remove args

.ipynb_checkpoints/app-checkpoint.py

@@ -1,208 +0,0 @@
-from strings import TITLE, ABSTRACT, BOTTOM_LINE
-from strings import DEFAULT_EXAMPLES
-from strings import SPECIAL_STRS
-from styles import PARENT_BLOCK_CSS
-
-import time
-import gradio as gr
-
-from args import parse_args
-from model import load_model
-from gen import get_output_batch, StreamModel
-from utils import generate_prompt, post_processes_batch, post_process_stream, get_generation_config, common_post_process
-
-def chat_stream(
-    context,
-    instruction,
-    state_chatbot,
-):
-    # print(instruction)
-
-    # user input should be appropriately formatted (don't be confused by the function name)
-    instruction_display = common_post_process(instruction)
-    instruction_prompt = generate_prompt(instruction, state_chatbot, context)    
-    bot_response = model(
-        instruction_prompt,
-        max_tokens=256,
-        temperature=1,
-        top_p=0.9
-    )
-    
-    instruction_display = None if instruction_display == SPECIAL_STRS["continue"] else instruction_display
-    state_chatbot = state_chatbot + [(instruction_display, None)]
-    
-    prev_index = 0
-    agg_tokens = ""
-    cutoff_idx = 0
-    for tokens in bot_response:
-        tokens = tokens.strip()
-        cur_token = tokens[prev_index:]
-        
-        if "#" in cur_token and agg_tokens == "":
-            cutoff_idx = tokens.find("#")
-            agg_tokens = tokens[cutoff_idx:]
-
-        if agg_tokens != "":
-            if len(agg_tokens) < len("### Instruction:") :
-                agg_tokens = agg_tokens + cur_token
-            elif len(agg_tokens) >= len("### Instruction:"):
-                if tokens.find("### Instruction:") > -1:
-                    processed_response, _ = post_process_stream(tokens[:tokens.find("### Instruction:")].strip())
-
-                    state_chatbot[-1] = (
-                        instruction_display, 
-                        processed_response
-                    )
-                    yield (state_chatbot, state_chatbot, context)
-                    break
-                else:
-                    agg_tokens = ""
-                    cutoff_idx = 0
-
-        if agg_tokens == "":
-            processed_response, to_exit = post_process_stream(tokens)
-            state_chatbot[-1] = (instruction_display, processed_response)
-            yield (state_chatbot, state_chatbot, context)
-
-            if to_exit:
-                break
-
-        prev_index = len(tokens)
-
-    yield (
-        state_chatbot,
-        state_chatbot,
-        gr.Textbox.update(value=tokens) if instruction_display == SPECIAL_STRS["summarize"] else context
-    )
-
-def chat_batch(
-    contexts,
-    instructions, 
-    state_chatbots,
-):
-    state_results = []
-    ctx_results = []
-
-    instruct_prompts = [
-        generate_prompt(instruct, histories, ctx) 
-        for ctx, instruct, histories in zip(contexts, instructions, state_chatbots)
-    ]
-        
-    bot_responses = get_output_batch(
-        model, tokenizer, instruct_prompts, generation_config
-    )
-    bot_responses = post_processes_batch(bot_responses)
-
-    for ctx, instruction, bot_response, state_chatbot in zip(contexts, instructions, bot_responses, state_chatbots):
-        new_state_chatbot = state_chatbot + [('' if instruction == SPECIAL_STRS["continue"] else instruction, bot_response)]
-        ctx_results.append(gr.Textbox.update(value=bot_response) if instruction == SPECIAL_STRS["summarize"] else ctx)
-        state_results.append(new_state_chatbot)
-
-    return (state_results, state_results, ctx_results)
-
-def reset_textbox():
-    return gr.Textbox.update(value='')
-
-def run(args):
-    global model, tokenizer, generation_config, batch_enabled
-    
-    batch_enabled = True if args.batch_size > 1 else False    
-
-    model, tokenizer = load_model(
-        base=args.base_url,
-        finetuned=args.ft_ckpt_url
-    )    
-    
-    generation_config = get_generation_config(
-        args.gen_config_path
-    )
-    
-    if not batch_enabled:
-        model = StreamModel(model, tokenizer)
-        # model.generation_config = generation_config
-    
-    with gr.Blocks(css=PARENT_BLOCK_CSS) as demo:
-        state_chatbot = gr.State([])
-
-        with gr.Column(elem_id='col_container'):
-            gr.Markdown(f"## {TITLE}\n\n\n{ABSTRACT}")
-
-            with gr.Accordion("Context Setting", open=False):
-                context_txtbox = gr.Textbox(placeholder="Surrounding information to AI", label="Enter Context")
-                hidden_txtbox = gr.Textbox(placeholder="", label="Order", visible=False)
-
-            chatbot = gr.Chatbot(elem_id='chatbot', label="Alpaca-LoRA")
-            instruction_txtbox = gr.Textbox(placeholder="What do you want to say to AI?", label="Instruction")
-            send_prompt_btn = gr.Button(value="Send Prompt")
-            
-            with gr.Accordion("Helper Buttons", open=False):
-                gr.Markdown(f"`Continue` lets AI to complete the previous incomplete answers. `Summarize` lets AI to summarize the conversations so far.")
-                continue_txtbox = gr.Textbox(value=SPECIAL_STRS["continue"], visible=False)
-                summrize_txtbox = gr.Textbox(value=SPECIAL_STRS["summarize"], visible=False)
-                
-                continue_btn = gr.Button(value="Continue")
-                summarize_btn = gr.Button(value="Summarize")
-
-            gr.Markdown("#### Examples")
-            for idx, examples in enumerate(DEFAULT_EXAMPLES):
-                with gr.Accordion(examples["title"], open=False):
-                    gr.Examples(
-                        examples=examples["examples"], 
-                        inputs=[
-                            hidden_txtbox, instruction_txtbox
-                        ],
-                        label=None
-                    )
-
-            gr.Markdown(f"{BOTTOM_LINE}")
-            
-        send_prompt_btn.click(
-            chat_batch if batch_enabled else chat_stream, 
-            [context_txtbox, instruction_txtbox, state_chatbot],
-            [state_chatbot, chatbot, context_txtbox],
-            batch=batch_enabled,
-            max_batch_size=args.batch_size,
-        )
-        send_prompt_btn.click(
-            reset_textbox, 
-            [], 
-            [instruction_txtbox],
-        )
-        
-        continue_btn.click(
-            chat_batch if batch_enabled else chat_stream, 
-            [context_txtbox, continue_txtbox, state_chatbot],
-            [state_chatbot, chatbot, context_txtbox],
-            batch=batch_enabled,
-            max_batch_size=args.batch_size,
-        )
-        continue_btn.click(
-            reset_textbox, 
-            [], 
-            [instruction_txtbox],
-        )
-        
-        summarize_btn.click(
-            chat_batch if batch_enabled else chat_stream, 
-            [context_txtbox, summrize_txtbox, state_chatbot],
-            [state_chatbot, chatbot, context_txtbox],
-            batch=batch_enabled,
-            max_batch_size=args.batch_size,
-        )
-        summarize_btn.click(
-            reset_textbox, 
-            [], 
-            [instruction_txtbox],
-        )              
-
-    demo.queue(
-        concurrency_count=2,
-        max_size=100,
-    ).launch(
-        max_threads=2,
-        server_name="0.0.0.0",
-    )
-
-if __name__ == "__main__":
-    args = parse_args()
-    run(args)

app.py

@@ -6,11 +6,17 @@ from styles import PARENT_BLOCK_CSS
 import time
 import gradio as gr
 
-from args import parse_args
 from model import load_model
 from gen import get_output_batch, StreamModel
 from utils import generate_prompt, post_processes_batch, post_process_stream, get_generation_config, common_post_process
 
+model, tokenizer = load_model(
+    base="decapoda-research/llama-7b-hf",
+    finetuned="tloen/alpaca-lora-7b"
+)    
+
+model = StreamModel(model, tokenizer)
+
 def chat_stream(
     context,
     instruction,
@@ -103,106 +109,78 @@ def chat_batch(
 def reset_textbox():
     return gr.Textbox.update(value='')
 
-def run(args):
-    global model, tokenizer, generation_config, batch_enabled
-    
-    batch_enabled = True if args.batch_size > 1 else False    
-
-    model, tokenizer = load_model(
-        base=args.base_url,
-        finetuned=args.ft_ckpt_url
-    )    
-    
-    generation_config = get_generation_config(
-        args.gen_config_path
+with gr.Blocks(css=PARENT_BLOCK_CSS) as demo:
+    state_chatbot = gr.State([])
+
+    with gr.Column(elem_id='col_container'):
+        gr.Markdown(f"## {TITLE}\n\n\n{ABSTRACT}")
+
+        with gr.Accordion("Context Setting", open=False):
+            context_txtbox = gr.Textbox(placeholder="Surrounding information to AI", label="Enter Context")
+            hidden_txtbox = gr.Textbox(placeholder="", label="Order", visible=False)
+
+        chatbot = gr.Chatbot(elem_id='chatbot', label="Alpaca-LoRA")
+        instruction_txtbox = gr.Textbox(placeholder="What do you want to say to AI?", label="Instruction")
+        send_prompt_btn = gr.Button(value="Send Prompt")
+
+        with gr.Accordion("Helper Buttons", open=False):
+            gr.Markdown(f"`Continue` lets AI to complete the previous incomplete answers. `Summarize` lets AI to summarize the conversations so far.")
+            continue_txtbox = gr.Textbox(value=SPECIAL_STRS["continue"], visible=False)
+            summrize_txtbox = gr.Textbox(value=SPECIAL_STRS["summarize"], visible=False)
+
+            continue_btn = gr.Button(value="Continue")
+            summarize_btn = gr.Button(value="Summarize")
+
+        gr.Markdown("#### Examples")
+        for idx, examples in enumerate(DEFAULT_EXAMPLES):
+            with gr.Accordion(examples["title"], open=False):
+                gr.Examples(
+                    examples=examples["examples"], 
+                    inputs=[
+                        hidden_txtbox, instruction_txtbox
+                    ],
+                    label=None
+                )
+
+        gr.Markdown(f"{BOTTOM_LINE}")
+
+    send_prompt_btn.click(
+        chat_batch if batch_enabled else chat_stream, 
+        [context_txtbox, instruction_txtbox, state_chatbot],
+        [state_chatbot, chatbot, context_txtbox],
+    )
+    send_prompt_btn.click(
+        reset_textbox, 
+        [], 
+        [instruction_txtbox],
     )
-    
-    if not batch_enabled:
-        model = StreamModel(model, tokenizer)
-        # model.generation_config = generation_config
-    
-    with gr.Blocks(css=PARENT_BLOCK_CSS) as demo:
-        state_chatbot = gr.State([])
-
-        with gr.Column(elem_id='col_container'):
-            gr.Markdown(f"## {TITLE}\n\n\n{ABSTRACT}")
-
-            with gr.Accordion("Context Setting", open=False):
-                context_txtbox = gr.Textbox(placeholder="Surrounding information to AI", label="Enter Context")
-                hidden_txtbox = gr.Textbox(placeholder="", label="Order", visible=False)
-
-            chatbot = gr.Chatbot(elem_id='chatbot', label="Alpaca-LoRA")
-            instruction_txtbox = gr.Textbox(placeholder="What do you want to say to AI?", label="Instruction")
-            send_prompt_btn = gr.Button(value="Send Prompt")
-            
-            with gr.Accordion("Helper Buttons", open=False):
-                gr.Markdown(f"`Continue` lets AI to complete the previous incomplete answers. `Summarize` lets AI to summarize the conversations so far.")
-                continue_txtbox = gr.Textbox(value=SPECIAL_STRS["continue"], visible=False)
-                summrize_txtbox = gr.Textbox(value=SPECIAL_STRS["summarize"], visible=False)
-                
-                continue_btn = gr.Button(value="Continue")
-                summarize_btn = gr.Button(value="Summarize")
-
-            gr.Markdown("#### Examples")
-            for idx, examples in enumerate(DEFAULT_EXAMPLES):
-                with gr.Accordion(examples["title"], open=False):
-                    gr.Examples(
-                        examples=examples["examples"], 
-                        inputs=[
-                            hidden_txtbox, instruction_txtbox
-                        ],
-                        label=None
-                    )
 
-            gr.Markdown(f"{BOTTOM_LINE}")
-            
-        send_prompt_btn.click(
-            chat_batch if batch_enabled else chat_stream, 
-            [context_txtbox, instruction_txtbox, state_chatbot],
-            [state_chatbot, chatbot, context_txtbox],
-            batch=batch_enabled,
-            max_batch_size=args.batch_size,
-        )
-        send_prompt_btn.click(
-            reset_textbox, 
-            [], 
-            [instruction_txtbox],
-        )
-        
-        continue_btn.click(
-            chat_batch if batch_enabled else chat_stream, 
-            [context_txtbox, continue_txtbox, state_chatbot],
-            [state_chatbot, chatbot, context_txtbox],
-            batch=batch_enabled,
-            max_batch_size=args.batch_size,
-        )
-        continue_btn.click(
-            reset_textbox, 
-            [], 
-            [instruction_txtbox],
-        )
-        
-        summarize_btn.click(
-            chat_batch if batch_enabled else chat_stream, 
-            [context_txtbox, summrize_txtbox, state_chatbot],
-            [state_chatbot, chatbot, context_txtbox],
-            batch=batch_enabled,
-            max_batch_size=args.batch_size,
-        )
-        summarize_btn.click(
-            reset_textbox, 
-            [], 
-            [instruction_txtbox],
-        )              
-
-    demo.queue(
-        concurrency_count=2,
-        max_size=100,
-    ).launch(
-        max_threads=2,
-        server_name="0.0.0.0",
+    continue_btn.click(
+        chat_batch if batch_enabled else chat_stream, 
+        [context_txtbox, continue_txtbox, state_chatbot],
+        [state_chatbot, chatbot, context_txtbox],
+    )
+    continue_btn.click(
+        reset_textbox, 
+        [], 
+        [instruction_txtbox],
     )
 
-if __name__ == "__main__":
-    args = parse_args()
-    run(args)
+    summarize_btn.click(
+        chat_batch if batch_enabled else chat_stream, 
+        [context_txtbox, summrize_txtbox, state_chatbot],
+        [state_chatbot, chatbot, context_txtbox],
+    )
+    summarize_btn.click(
+        reset_textbox, 
+        [], 
+        [instruction_txtbox],
+    )              
+
+demo.queue(
+    concurrency_count=2,
+    max_size=100,
+).launch(
+    max_threads=2,
+    server_name="0.0.0.0",
+)

args.py

@@ -1,51 +0,0 @@
-import argparse
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Gradio Application for Alpaca-LoRA as a chatbot service"
-    )
-    # Dataset related.
-    parser.add_argument(
-        "--base_url",
-        help="Hugging Face Hub URL",
-        default="decapoda-research/llama-7b-hf",
-        type=str,
-    )
-    parser.add_argument(
-        "--ft_ckpt_url",
-        help="Hugging Face Hub URL",
-        default="tloen/alpaca-lora-7b",
-        type=str,
-    )
-    parser.add_argument(
-        "--port",
-        help="PORT number where the app is served",
-        default=6006,
-        type=int,
-    )
-    parser.add_argument(
-        "--batch_size",
-        help="how many requests to handle at the same time",
-        default=1,
-        type=int
-    )        
-    parser.add_argument(
-        "--api_open",
-        help="do you want to open as API",
-        default="no",
-        type=str,
-    )
-    parser.add_argument(
-        "--share",
-        help="do you want to share temporarily (useful in Colab env)",
-        default="no",
-        type=str
-    )
-    parser.add_argument(
-        "--gen_config_path",
-        help="which config to use for GenerationConfig",
-        default="generation_config_default.yaml",
-        type=str
-    )
-
-    return parser.parse_args()