add: a basic streamlit app impl for gui

app.py

@@ -0,0 +1,87 @@
+import streamlit as st
+import weave
+from dotenv import load_dotenv
+
+from medrag_multi_modal.assistant import (
+    FigureAnnotatorFromPageImage,
+    LLMClient,
+    MedQAAssistant,
+)
+from medrag_multi_modal.retrieval import MedCPTRetriever
+
+# Load environment variables
+load_dotenv()
+
+# Sidebar for configuration settings
+st.sidebar.title("Configuration Settings")
+project_name = st.sidebar.text_input(
+    "Project Name",
+    "ml-colabs/medrag-multi-modal"
+)
+chunk_dataset_name = st.sidebar.text_input(
+    "Text Chunk WandB Dataset Name",
+    "grays-anatomy-chunks:v0"
+)
+index_artifact_address = st.sidebar.text_input(
+    "WandB Index Artifact Address",
+    "ml-colabs/medrag-multi-modal/grays-anatomy-medcpt:v0",
+)
+image_artifact_address = st.sidebar.text_input(
+    "WandB Image Artifact Address",
+    "ml-colabs/medrag-multi-modal/grays-anatomy-images-marker:v6",
+)
+llm_model_name = st.sidebar.text_input(
+    "LLM Client Model Name",
+    "gemini-1.5-flash"
+)
+figure_extraction_model_name = st.sidebar.text_input(
+    "Figure Extraction Model Name",
+    "pixtral-12b-2409"
+)
+structured_output_model_name = st.sidebar.text_input(
+    "Structured Output Model Name",
+    "gpt-4o"
+)
+
+# Initialize Weave
+weave.init(project_name=project_name)
+
+# Initialize clients and assistants
+llm_client = LLMClient(model_name=llm_model_name)
+retriever = MedCPTRetriever.from_wandb_artifact(
+    chunk_dataset_name=chunk_dataset_name,
+    index_artifact_address=index_artifact_address,
+)
+figure_annotator = FigureAnnotatorFromPageImage(
+    figure_extraction_llm_client=LLMClient(model_name=figure_extraction_model_name),
+    structured_output_llm_client=LLMClient(model_name=structured_output_model_name),
+    image_artifact_address=image_artifact_address,
+)
+medqa_assistant = MedQAAssistant(
+    llm_client=llm_client, retriever=retriever, figure_annotator=figure_annotator
+)
+
+# Streamlit app layout
+st.title("MedQA Assistant App")
+
+# Initialize chat history
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.chat_history:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Chat thread section with user input and response
+if query := st.chat_input("What medical question can I assist you with today?"):
+    # Add user message to chat history
+    st.session_state.chat_history.append({"role": "user", "content": query})
+    with st.chat_message("user"):
+        st.markdown(query)
+
+    # Process query and get response
+    response = medqa_assistant.predict(query=query)
+    st.session_state.chat_history.append({"role": "assistant", "content": response})
+    with st.chat_message("assistant"):
+        st.markdown(response)

pyproject.toml

@@ -44,9 +44,13 @@ dependencies = [
     "jsonlines>=4.0.0",
     "opencv-python>=4.10.0.84",
     "openai>=1.52.2",
+    "streamlit>=1.39.0",
 ]
 
 [project.optional-dependencies]
+app = [
+    "streamlit>=1.39.0",
+]
 core = [
     "adapters>=1.0.0",
     "bm25s[full]>=0.2.2",
@@ -74,10 +78,12 @@ core = [
     "opencv-python>=4.10.0.84",
     "openai>=1.52.2",
 ]
-
-dev = ["pytest>=8.3.3", "isort>=5.13.2", "black>=24.10.0", "ruff>=0.6.9"]
-
-
+dev = [
+    "pytest>=8.3.3",
+    "isort>=5.13.2",
+    "black>=24.10.0",
+    "ruff>=0.6.9",
+]
 docs = [
     "mkdocs>=1.6.1",
     "mkdocstrings>=0.26.1",
@@ -91,4 +97,4 @@ docs = [
 
 
 [tool.pytest.ini_options]
-pythonpath = "."
+pythonpath = "."