Implement chatbot functionality using Streamlit (OpenBMB#61)

* 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.

web_demo_streamlit-2_5.py

@@ -0,0 +1,108 @@
+import streamlit as st
+from PIL import Image
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+# Model path
+model_path = "openbmb/MiniCPM-Llama3-V-2_5"
+
+# User and assistant names
+U_NAME = "User"
+A_NAME = "Assistant"
+
+# Set page configuration
+st.set_page_config(
+    page_title="MiniCPM-Llama3-V-2_5 Streamlit",
+    page_icon=":robot:",
+    layout="wide"
+)
+
+
+# Load model and tokenizer
+@st.cache_resource
+def load_model_and_tokenizer():
+    print(f"load_model_and_tokenizer from {model_path}")
+    model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.float16).to(device="cuda")
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    return model, tokenizer
+
+
+# Initialize session state
+if 'model' not in st.session_state:
+    st.session_state.model, st.session_state.tokenizer = load_model_and_tokenizer()
+    st.session_state.model.eval()
+    print("model and tokenizer had loaded completed!")
+
+# Initialize session state
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+
+# Sidebar settings
+sidebar_name = st.sidebar.title("MiniCPM-Llama3-V-2_5 Streamlit")
+max_length = st.sidebar.slider("max_length", 0, 4096, 2048, step=2)
+repetition_penalty = st.sidebar.slider("repetition_penalty", 0.0, 2.0, 1.05, step=0.01)
+top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
+top_k = st.sidebar.slider("top_k", 0, 100, 100, step=1)
+temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.7, step=0.01)
+
+# Clear chat history button
+buttonClean = st.sidebar.button("Clear chat history", key="clean")
+if buttonClean:
+    st.session_state.chat_history = []
+    st.session_state.response = ""
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    st.rerun()
+
+# Display chat history
+for i, message in enumerate(st.session_state.chat_history):
+    if message["role"] == "user":
+        with st.chat_message(name="user", avatar="user"):
+            if message["image"] is not None:
+                st.image(message["image"], caption='User uploaded image', width=448, use_column_width=False)
+                continue
+            elif message["content"] is not None:
+                st.markdown(message["content"])
+    else:
+        with st.chat_message(name="model", avatar="assistant"):
+            st.markdown(message["content"])
+
+# Select mode
+selected_mode = st.sidebar.selectbox("Select mode", ["Text", "Image"])
+if selected_mode == "Image":
+    # Image mode
+    uploaded_image = st.sidebar.file_uploader("Upload image", key=1, type=["jpg", "jpeg", "png"],
+                                              accept_multiple_files=False)
+    if uploaded_image is not None:
+        st.image(uploaded_image, caption='User uploaded image', width=468, use_column_width=False)
+        # Add uploaded image to chat history
+        st.session_state.chat_history.append({"role": "user", "content": None, "image": uploaded_image})
+
+# User input box
+user_text = st.chat_input("Enter your question")
+if user_text:
+    with st.chat_message(U_NAME, avatar="user"):
+        st.session_state.chat_history.append({"role": "user", "content": user_text, "image": None})
+        st.markdown(f"{U_NAME}: {user_text}")
+
+    # Generate reply using the model
+    model = st.session_state.model
+    tokenizer = st.session_state.tokenizer
+
+    with st.chat_message(A_NAME, avatar="assistant"):
+        # If the previous message contains an image, pass the image to the model
+        if len(st.session_state.chat_history) > 1 and st.session_state.chat_history[-2]["image"] is not None:
+            uploaded_image = st.session_state.chat_history[-2]["image"]
+            imagefile = Image.open(uploaded_image).convert('RGB')
+
+        msgs = [{"role": "user", "content": user_text}]
+        res = model.chat(image=imagefile, msgs=msgs, context=None, tokenizer=tokenizer,
+                         sampling=True, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty,
+                         temperature=temperature, stream=True)
+
+        # Collect the generated_text str
+        generated_text = st.write_stream(res)
+
+        st.session_state.chat_history.append({"role": "model", "content": generated_text, "image": None})
+
+    st.divider()

web_demo_streamlit.py

@@ -0,0 +1,99 @@
+import streamlit as st
+from PIL import Image
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+# Model path
+model_path = "openbmb/MiniCPM-V-2"
+
+# User and assistant names
+U_NAME = "User"
+A_NAME = "Assistant"
+
+# Set page configuration
+st.set_page_config(
+    page_title="Minicpm-V-2 Streamlit",
+    page_icon=":robot:",
+    layout="wide"
+)
+
+# Load model and tokenizer
+@st.cache_resource
+def load_model_and_tokenizer():
+    print(f"load_model_and_tokenizer from {model_path}")
+    model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16).to(
+        device="cuda:0", dtype=torch.bfloat16)
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    return model, tokenizer
+
+# Initialize session state
+if 'model' not in st.session_state:
+    st.session_state.model, st.session_state.tokenizer = load_model_and_tokenizer()
+    print("model and tokenizer had loaded completed!")
+
+# Initialize session state
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+
+# Sidebar settings
+sidebar_name = st.sidebar.title("Minicpm-V-2 Streamlit")
+max_length = st.sidebar.slider("max_length", 0, 4096, 2048, step=2)
+top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
+temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.7, step=0.01)
+
+# Clear chat history button
+buttonClean = st.sidebar.button("Clear chat history", key="clean")
+if buttonClean:
+    st.session_state.chat_history = []
+    st.session_state.response = ""
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    st.rerun()
+
+# Display chat history
+for i, message in enumerate(st.session_state.chat_history):
+    if message["role"] == "user":
+        with st.chat_message(name="user", avatar="user"):
+            if message["image"] is not None:
+                st.image(message["image"], caption='User uploaded image', width=468, use_column_width=False)
+                continue
+            elif message["content"] is not None:
+                st.markdown(message["content"])
+    else:
+        with st.chat_message(name="model", avatar="assistant"):
+            st.markdown(message["content"])
+
+# Select mode
+selected_mode = st.sidebar.selectbox("Select mode", ["Text", "Image"])
+if selected_mode == "Image":
+    # Image mode
+    uploaded_image = st.sidebar.file_uploader("Upload image", key=1, type=["jpg", "jpeg", "png"], accept_multiple_files=False)
+    if uploaded_image is not None:
+        st.image(uploaded_image, caption='User uploaded image', width=468, use_column_width=False)
+        # Add uploaded image to chat history
+        st.session_state.chat_history.append({"role": "user", "content": None, "image": uploaded_image})
+
+# User input box
+user_text = st.chat_input("Enter your question")
+if user_text:
+    with st.chat_message(U_NAME, avatar="user"):
+        st.session_state.chat_history.append({"role": "user", "content": user_text, "image": None})
+        st.markdown(f"{U_NAME}: {user_text}")
+
+    # Generate reply using the model
+    model = st.session_state.model
+    tokenizer = st.session_state.tokenizer
+
+    with st.chat_message(A_NAME, avatar="assistant"):
+        # If the previous message contains an image, pass the image to the model
+        if len(st.session_state.chat_history) > 1 and st.session_state.chat_history[-2]["image"] is not None:
+            uploaded_image = st.session_state.chat_history[-2]["image"]
+            imagefile = Image.open(uploaded_image).convert('RGB')
+
+        msgs = [{"role": "user", "content": user_text}]
+        res, context, _ = model.chat(image=imagefile, msgs=msgs, context=None, tokenizer=tokenizer,
+                                     sampling=True,top_p=top_p,temperature=temperature)
+        st.markdown(f"{A_NAME}: {res}")
+        st.session_state.chat_history.append({"role": "model", "content": res, "image": None})
+
+    st.divider()