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| import os | |
| import streamlit as st | |
| import re | |
| import time | |
| from PIL import Image | |
| import ast | |
| import numpy as np | |
| def reset_conversation(): | |
| st.session_state.messages = [] | |
| def display_mask_image(image_path): | |
| if os.path.isfile(image_path): | |
| image = Image.open(image_path) | |
| st.image(image, caption='Final Mask', use_column_width=True) | |
| def tyre_synap_bot(filter_agent,image_file_path): | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| print("Found image file path: ",image_file_path) | |
| # Display chat messages from history on app rerun | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # React to user input | |
| if prompt := st.chat_input("What is up?"): | |
| # Display user message in chat message container | |
| st.chat_message("user").markdown(prompt) | |
| # Add user message to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| ai_response = filter_agent.invoke( | |
| { | |
| "input": f'{prompt}, provided image path: {image_file_path}' | |
| } | |
| ) | |
| # ai_response = filter_agent.run(f'{prompt} provided image path :{image_file_path}') | |
| response = f"Echo: {ai_response['output']}" | |
| with st.chat_message("assistant"): | |
| message_placeholder = st.empty() | |
| full_response = "" | |
| if 'mask' in ai_response['output'] or 'predicted_image' in ai_response['output']: | |
| display_mask_image('final_mask.png') | |
| for chunk in re.split(r'(\s+)', response): | |
| full_response += chunk + " " | |
| time.sleep(0.01) | |
| # Add a blinking cursor to simulate typing | |
| message_placeholder.markdown(full_response + "▌") | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": full_response}) | |
| st.button('Reset Chat', on_click=reset_conversation) | |