✨ first version of owid datasette oracle

apps/wizard/config/config.yml

@@ -35,6 +35,12 @@ main:
       maintainer: "@lucas"
       entrypoint: pages/expert/app.py
       emoji: "🧙"
+  oracle:
+      title: "OWID Datasette Oracle"
+      description: "Get help writing SQL queries for datasette!"
+      maintainer: "@daniel"
+      entrypoint: pages/owid_datasette_oracle.py
+      emoji: "🔮"
 
 # ETL steps
 etl:

apps/wizard/pages/owid_datasette_oracle.py

@@ -0,0 +1,223 @@
+"""Ask chat GPT for help writing datasette queries.
+"""
+from typing import Any, Dict, cast
+
+import streamlit as st
+from st_pages import add_indentation
+from streamlit_feedback import streamlit_feedback
+from structlog import get_logger
+
+from apps.wizard.pages.owid_datasette_oracle_prompt import OWID_DATASETTE_ORACLE_PROMPT
+from apps.wizard.utils import set_states
+from apps.wizard.utils.db import DB_IS_SET_UP, WizardDB
+from apps.wizard.utils.gpt import OpenAIWrapper, get_cost_and_tokens
+from etl.config import load_env
+
+# LOG
+log = get_logger()
+
+# CONFIG
+st.set_page_config(page_title="OWID Datasette Oracle", page_icon="🔮")
+add_indentation()
+## Title/subtitle
+st.title("**OWID Datasette oracle** 🔮")
+st.markdown("Get help writing SQL queries for Datasette!")
+
+## Load variables
+load_env()
+
+
+@st.cache_data(show_spinner=True)
+def ask_gpt(query, model):
+    response = api.query_gpt(query, model=model)
+    return response
+
+
+# GPT CONFIG
+MODEL_DEFAULT = "gpt-4-turbo-preview"
+MODELS_AVAILABLE = {
+    "gpt-3.5-turbo-0125": "GPT-3.5 Turbo (gpt-3.5-turbo-0125)",
+    "gpt-4-turbo-preview": "GPT-4 Turbo (gpt-4-turbo-preview)",
+}
+MODELS_AVAILABLE_LIST = list(MODELS_AVAILABLE.keys())
+
+
+# Handle feedback
+def handle_feedback(feedback: Dict[str, Any]) -> None:
+    """Handle feedback."""
+    print("handle feedback")
+    print(feedback)
+    # st.write(feedback)
+    # st.write(st.session_state.prompt)
+    # st.write(st.session_state.response)
+    WizardDB().add_usage(
+        question=st.session_state.messages[-2]["content"],
+        answer=st.session_state.response,
+        feedback=1 if feedback["score"] == "👍" else 0,
+        feedback_text=feedback.get("text", None),
+        cost=st.session_state.cost_last,
+    )
+
+
+# Switch category function
+def get_system_prompt() -> str:
+    """Get appropriate system prompt."""
+    return OWID_DATASETTE_ORACLE_PROMPT
+
+
+# Reset chat history
+def reset_messages() -> None:
+    """Reset messages to default."""
+    set_states(
+        {
+            "messages": [{"role": "system", "content": get_system_prompt()}],
+            "response": None,
+            "prompt": None,
+        }
+    )
+
+
+## Examples
+EXAMPLE_QUERIES = [
+    "> Which are our top 10 articles by pageviews?",
+    "> How many charts do we have that use only a single indicator?",
+    "> Do we have datasets whose indicators are not used in any chart?",
+]
+with st.popover("See examples"):
+    for example in EXAMPLE_QUERIES:
+        st.markdown(example)
+
+# Sidebar with GPT config
+st.session_state.analytics = st.session_state.get("analytics", True)
+with st.sidebar:
+    st.button(
+        label="Clear chat",
+        on_click=reset_messages,
+    )
+    st.divider()
+    st.toggle(
+        label="Collect data for analytics",
+        value=True,
+        on_change=lambda: set_states(
+            {
+                "analytics": not st.session_state.analytics,
+            }
+        ),
+        help="If enabled, we will collect usage data to improve the app. \n\nThis **is really helpful to improve** how we query chat GPT: E.g. which system prompt to use, optimise costs, and much more 😊. \n\nData collected: questions, responses and feedback submitted. \n\nYou can see how this data is collected [here](https://github.com/owid/etl/blob/master/apps/wizard/utils/db.py). \n\nRecords are anonymous.",
+    )
+    st.divider()
+    st.markdown("## GPT Configuration")
+    model_name = st.selectbox(
+        label="Select GPT model",
+        options=MODELS_AVAILABLE_LIST,
+        format_func=lambda x: MODELS_AVAILABLE[x],
+        index=MODELS_AVAILABLE_LIST.index(MODEL_DEFAULT),
+        help="[Pricing](https://openai.com/pricing) | [Model list](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo)",
+    )
+    ## See pricing list: https://openai.com/pricing (USD)
+    ## See model list: https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo
+
+    use_reduced_context = st.toggle(
+        "Reduced context window",
+        value=False,
+        help="If checked, only the last user message will be accounted (i.e less tokens and therefore cheaper).",
+    )
+    temperature = st.slider(
+        "Temperature",
+        min_value=0.0,
+        max_value=2.0,
+        value=0.15,
+        step=0.01,
+        help="What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.",
+    )
+    max_tokens = int(
+        st.number_input(
+            "Max tokens",
+            min_value=32,
+            max_value=4096,
+            value=4096,
+            step=32,
+            help="The maximum number of tokens in the response.",
+        )
+    )
+
+# API with OPENAI
+api = OpenAIWrapper()
+
+# ACTUAL APP
+# Initialize chat history
+if "messages" not in st.session_state:
+    reset_messages()
+
+# DEGUG
+# st.write([m for m in st.session_state.messages if m["role"] != "system"])
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    if message["role"] != "system":
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+# Initialise session state
+st.session_state.response = st.session_state.get("response", None)
+st.session_state.prompt = st.session_state.get("prompt", None)
+st.session_state.feedback_key = st.session_state.get("feedback_key", 0)
+st.session_state.cost_last = st.session_state.get("cost_last", 0)
+
+# React to user input
+if prompt := st.chat_input("Ask me!"):
+    st.session_state.feedback_key += 1
+    print("asking GPT...")
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    # Build GPT query (only use the system prompt and latest user input)
+    if use_reduced_context:
+        messages = [{"role": "system", "content": get_system_prompt()}, {"role": "user", "content": prompt}]
+    else:
+        messages = st.session_state.messages
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        # Ask GPT (stream)
+        stream = api.chat.completions.create(
+            model=cast(str, model_name),
+            messages=messages,  # type: ignore
+            temperature=temperature,
+            max_tokens=max_tokens,
+            stream=True,
+        )
+        st.session_state.response = cast(str, st.write_stream(stream))
+
+        # Add new response by the System
+        st.session_state.messages.append({"role": "assistant", "content": st.session_state.response})
+
+        # Add prompt to session state
+        st.session_state.prompt = prompt
+
+        print("finished asking GPT...")
+
+if st.session_state.response:
+    # Get cost & tokens
+    text_in = "\n".join([m["content"] for m in st.session_state.messages])
+    cost, num_tokens = get_cost_and_tokens(text_in, st.session_state.response, cast(str, model_name))
+    cost_msg = f"**Cost**: ≥{cost} USD.\n\n **Tokens**: ≥{num_tokens}."
+    st.session_state.cost_last = cost
+
+    if DB_IS_SET_UP and st.session_state.analytics:
+        # Get feedback only if DB is properly setup
+        feedback = streamlit_feedback(
+            feedback_type="thumbs",
+            optional_text_label="[Optional] Please provide an explanation",
+            key=f"feedback_{st.session_state.feedback_key}",
+            on_submit=handle_feedback,
+        )
+    # Show cost below feedback
+    st.info(cost_msg)
+
+# DEBUG
+# st.write([m for m in st.session_state.messages if m["role"] != "system"])