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import gradio as gr
import os
os.system('pip install transformers torch')
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load the pre-trained model and tokenizer
model_name = "microsoft/DialoGPT-small"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
# Initial system prompt and chat history
system_prompt = "You are a helpful assistant."
chat_history = system_prompt
def generate_response(prompt, max_length=50, temperature=0.8):
global chat_history
input_text = chat_history + " User: " + prompt
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output_ids = model.generate(input_ids, max_length=max_length, temperature=temperature, num_return_sequences=1)
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
# Update chat history
chat_history += f" User: {prompt} Assistant: {response}"
return response
iface = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(),
outputs="text",
)
iface.launch() |