app.py

import streamlit as st

from PIL import Image
from tool_loader import ToolLoader
from app_user_desc import app_user_desc
from app_dev_desc import app_dev_desc
from logger import log_response
from logger import log_enabled
from app_chat import app_chat
import numpy as np
import re,sys,unicodedata

from app_agent_config import AgentConfig 

st.set_page_config(
    page_title="Custom Transformers can realy do anything...",
    page_icon="👋",
)
# Create an instance of AgentConfig
agent_config = AgentConfig()




st.title("Hugging Face Agent and Tools")

## LB https://huggingface.co/spaces/qiantong-xu/toolbench-leaderboard

st.markdown("Welcome to the Hugging Face Agent and Tools app! This app allows you to interact with various tools using the Hugging Face Inference API.  CustomTransformers can do anything \n🤪🤗😄🤗🤪.")

#######

import pandas as pd
from io import StringIO
with st.sidebar:

    st.header("Set Tools and Option. ")

    with st.expander("Configure the agent and activate tools"):

            agent_config.configure()

    with st.expander("Set Content and Context"):

            agent_config.context = st.text_area("Context")

            agent_config.image = st.camera_input("Take a picture")

            img_file_buffer = st.file_uploader('Upload a PNG image', type='png')
       
            if img_file_buffer is not None:
                image_raw = Image.open(img_file_buffer)
                #global image
                agent_config.image = np.array(image_raw)
                ######## 
                st.image(agent_config.image)
                
            uploaded_file = st.file_uploader("Choose a pdf", type='pdf')
            if uploaded_file is not None:
                # To read file as bytes:
                pdf_document = uploaded_file.getvalue() 
                agent_config.document = pdf_document
                st.write(pdf_document)
                
            uploaded_txt_file = st.file_uploader("Choose a txt", type='txt')
            if uploaded_txt_file is not None:
                # To read file as bytes:
                txt_document = uploaded_txt_file.getvalue()
                agent_config.document = txt_document
                st.write(txt_document)
                
            uploaded_csv_file = st.file_uploader("Choose a csv", type='csv')
            if uploaded_csv_file is not None:
                # To read file as bytes:
                csv_document = uploaded_csv_file.getvalue()
                agent_config.document = csv_document
                st.write(csv_document)
                            
            uploaded_csv_file = st.file_uploader("Choose audio", type='wav')
            if uploaded_csv_file is not None:
                # To read file as bytes:
                csv_document = uploaded_csv_file.getvalue()
                agent_config.document = csv_document
                st.write(csv_document)
                
            uploaded_csv_file = st.file_uploader("Choose video", type='avi')
            if uploaded_csv_file is not None:
                # To read file as bytes:
                csv_document = uploaded_csv_file.getvalue()
                agent_config.document = csv_document
                st.write(csv_document)
                        
                # To convert to a string based IO:
                #stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
                #st.write(stringio)
            
                # To read file as string:
                #string_data = stringio.read()
                #st.write(string_data)
            
                # Can be used wherever a "file-like" object is accepted:
                dataframe = pd.read_csv(uploaded_file)
                st.write(dataframe)

# Create a page with tabs
tabs = st.tabs(["Chat","User Description"])

with tabs[0]: 
    st.markdown("Start to chat. e.g. Generate an image of a boat. This will make the agent use the tool text2image to generate an image. Set content, context, Inference URL , tools and logging in the sidebar.")
 
with tabs[1]:
    app_user_desc()
       
app_chat(agent_config)