app_agent_config.py

# app_agent_config.py
import streamlit as st
from tool_loader import ToolLoader
from tool_config import tool_names
from logger import log_enabled

from PIL import Image 
import numpy as np

class AgentConfig:
    def __init__(self):
        self.tool_checkboxes = []
        self.url_endpoint = ""
        self.image = []
        self.document = ""
        self.log_enabled = False
        self.context = ""
        self.tool_loader = ToolLoader(tool_names)

    def configure(self):
        st.markdown("Change the agent's configuration here.")
        self.url_endpoint = st.selectbox("Select Inference URL", [
            "https://api-inference.huggingface.co/models/bigcode/starcoder",
            "https://api-inference.huggingface.co/models/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
            "https://api-inference.huggingface.co/models/gpt2"
        ])
        tool_loader = ToolLoader(tool_names)
        self.log_enabled = st.checkbox("Enable Logging")
        self.tool_checkboxes = [st.checkbox(f"{tool.name} --- {tool.description} ") for tool in tool_loader.tools]

    def content_and_context(self):
        self.context = st.text_area("Context")

        self.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
            self.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() 
            self.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()
            self.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()
            self.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()
            self.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()
            self.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)