Updated sql migration assistant UI (#260)

Revamps the UI for the migration assistant, adds workflow automation

sql_migration_assistant/__init__.py

@@ -9,6 +9,7 @@ def hello():
     w = WorkspaceClient(product="sql_migration_assistant", product_version="0.0.1")
     p = Prompts()
     setter_upper = SetUpMigrationAssistant()
+    setter_upper.check_cloud(w)
     final_config = setter_upper.setup_migration_assistant(w, p)
     current_path = Path(__file__).parent.resolve()
 

sql_migration_assistant/app/llm.py

@@ -1,24 +1,15 @@
-import logging
+import gradio as gr
 
 from databricks.sdk import WorkspaceClient
 from databricks.sdk.service.serving import ChatMessage, ChatMessageRole
 
-w = WorkspaceClient()
-foundation_llm_name = "databricks-meta-llama-3-1-405b-instruct"
-max_token = 4096
-messages = [
-    ChatMessage(role=ChatMessageRole.SYSTEM, content="You are an unhelpful assistant"),
-    ChatMessage(role=ChatMessageRole.USER, content="What is RAG?"),
-]
-
 
 class LLMCalls:
-    def __init__(self, foundation_llm_name, max_tokens):
+    def __init__(self, foundation_llm_name):
         self.w = WorkspaceClient()
         self.foundation_llm_name = foundation_llm_name
-        self.max_tokens = int(max_tokens)
 
-    def call_llm(self, messages):
+    def call_llm(self, messages, max_tokens, temperature):
         """
         Function to call the LLM model and return the response.
         :param messages: list of messages like
@@ -29,8 +20,17 @@ def call_llm(self, messages):
                     ]
         :return: the response from the model
         """
+
+        max_tokens = int(max_tokens)
+        temperature = float(temperature)
+        # check to make sure temperature is between 0.0 and 1.0
+        if temperature < 0.0 or temperature > 1.0:
+            raise gr.Error("Temperature must be between 0.0 and 1.0")
         response = self.w.serving_endpoints.query(
-            name=foundation_llm_name, max_tokens=max_token, messages=messages
+            name=self.foundation_llm_name,
+            max_tokens=max_tokens,
+            messages=messages,
+            temperature=temperature,
         )
         message = response.choices[0].message.content
         return message
@@ -53,14 +53,16 @@ def convert_chat_to_llm_input(self, system_prompt, chat):
 
     # this is called to actually send a request and receive response from the llm endpoint.
 
-    def llm_translate(self, system_prompt, input_code):
+    def llm_translate(self, system_prompt, input_code, max_tokens, temperature):
         messages = [
             ChatMessage(role=ChatMessageRole.SYSTEM, content=system_prompt),
             ChatMessage(role=ChatMessageRole.USER, content=input_code),
         ]
 
         # call the LLM end point.
-        llm_answer = self.call_llm(messages=messages)
+        llm_answer = self.call_llm(
+            messages=messages, max_tokens=max_tokens, temperature=temperature
+        )
         # Extract the code from in between the triple backticks (```), since LLM often prints the code like this.
         # Also removes the 'sql' prefix always added by the LLM.
         translation = llm_answer  # .split("Final answer:\n")[1].replace(">>", "").replace("<<", "")
@@ -73,12 +75,14 @@ def llm_chat(self, system_prompt, query, chat_history):
         llm_answer = self.call_llm(messages=messages)
         return llm_answer
 
-    def llm_intent(self, system_prompt, input_code):
+    def llm_intent(self, system_prompt, input_code, max_tokens, temperature):
         messages = [
             ChatMessage(role=ChatMessageRole.SYSTEM, content=system_prompt),
             ChatMessage(role=ChatMessageRole.USER, content=input_code),
         ]
 
         # call the LLM end point.
-        llm_answer = self.call_llm(messages=messages)
+        llm_answer = self.call_llm(
+            messages=messages, max_tokens=max_tokens, temperature=temperature
+        )
         return llm_answer