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https://github.com/OpenBMB/MiniCPM-V.git
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b89f8354f9
* Implement chatbot functionality using Streamlit This commit adds the implementation of a chatbot using Streamlit, a Python library for building interactive web applications. The chatbot allows users to interact with an AI assistant, asking questions and receiving responses in real-time. Features include: - Integration with the MiniCPM-V-2.0 model for generating responses. - User-friendly interface with text input for questions and options for uploading images. - Sidebar settings for adjusting parameters such as max_length, top_p, and temperature. - Ability to clear chat history to start a new conversation. The chat history and session state are managed using Streamlit's session_state functionality, ensuring a seamless user experience across interactions. This implementation provides a simple and intuitive way for users to engage with the chatbot, making it accessible for various use cases. * update MiniCPM-Llama3-V-2_5 streamlit demo * Update web_demo_streamlit-2_5.py This update, based on the May 25, 2024 version of modeling_minicpmv.py, includes the following enhancements: 1. Introduction of repetition_penalty and top_k parameters to the st.sidebar, enabling users to adjust these model parameters dynamically. 2. Default support for stream=True in the model.chat method to facilitate real-time streaming responses.
100 lines
3.9 KiB
Python
100 lines
3.9 KiB
Python
import streamlit as st
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from PIL import Image
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import torch
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from transformers import AutoModel, AutoTokenizer
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# Model path
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model_path = "openbmb/MiniCPM-V-2"
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# User and assistant names
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U_NAME = "User"
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A_NAME = "Assistant"
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# Set page configuration
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st.set_page_config(
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page_title="Minicpm-V-2 Streamlit",
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page_icon=":robot:",
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layout="wide"
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)
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# Load model and tokenizer
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@st.cache_resource
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def load_model_and_tokenizer():
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print(f"load_model_and_tokenizer from {model_path}")
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model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16).to(
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device="cuda:0", dtype=torch.bfloat16)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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return model, tokenizer
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# Initialize session state
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if 'model' not in st.session_state:
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st.session_state.model, st.session_state.tokenizer = load_model_and_tokenizer()
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print("model and tokenizer had loaded completed!")
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# Initialize session state
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if 'chat_history' not in st.session_state:
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st.session_state.chat_history = []
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# Sidebar settings
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sidebar_name = st.sidebar.title("Minicpm-V-2 Streamlit")
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max_length = st.sidebar.slider("max_length", 0, 4096, 2048, step=2)
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top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
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temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.7, step=0.01)
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# Clear chat history button
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buttonClean = st.sidebar.button("Clear chat history", key="clean")
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if buttonClean:
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st.session_state.chat_history = []
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st.session_state.response = ""
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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st.rerun()
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# Display chat history
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for i, message in enumerate(st.session_state.chat_history):
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if message["role"] == "user":
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with st.chat_message(name="user", avatar="user"):
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if message["image"] is not None:
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st.image(message["image"], caption='User uploaded image', width=468, use_column_width=False)
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continue
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elif message["content"] is not None:
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st.markdown(message["content"])
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else:
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with st.chat_message(name="model", avatar="assistant"):
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st.markdown(message["content"])
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# Select mode
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selected_mode = st.sidebar.selectbox("Select mode", ["Text", "Image"])
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if selected_mode == "Image":
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# Image mode
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uploaded_image = st.sidebar.file_uploader("Upload image", key=1, type=["jpg", "jpeg", "png"], accept_multiple_files=False)
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if uploaded_image is not None:
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st.image(uploaded_image, caption='User uploaded image', width=468, use_column_width=False)
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# Add uploaded image to chat history
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st.session_state.chat_history.append({"role": "user", "content": None, "image": uploaded_image})
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# User input box
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user_text = st.chat_input("Enter your question")
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if user_text:
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with st.chat_message(U_NAME, avatar="user"):
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st.session_state.chat_history.append({"role": "user", "content": user_text, "image": None})
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st.markdown(f"{U_NAME}: {user_text}")
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# Generate reply using the model
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model = st.session_state.model
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tokenizer = st.session_state.tokenizer
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with st.chat_message(A_NAME, avatar="assistant"):
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# If the previous message contains an image, pass the image to the model
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if len(st.session_state.chat_history) > 1 and st.session_state.chat_history[-2]["image"] is not None:
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uploaded_image = st.session_state.chat_history[-2]["image"]
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imagefile = Image.open(uploaded_image).convert('RGB')
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msgs = [{"role": "user", "content": user_text}]
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res, context, _ = model.chat(image=imagefile, msgs=msgs, context=None, tokenizer=tokenizer,
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sampling=True,top_p=top_p,temperature=temperature)
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st.markdown(f"{A_NAME}: {res}")
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st.session_state.chat_history.append({"role": "model", "content": res, "image": None})
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st.divider()
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