Spaces:
Runtime error
Runtime error
File size: 2,786 Bytes
7c6415e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# Select the models you want to offer for chat
MODELS = [
"gpt2",
"distilgpt2",
"openai-gpt",
"openai-gpt-2",
"openai-gpt3",
]
# Define the system prompt
SYSTEM_PROMPT = "You are a helpful assistant. Answer the user's questions as best as you can."
# Create a dictionary to store conversation history
conversation_history = {}
# Create a function to generate the chatbot response
def chatbot_response(input_text, model_name, system_prompt):
# Load the selected model and tokenizer
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the chatbot pipeline
chatbot_pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Prepare the input for the chatbot
inputs = tokenizer([SYSTEM_PROMPT + " " + input_text], return_tensors="pt")
# Generate a response from the chatbot
response = chatbot_pipe(inputs)
# Return the response
return response[0]['generated_text'].strip()
# Create a Gradio interface
interface = gr.Interface(
fn=chatbot_response,
inputs=[
gr.inputs.Textbox(label="User input"),
gr.inputs.Radio(choices=MODELS, label="Model"),
gr.inputs.Textbox(label="System prompt", value=SYSTEM_PROMPT),
],
outputs="text",
title="Large Language Model Chatbot",
description="Chat with a large language model from the HuggingFace Transformers library.",
)
# Initialize the conversation history
for model in MODELS:
conversation_history[model] = []
# Define a function to update the conversation history
def update_history(history, new_message):
history.append(new_message)
return history
# Define a function to display the conversation history
def display_history(history):
return "\n".join(history)
# Create a Gradio block to display the conversation history
history_block = gr.Block(
label="Conversation History",
elem_id="history",
visible=False,
)
# Update the conversation history when a new message is sent
def update_history_on_message(history, model, new_message):
history = update_history(history, new_message)
conversation_history[model] = history
return history
# Display the conversation history
def display_history_on_message(history):
return display_history(history)
# Define event handlers for the Gradio interface
interface.change(history_block.update, [conversation_history], queue=False)
interface.submit(update_history_on_message, [conversation_history], [conversation_history], queue=False)
history_block.change(display_history_on_message, [conversation_history], queue=False)
# Launch the Gradio interface
interface.launch() |