File size: 1,704 Bytes
fdfb0c4 bd2daac b066a4d bb77c30 b066a4d bb77c30 b066a4d bb77c30 b066a4d bd2daac b066a4d fdfb0c4 |
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 |
import streamlit as st
#from transformers import BertModel, BertTokenizer
from transformers import HfAgent, load_tool
# Load tools
controlnet_transformer = load_tool("huggingface-tools/text-to-image")
upscaler = load_tool("diffusers/latent-upscaler-tool")
tools = [controlnet_transformer, upscaler ]
# Define the model and tokenizer
#model = BertModel.from_pretrained('bert-base-uncased')
#tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Create the Streamlit app
st.title("Hugging Face Agent")
# Input field for the user's message
message_input = st.text_input("Enter your message:", "")
# Checkboxes for the tools to be used by the agent
tool_checkboxes = [st.checkbox(f"Use {tool}") for tool in tools]
# Submit button
submit_button = st.button("Submit")
# Define the callback function to handle the form submission
def handle_submission():
# Get the user's message and the selected tools
message = message_input
selected_tools = [tool for tool, checkbox in zip(tools, tool_checkboxes) if checkbox]
# Initialize the agent with the selected tools
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=tools)
# agent.config.tokenizer = tokenizer
agent.config.tools = selected_tools
# Process the user's message
# inputs = tokenizer.encode_plus(message, add_special_tokens=True, return_tensors="pt")
# outputs = agent(inputs['input_ids'], attention_mask=inputs['attention_mask'])
# Display the agent's response
response = agent.run(message)
st.text(f"{response:.4f}")
# Add the callback function to the Streamlit app
submit_button = st.button("Submit", on_click=handle_submission)
|