Update app.py

app.py

@@ -14,55 +14,25 @@ import random
 # you need to set your OpenAI API key as environment variable
 openai.api_key = st.secrets["API_KEY"]
 
-MOVIES_EXAMPLE_DOC = """{
-  _id: ObjectId("573a1390f29313caabcd4135"),
-  genres: [ 'Short' ],
-  runtime: 1,
-  cast: [ 'Charles Kayser', 'John Ott' ],
-  num_mflix_comments: 0,
-  title: 'Blacksmith Scene',
-  countries: [ 'USA' ],
-  released: ISODate("1893-05-09T00:00:00.000Z"),
-  directors: [ 'William K.L. Dickson' ],
-  rated: 'UNRATED',
-  awards: { wins: 1, nominations: 0, text: '1 win.' },
-  lastupdated: '2015-08-26 00:03:50.133000000',
-  year: 1893,
-  imdb: { rating: 6.2, votes: 1189, id: 5 },
-  type: 'movie',
-  tomatoes: {
-    viewer: { rating: 3, numReviews: 184, meter: 32 },
-    lastUpdated: ISODate("2015-06-28T18:34:09.000Z")
-  }
-}"""
-
-MOVIES_EXAMPLE_QUESTIONS = [
+SQL_EXAMPLE_QUERIES = [
     (
-        "How many fantasy or horror movies from the USA with an imdb rating "
-        "greater than 6.0 are there in this dataset?"
+        "SELECT CustomerName, City FROM Customers;"
     ),
     (
-        "Which movies were released on a Monday and have a higher tomato rating "
-        "than IMDB rating? Keep in mind that IMDB goes from 1-10 and tomatoes "
-        "only from 1-5, so you need to normalise the ratings to do a fair comparison."
+        "Select distinct Salary from Employee e1 where 2=Select count(distinct Salary) from Employee e2 where e1.salary<=e2.salary;"
     ),
-    "What movies should I watch to learn more about Japanse culture?",
+    "Select Employee_name,Salary/12 as ‘Monthly Salary’ from employee;",
     (
-        "How many movies were released in each decade? Write decade as a string, e.g. "
-        "'1920-1929'. Sort ascending by decade."
-    ),
-    (
-        "Find movies that are suitable to watch with my kids, both by genre and their "
-        "parental guidance rating. Just recommend good movies."
+        "Select * from Employee where Rowid= select min(Rowid) from Employee;"
     ),
 ]
 
 BASE_CHAT_MESSAGES = [
     {
         "role": "system",
-        "content": "You are an expert English to MongoDB aggregation pipeline translation system."
-        "You will accept an example document from a collection and an English question, and return an aggregation "
-        "pipeline that can answer the question. Do not explain the query or add any additional comments, only "
+        "content": "You are an expert SQL to MongoDB aggregation pipeline translation system."
+        "You will accept SQL query and return a MongoDB aggregation pipeline "
+        "that can convert the SQL query. Do not explain the query or add any additional comments, only "
         "return a single code block with the aggregation pipeline without the aggregate command.",
     }
 ]
@@ -78,7 +48,7 @@ def ask_model(doc, question):
     messages = BASE_CHAT_MESSAGES + [
         {
             "role": "user",
-            "content": f"Example document: {doc.strip()}\n\nQuestion: {question.strip()}\n\n",
+            "content": f"Example SQL Query: {question.strip()}\n\n",
         }
     ]
 
@@ -112,10 +82,10 @@ if not "default_question" in st.session_state:
 
 
 st.markdown(
-    """# English to MQL Demo
+    """# SQL to MQL Demo
 
-This demo app uses OpenAI's GPT-4 (gpt-4) model to generate a MongoDB
-aggregation pipeline from an English question and example document.
+This demo app uses OpenAI's GPT-3.5 (gpt-3.5) model to generate a MongoDB
+aggregation pipeline from a SQL query.
 
 🚧 The app is experimental and may return incorrect results. Do not enter any sensitive information! 🚧
 """
@@ -126,18 +96,12 @@ aggregation pipeline from an English question and example document.
 col_left, col_right = st.columns(2, gap="large")
 
 with col_left:
-    st.markdown("### Example Document and Question")
+    st.markdown("### Example SQL query")
     # wrap textareas in form
     with st.form("text_inputs"):
-        doc = st.text_area(
-            "Enter example document from collection, e.g. db.collection.findOne()",
-            value=MOVIES_EXAMPLE_DOC,
-            height=300,
-        )
-
         # question textarea
-        question = st.text_area(
-            label="Ask question in English",
+        query = st.text_area(
+            label="SQL query",
             value=st.session_state.default_question,
         )
 
@@ -146,7 +110,7 @@ with col_left:
         if submitted:
             st.session_state._id = None
             st.session_state.feedback = False
-            st.session_state.response = ask_model(doc, question)
+            st.session_state.response = ask_model(query)
 
 
 with col_right: