major updates on cli module

autollm/serve/cli.py

@@ -6,17 +6,13 @@
 
 from autollm.auto.llm import AutoLiteLLM
 from autollm.auto.query_engine import AutoQueryEngine
-from autollm.serve.prompts import LLM_BUILDER_SYSTEM_PROMPT
+from autollm.serve.llm_utils import create_custom_llm
 from autollm.utils.document_reading import read_files_as_documents
 
 llama_index.set_global_handler("simple")
 
-llm_builder = AutoLiteLLM.from_defaults(system_prompt=LLM_BUILDER_SYSTEM_PROMPT)
 
-
-def configure_app(
-        openai_api_key, palm_api_key, uploaded_files, webpage_input, what_to_make_area, config_file, emoji,
-        name, description, instruction):
+def create_app(openai_api_key, palm_api_key, what_to_make_area, uploaded_files, webpage_input, config_file):
     global query_engine
     progress = gr.Progress()
 
@@ -26,6 +22,10 @@ def configure_app(
     progress(0.2, desc="Reading files...")
     file_documents = read_files_as_documents(input_files=uploaded_files)
 
+    progress(0.4, desc="Updating LLM...")
+    custom_llm = create_custom_llm(user_prompt=what_to_make_area, config=config_file)
+    emoji, name, description, instruction = update_configurations(custom_llm)
+
     progress(0.8, desc="Configuring app..")
     query_engine = AutoQueryEngine.from_defaults(
         documents=file_documents,
@@ -36,8 +36,22 @@ def configure_app(
 
     # Complete progress
     progress(1.0, desc="Completed")  # Complete progress bar
+    create_preview_output = gr.Textbox("App preview created on the right screen.")
+
+    return create_preview_output, emoji, name, description, instruction
+
+
+def update_configurations(custom_llm):
+    emoji = custom_llm.emoji
+    name = custom_llm.name
+    description = custom_llm.description
+    instruction = custom_llm.instructions
+
+    return gr.Textbox(emoji), gr.Textbox(name), gr.Textbox(description), gr.Textbox(instruction)
+
 
-    return gr.Textbox("App preview created on the right screen.")
+def update_app():
+    pass
 
 
 def predict(message, history):
@@ -89,15 +103,15 @@ def predict(message, history):
                     with gr.Accordion(label="Load config file", open=False):
                         config_file_upload = gr.File(
                             label="Configurations of LLM, Vector Store..", file_count="single")
-                    emoji_input = gr.Textbox(label="Emoji")
-                    name_input = gr.Textbox(label="Name")
-                    description_input = gr.Textbox(label="Description")
-                    instruction_input = gr.TextArea(label="Instructions")
+                    emoji = gr.Textbox(label="Emoji")
+                    name = gr.Textbox(label="Name")
+                    description = gr.Textbox(label="Description")
+                    instruction = gr.TextArea(label="Instructions")
                     with gr.Row():
                         with gr.Column(scale=1, min_width=10):
                             placeholder = gr.Button(visible=False, interactive=False)
                         with gr.Column(scale=1, min_width=100):
-                            create_preview_button_2 = gr.Button("Create Preview", variant="primary")
+                            update_preview_button = gr.Button("Update Preview", variant="primary")
                 configure_output = gr.Textbox(label="👆 Click `Create Preview` to see preview of the LLM app")
             with gr.Tab("Export"):
                 # Controls for 'Export' tab
@@ -121,18 +135,18 @@ def predict(message, history):
                     chat_interface = gr.ChatInterface(predict, chatbot=chatbot)
 
         create_preview_button.click(
-            configure_app,
+            create_app,
             inputs=[
-                openai_api_key_input, palm_api_key_input, uploaded_files, webpage_input, what_to_make_area,
-                config_file_upload, emoji_input, name_input, description_input, instruction_input
+                openai_api_key_input, palm_api_key_input, what_to_make_area, uploaded_files, webpage_input,
+                config_file_upload
             ],
-            outputs=[create_preview_output])
+            outputs=[create_preview_output, emoji, name, description, instruction])
 
-        create_preview_button_2.click(
-            configure_app,
+        update_preview_button.click(
+            update_app,
             inputs=[
-                openai_api_key_input, palm_api_key_input, uploaded_files, webpage_input, what_to_make_area,
-                config_file_upload, emoji_input, name_input, description_input, instruction_input
+                openai_api_key_input, palm_api_key_input, what_to_make_area, uploaded_files, webpage_input,
+                config_file_upload, emoji, name, description, instruction
             ],
             outputs=[configure_output],
             scroll_to_output=True)

autollm/serve/llm_utils.py

@@ -5,9 +5,13 @@
 
 from autollm import AutoLiteLLM
 
+DEFAULT_LLM_MODEL = "azure/gpt-4-1106"
+DEFAULT_LLM_MAX_TOKENS = 1024
+DEFAULT_LLM_TEMPERATURE = 0.1
+
 
 class CustomLLM(BaseModel):
-    """Data model for custom LLM creation."""
+    """Data model for custom LLM creation from user prompt."""
 
     emoji: str = Field(
         ...,
@@ -52,17 +56,18 @@ class CustomLLM(BaseModel):
 
 
 PROMPT_TEMPLATE_STR = """\
-Enhance the following user prompt for optimal interaction \
-with a custom LLM model. Ensure the revised prompt maintains the \
-original intent, is clear and detailed, and is adapted to the \
-specific context and task mentioned in the user input.
-
-User Input: {user_prompt}
+Your task is to revise the user prompt and create a JSON object \
+in the format of the CustomLLM data model. The JSON object will \
+be used to create a custom LLM model. Ensure the revised prompt \
+maintains the original intent, is clear and detailed, and is \
+adapted to the specific context and task mentioned in the user input.
 
 1. Analyze the basic prompt to understand its primary purpose and context.
 2. Refine the prompt to be clear, detailed, specific, and tailored to the context and task.
 3. Retain the core elements and intent of the original prompt.
 4. Provide an enhanced version of the prompt, ensuring it is optimized for a LLM model interaction.
+
+User prompt: {user_prompt}
 """
 
 
@@ -71,9 +76,12 @@ def create_custom_llm(user_prompt: str, config: Optional[Any] = None) -> CustomL
     if not user_prompt:
         raise ValueError("Please fill in the area of 'What would you like to make?'")
 
-    llm_model = config.get('llm_model', 'azure/gpt-4-1106')
-    llm_max_tokens = config.get('llm_max_tokens', 1024)
-    llm_temperature = config.get('llm_temperature', 0.1)
+    if not config:
+        config = {}
+
+    llm_model = config.get('llm_model', DEFAULT_LLM_MODEL)
+    llm_max_tokens = config.get('llm_max_tokens', DEFAULT_LLM_MAX_TOKENS)
+    llm_temperature = config.get('llm_temperature', DEFAULT_LLM_TEMPERATURE)
     llm_api_base = config.get('llm_api_base', None)
 
     llm = AutoLiteLLM.from_defaults(