output_dir=output_dir,
+    overwrite_output_dir=True,
+    num_train_epochs=10,
+    per_device_train_batch_size=2,
+    save_steps=10_000,
+    save_total_limit=2,
+    logging_dir=output_dir,  # Add this line for logging
+    logging_steps=100,  # Adjust this value based on your requirements
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    data_collator=data_collator,
+    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,
+        num_beams=5,
+        no_repeat_ngram_size=2,
+        top_k=50,
+        top_p=0.90,
+        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",
+    outputs="text",
+    live=True
+)
+
+# Launch the Gradio interface
+iface.launch()