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| import torch | |
| import gradio as gr | |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| # Load the model and tokenizer | |
| def load_model(): | |
| model_name = "gpt2" | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_name) | |
| model = GPT2LMHeadModel.from_pretrained(model_name) | |
| return tokenizer, model | |
| # Function to generate response with instructions | |
| def generate_response(user_input, instructions="Be friendly and helpful, and ensure your response is accurate and relevant."): | |
| tokenizer, model = load_model() | |
| model.eval() | |
| # Add instructions at the beginning of the user input | |
| prompt = instructions + " " + user_input | |
| input_ids = tokenizer.encode(prompt, return_tensors="pt") | |
| with torch.no_grad(): | |
| output = model.generate( | |
| input_ids, | |
| max_length=100, | |
| pad_token_id=tokenizer.eos_token_id, | |
| no_repeat_ngram_size=2, # Avoid repeating phrases | |
| temperature=0.7, # Control randomness | |
| top_k=50, # Limit token selection | |
| top_p=0.9, # Nucleus sampling | |
| do_sample=True # Enable sampling | |
| ) | |
| response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True) | |
| return response | |
| # Gradio interface | |
| def chatbot_interface(): | |
| interface = gr.Interface( | |
| fn=generate_response, # Function to process the input | |
| inputs=[ | |
| gr.Textbox(label="Enter your message", placeholder="Ask a question or make a request."), | |
| gr.Textbox(label="Instruction for the bot", placeholder="For example: Be friendly and helpful, ensure accuracy.") | |
| ], # Two text boxes: one for input and one for instructions | |
| outputs="text", # Output type - text | |
| title="GPT-2 Chatbot with Accuracy and Relevance Instructions", # Application title | |
| description="This is a chatbot based on the GPT-2 model. You can provide instructions to adjust the style of the bot's responses, ensuring accuracy and relevance.", # Description | |
| theme="compact" # Interface theme | |
| ) | |
| interface.launch() | |
| # Run the interface | |
| if __name__ == "__main__": | |
| chatbot_interface() |