Update app.py

app.py

@@ -1,80 +1,19 @@
 import streamlit as st
-import os
-import base64
-import io
-from PIL import Image
-from pydub import AudioSegment
-import IPython
-import soundfile as sf
-import requests
-import pandas as pd  # If you're working with DataFrames
-import matplotlib.figure  # If you're using matplotlib figures
-import numpy as np
 
-from custom_agent import CustomHfAgent 
 from tool_loader import ToolLoader
-from tool_config import tool_names   
-from app_description import show_app_description
+from app_chat import app_chat
+from app_user_desc import app_user_desc
+from app_dev_desc import app_dev_desc
+from app_agent_config import app_agent_config
 from logger import log_response
 
-# For Altair charts
-import altair as alt
-
-# For Bokeh charts
-from bokeh.models import Plot
-
-# For Plotly charts
-import plotly.express as px
-
-# For Pydeck charts
-import pydeck as pdk
-
-
-import logging
-import streamlit as st
-from transformers import load_tool, Agent
-from tool_loader import ToolLoader
-
-# Configure the logging settings for transformers
-transformers_logger = logging.getLogger("transformers.file_utils")
-transformers_logger.setLevel(logging.INFO)  # Set the desired logging level
-  
-
-import time  
-import torch
-
-
-def handle_submission(user_message, selected_tools, url_endpoint):
-    
-    log_response("User input \n {}".format(user_message))
-    log_response("selected_tools \n {}".format(selected_tools))
-    log_response("url_endpoint \n {}".format(url_endpoint))
-    
-    agent = CustomHfAgent(
-        url_endpoint=url_endpoint,
-        token=os.environ['HF_token'],
-        additional_tools=selected_tools,
-        input_params={"max_new_tokens": 192},
-    )
-
-    response = agent.run(user_message)
-
-    log_response("Agent Response\n {}".format(response))
-
-    return response
+#from transformers import load_tool, Agent 
 
 # Declare global variable
 global log_enabled
 log_enabled = False
 
-
-
-    
-
-
-# Create tool loader instance
-tool_loader = ToolLoader(tool_names)
-
+ 
 st.title("Hugging Face Agent and tools")
 
 ## LB https://huggingface.co/spaces/qiantong-xu/toolbench-leaderboard
@@ -92,83 +31,18 @@ with tabs[0]:
     # Examples for the user perspective
     st.markdown("Stat to chat. e.g. Generate an image of a boat. This will make the agent use the tool text2image to generate an image.")
  
-        
 # Tab 2: URL and Tools
 with tabs[1]:
     #
-    app_config()
+    app_agent_config()
 
 # Tab 3: User Description
 with tabs[2]:
     #
-    app_user_description()
-    
-
+    app_user_desc()
     
 # Tab 4: Developers
 with tabs[3]:
     app_dev_desc()     
    
-    
-    
-# Chat code (user input, agent responses, etc.)
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-with st.chat_message("assistant"):
-    st.markdown("Hello there! How can I assist you today?")
-
-if user_message := st.chat_input("Enter message"):
-    st.chat_message("user").markdown(user_message)
-    st.session_state.messages.append({"role": "user", "content": user_message})
-
-    selected_tools = [tool_loader.tools[idx] for idx, checkbox in enumerate(tool_checkboxes) if checkbox]
-    # Handle submission with the selected inference URL
-    response = handle_submission(user_message, selected_tools, url_endpoint)
-
-    with st.chat_message("assistant"):
-        if response is None:
-            st.warning("The agent's response is None. Please try again. Generate an image of a flying horse.")
-        elif isinstance(response, Image.Image):
-            st.image(response)
-        elif isinstance(response, AudioSegment):
-            st.audio(response)
-        elif isinstance(response, int):
-            st.markdown(response)
-        elif isinstance(response, str):
-            if "emojified_text" in response:
-                st.markdown(f"{response['emojified_text']}")
-            else:
-                st.markdown(response)
-        elif isinstance(response, list):
-            for item in response:
-                st.markdown(item)  # Assuming the list contains strings
-        elif isinstance(response, pd.DataFrame):
-            st.dataframe(response)
-        elif isinstance(response, pd.Series):
-            st.table(response.iloc[0:10])
-        elif isinstance(response, dict):
-            st.json(response)
-        elif isinstance(response, st.graphics_altair.AltairChart):
-            st.altair_chart(response)
-        elif isinstance(response, st.graphics_bokeh.BokehChart):
-            st.bokeh_chart(response)
-        elif isinstance(response, st.graphics_graphviz.GraphvizChart):
-            st.graphviz_chart(response)
-        elif isinstance(response, st.graphics_plotly.PlotlyChart):
-            st.plotly_chart(response)
-        elif isinstance(response, st.graphics_pydeck.PydeckChart):
-            st.pydeck_chart(response)
-        elif isinstance(response, matplotlib.figure.Figure):
-            st.pyplot(response)
-        elif isinstance(response, streamlit.graphics_vega_lite.VegaLiteChart):
-            st.vega_lite_chart(response)
-        else:
-            st.warning("Unrecognized response type. Please try again. e.g. Generate an image of a flying horse.")
-
-    st.session_state.messages.append({"role": "assistant", "content": response})
-
+app_chat()