Passing variables in workflow

app.py

@@ -12,6 +12,7 @@ from threading import Thread
 import time
 import psutil
 from sentence_transformers import SentenceTransformer
+import textwrap
 
 #Imported Long Variables - comment for each move to search
 from relatively_constant_variables import *
@@ -756,6 +757,22 @@ def refresh_file_explorer():
 
 #-----------------------------------------------------------------------------------------------------------------------------------
 
+LinPEWFprevious_messages = []
+
+def LinPEWFformat_prompt(current_prompt, prev_messages):
+    formatted_prompt = textwrap.dedent("""
+    Previous prompts and responses:
+    {history}
+    
+    Current prompt:
+    {current}
+    
+    Please respond to the current prompt, taking into account the context from previous prompts and responses.
+    """).strip()
+    
+    history = "\n\n".join(f"Prompt {i+1}: {msg}" for i, msg in enumerate(prev_messages))
+    return formatted_prompt.format(history=history, current=current_prompt)
+
 #-----------------------------------------------------------------------------------------------------------------------------------
 
 with gr.Blocks() as demo:
@@ -771,9 +788,65 @@ with gr.Blocks() as demo:
     with gr.Accordion("Config and Asset Assistance - Click to open", open=False):
         with gr.Accordion("Themes to consider before you attempt config", open=False):
             gr.HTML("Copy paste any old config to llm and ask to remix is the easiest <br>To bake 'Moral of the story' in you have to be very deliberate")
-        with gr.Accordion("New Config Proto Assist - Trying to abstract the process into one worflow is beyond me so multiple paths to goal (config) is the aim now", open=False):
             gr.HTML("UI can be media and all items can have media")
-            with gr.Tab("List to Empty Config with Edit support - Linear"):
+            with gr.Accordion("Qwen 0.5B as Space Guide Tests", open=False):
+                with gr.Tab("General FAQ Attempt"):
+                    FAQMainOutput = gr.TextArea(placeholder='Output will show here', value='')
+                    FAQCustomButtonInput = gr.TextArea(lines=1, placeholder='Prompt goes here')
+
+                    for category_name, category_prompts in FAQAllprompts.items():
+                        with gr.Accordion(f"General {category_name} Pattern based", open=False):
+                            with gr.Group():
+                                for index, (prompt, _) in enumerate(category_prompts):
+                                    button = gr.Button(prompt)
+                                    button.click(llmguide_generate_response, inputs=[FAQCustomButtonInput, gr.State(index), gr.State(category_name)], outputs=FAQMainOutput)
+
+                with gr.Tab("General RAG (Pathfinder?) Attempt"):
+                    gr.HTML("https://huggingface.co/spaces/mteb/leaderboard - Source for SOTA - current using all-MiniLM-L6-v2")
+                    gr.HTML("Placeholder for weak RAG Type Charcter interaction test aka input for JSON 'Knowledge Base' Input")
+                    gr.Interface(
+                        fn=process_query,
+                        inputs=[
+                            gr.Textbox(lines=2, placeholder="Enter your question here..."),
+                            gr.Checkbox(label="Use RAG"),
+                            gr.Checkbox(label="Stream output")
+                        ],
+                        outputs=[
+                            gr.Textbox(label="Generated Response"),
+                            gr.Textbox(label="Tokens per second"),
+                            gr.Textbox(label="RAM Usage"),
+                            gr.Textbox(label="Referenced Documents")
+                        ],
+                        title="RAG/Non-RAG Q&A System",
+                        description="Ask a question with or without using RAG. The response is generated using a GPU-accelerated model. RAM usage and referenced document IDs (for RAG) are logged."
+                    )
+
+                with gr.Tab("Any Request to Qwen2-0.5B"):
+                    gr.HTML("Placeholder for https://huggingface.co/h2oai/h2o-danube3-500m-chat-GGUF and https://huggingface.co/OuteAI/Lite-Mistral-150M-v2-Instruct as alternative")
+                    gr.HTML("https://huggingface.co/spaces/HuggingFaceTB/SmolLM-135M-Instruct-WebGPU 125 mdeol to be tested as alternative (and all up to 1.5b - how to delte a model in your code?) - Need to go over the dataset to see how to prompt it - https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus ")
+                    gr.HTML("Placeholder for qwen 2 72b as alternative use checkbox and gradio client api call")
+                    gr.Markdown("# Qwen-0.5B-Instruct Language Model")
+                    gr.Markdown("This demo uses the Qwen-0.5B-Instruct model to generate responses based on your input.")
+                    gr.HTML("Example prompts: <br>I am writing a story about a chef. please write dishes to appear on the menu. <br>What are the most common decisions that a chef story would include? <br>What are the kinds problems that a chef story would include? <br>What are the kinds of out of reach goals that a chef story would include? <br>Continue this config - Paste any complete block of the config")
+                    
+                    with gr.Row():
+                        with gr.Column():
+                            llmguide_prompt = gr.Textbox(lines=2, placeholder="Enter your prompt here...")
+                            llmguide_stream_checkbox = gr.Checkbox(label="Enable streaming")
+                            llmguide_submit_button = gr.Button("Generate")
+                        
+                        with gr.Column():
+                            llmguide_output = gr.Textbox(lines=10, label="Generated Response")
+                            llmguide_tokens_per_second = gr.Textbox(label="Tokens per Second")
+                    
+                    llmguide_submit_button.click(
+                        llmguide_generate_response,
+                        inputs=[llmguide_prompt, llmguide_stream_checkbox],
+                        outputs=[llmguide_output, llmguide_tokens_per_second],
+                    )
+
+        with gr.Accordion("New Config Proto Assist - Trying to abstract the process into one worflow is beyond me so multiple paths to goal (config) is the aim now", open=False):
+            with gr.Tab("Linear - List to Empty Config with Edit support"):
                 with gr.Accordion("Can copy in the Test Example State Machine tab - only linear path for now", open=False):
                     gr.Markdown("# Story and Timeline Generator")
                     gr.Markdown("Click the button to generate a random timeline and story based on UI elements and story events. <br>Ask an LLM to use this to write a story around")
@@ -812,20 +885,40 @@ with gr.Blocks() as demo:
                     
                     generate_button.click(generate_story_and_timeline, inputs=[generate_no_story_timeline_points, generate_no_ui_timeline_points, timeline_num_lists_slider, timeline_items_per_list_slider, timeline_include_existing_games, timeline_include_multiplayer], outputs=[timeline_output_with_assets, story_output, game_structure_output_text_with_media, timeline_output_text, timeline_selected_lists_text]) #, generate_no_media_timeline_points, generate_with_media_check], outputs=[timeline_output_with_assets, timeline_output, story_output, game_structure_output_text_with_media, game_structure_output_text])
 
-            with gr.Tab("Prompt Engineering as basis for ideation process"):
+            with gr.Tab("Linear - Prompt Engineering as basis for ideation process"):
                 gr.HTML("Current Assited workflow idea - Story timeline events suggestions (LLM / Premade List) | Merging events with premade mermaid structures (LLM + Story Text + Mermaid Text) | Edit mermaid till satisfied (LLM + Story Text) | Ask LLM to convert to config (LLM + JSON Text) | Edit config (LLM / User with format assistance or not) | Playtest and go back to mermaaid or config if there are problems")
                 gr.HTML("Interactive movie (UI interaction or no progress) vs Branching Paths (Maze)")
                 gr.HTML("Things that can change the workflow - Asset First (Make Asset and make the transitions using LLM), Export First (Custom JS config, Playcanvas, Unreal Engine reverse engineered to this spaces config?) Game Mechanics First (eg. Player Stats, Inventory and NPCS not implemented yet, so traversal type games best aka graph like structures)")
                 gr.HTML("Config writing = Remix old one, Ask LLM to make one, Endless combination testing using the prompt engineering above or writing by hand (prompt engineering on yourself)")    
                 gr.HTML("Can use song lyrics as thematic source")
                 gr.HTML("Placeholder for each below prompt getting a Textbox")
-                for item in Storycraftprompts:
+                # for item in Storycraftprompts:
+                #     input = gr.State(item)
+                #     output = gr.Textbox("", label=item)
+                #     outputbtn = gr.Button(item).click(fn=llmguide_generate_response, inputs=input, outputs=output)
+                for i, item in enumerate(Storycraftprompts, 1):
                     input = gr.State(item)
-                    output = gr.Textbox("", label=item)
-                    outputbtn = gr.Button(item).click(fn=llmguide_generate_response, inputs=input, outputs=output)
+                    previous_input = gr.State(lambda: LinPEWFprevious_messages)
+                    output = gr.Textbox("", label=f"Output {i}")
+                    
+                    def LinPEWF_update_and_generate(prompt, prev_msgs):
+                        prev_msgs.append(prompt)
+                        formatted_prompt = LinPEWFformat_prompt(prompt, prev_msgs)
+                        response = llmguide_generate_response(formatted_prompt)
+                        prev_msgs.append(f"Response: {response}")
+                        return response
+                    
+                    outputbtn = gr.Button(f"Generate {i}").click(
+                        fn=LinPEWF_update_and_generate, 
+                        inputs=[input, previous_input], 
+                        outputs=output
+                    )
+
+                    LinPEWFprevious_messages.append(item)
+
 
             #with gr.Accordion("Decisions / Timeline Creation to Story to Config Conversation", open=False):
-            with gr.Tab("Decisions / Timeline Creation to Story to Config Conversation"):
+            with gr.Tab("Branching - Decisions / Timeline Creation to Story to Config Conversation"):
                 gr.HTML("Structures for interesting timeline progression")
                 gr.HTML("Placeholder - Considerations - Story from the perspective of Main character or NPC in the LLM genereated story")
                 mermaideditoriframebtn = gr.Button("Load Mermaid Editor")
@@ -853,62 +946,6 @@ with gr.Blocks() as demo:
                 with gr.Tab("Themes"):
                     gr.HTML("")
 
-        with gr.Accordion("Qwen 0.5B as Space Guide Tests", open=False):
-            with gr.Tab("General FAQ Attempt"):
-                FAQMainOutput = gr.TextArea(placeholder='Output will show here', value='')
-                FAQCustomButtonInput = gr.TextArea(lines=1, placeholder='Prompt goes here')
-
-                for category_name, category_prompts in FAQAllprompts.items():
-                    with gr.Accordion(f"General {category_name} Pattern based", open=False):
-                        with gr.Group():
-                            for index, (prompt, _) in enumerate(category_prompts):
-                                button = gr.Button(prompt)
-                                button.click(llmguide_generate_response, inputs=[FAQCustomButtonInput, gr.State(index), gr.State(category_name)], outputs=FAQMainOutput)
-
-            with gr.Tab("General RAG (Pathfinder?) Attempt"):
-                gr.HTML("https://huggingface.co/spaces/mteb/leaderboard - Source for SOTA - current using all-MiniLM-L6-v2")
-                gr.HTML("Placeholder for weak RAG Type Charcter interaction test aka input for JSON 'Knowledge Base' Input")
-                gr.Interface(
-                    fn=process_query,
-                    inputs=[
-                        gr.Textbox(lines=2, placeholder="Enter your question here..."),
-                        gr.Checkbox(label="Use RAG"),
-                        gr.Checkbox(label="Stream output")
-                    ],
-                    outputs=[
-                        gr.Textbox(label="Generated Response"),
-                        gr.Textbox(label="Tokens per second"),
-                        gr.Textbox(label="RAM Usage"),
-                        gr.Textbox(label="Referenced Documents")
-                    ],
-                    title="RAG/Non-RAG Q&A System",
-                    description="Ask a question with or without using RAG. The response is generated using a GPU-accelerated model. RAM usage and referenced document IDs (for RAG) are logged."
-                )
-
-            with gr.Tab("Any Request to Qwen2-0.5B"):
-                gr.HTML("Placeholder for https://huggingface.co/h2oai/h2o-danube3-500m-chat-GGUF and https://huggingface.co/OuteAI/Lite-Mistral-150M-v2-Instruct as alternative")
-                gr.HTML("https://huggingface.co/spaces/HuggingFaceTB/SmolLM-135M-Instruct-WebGPU 125 mdeol to be tested as alternative (and all up to 1.5b - how to delte a model in your code?) - Need to go over the dataset to see how to prompt it - https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus ")
-                gr.HTML("Placeholder for qwen 2 72b as alternative use checkbox and gradio client api call")
-                gr.Markdown("# Qwen-0.5B-Instruct Language Model")
-                gr.Markdown("This demo uses the Qwen-0.5B-Instruct model to generate responses based on your input.")
-                gr.HTML("Example prompts: <br>I am writing a story about a chef. please write dishes to appear on the menu. <br>What are the most common decisions that a chef story would include? <br>What are the kinds problems that a chef story would include? <br>What are the kinds of out of reach goals that a chef story would include? <br>Continue this config - Paste any complete block of the config")
-                
-                with gr.Row():
-                    with gr.Column():
-                        llmguide_prompt = gr.Textbox(lines=2, placeholder="Enter your prompt here...")
-                        llmguide_stream_checkbox = gr.Checkbox(label="Enable streaming")
-                        llmguide_submit_button = gr.Button("Generate")
-                    
-                    with gr.Column():
-                        llmguide_output = gr.Textbox(lines=10, label="Generated Response")
-                        llmguide_tokens_per_second = gr.Textbox(label="Tokens per Second")
-                
-                llmguide_submit_button.click(
-                    llmguide_generate_response,
-                    inputs=[llmguide_prompt, llmguide_stream_checkbox],
-                    outputs=[llmguide_output, llmguide_tokens_per_second],
-                )
-
         with gr.Accordion("Existing Config Crafting Progression - click to open", open=False):
             with gr.Accordion("Test for config to gradio components order - ignore for now", open=False ):
                     gr.HTML("Placeholder for changing the render below to the one above for new config but with the ability to upload files aka the media field should be file uploader / dropdowns for all files that have been uploaded")