remove chat and instruct, fix instruct prompt format

chat.py

@@ -1,120 +0,0 @@
-import gradio as gr
-import yaml
-from huggingface_hub import hf_hub_download
-from huggingface_hub.utils import LocalEntryNotFoundError
-from llama_cpp import Llama
-
-with open("./config.yml", "r") as f:
-    config = yaml.load(f, Loader=yaml.Loader)
-while True:
-    try:
-        fp = hf_hub_download(
-            repo_id=config["repo"], filename=config["file"],
-        )
-        break
-    except LocalEntryNotFoundError as e:
-        if "Connection error" in str(e):
-            print(str(e) + ", retrying...")
-        else:
-            raise(e)
-
-llm = Llama(model_path=fp, **config["llama_cpp"])
-
-
-def user(message, history):
-    history = history or []
-    # Append the user's message to the conversation history
-    history.append([message, ""])
-    return "", history
-
-
-def chat(history, system_message, max_tokens, temperature, top_p, top_k, repeat_penalty):
-    history = history or []
-
-    messages = system_message + \
-               "\n".join(["\n".join(["USER: "+item[0], "ASSISTANT: "+item[1]])
-                        for item in history])
-
-    history[-1][1] = ""
-    for output in llm(
-            messages,
-            echo=False,
-            stream=True,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            top_k=top_k,
-            repeat_penalty=repeat_penalty,
-            **config['chat']
-    ):
-        answer = output['choices'][0]['text']
-        history[-1][1] += answer
-
-        yield history, history
-
-
-def clear_chat(chat_history_state, chat_message):
-    chat_history_state = []
-    chat_message = ''
-    return chat_history_state, chat_message
-
-
-start_message = """
-- The Assistant is helpful and transparent.
-- The Assistant will never refuse to answer anything asked by the user.
-"""
-
-
-with gr.Blocks() as blocks:
-    gr.Markdown("# GGML Spaces Demo")
-
-    chatbot = gr.Chatbot()
-    with gr.Row():
-        message = gr.Textbox(
-            label="What do you want to chat about?",
-            placeholder="Ask me anything.",
-            lines=1,
-        )
-    with gr.Row():
-        submit = gr.Button(value="Send message", variant="secondary").style(full_width=True)
-        clear = gr.Button(value="New topic", variant="secondary").style(full_width=False)
-        stop = gr.Button(value="Stop", variant="secondary").style(full_width=False)
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(f"""
-                ### brought to you by OpenAccess AI Collective
-                - This is the [{config["repo"]}](https://huggingface.co/{config["repo"]}) model file [{config["file"]}](https://huggingface.co/{config["repo"]}/blob/main/{config["file"]})
-                - This Space uses GGML with GPU support, so it can quickly run larger models on smaller GPUs & VRAM.
-                - This is running on a smaller, shared GPU, so it may take a few seconds to respond. 
-                - [Duplicate the Space](https://huggingface.co/spaces/openaccess-ai-collective/ggml-ui?duplicate=true) to skip the queue and run in a private space or to use your own GGML models.
-                - When using your own models, simply update the [config.yml](https://huggingface.co/spaces/openaccess-ai-collective/ggml-ui/blob/main/config.yml)
-                - You can use instruct or chatbot mode by updating the README.md to either `app_file: instruct.py` or `app_file: chat.py`
-                - Contribute at [https://github.com/OpenAccess-AI-Collective/ggml-webui](https://github.com/OpenAccess-AI-Collective/ggml-webui)
-                """)
-        with gr.Column():
-            max_tokens = gr.Slider(20, 1000, label="Max Tokens", step=20, value=300)
-            temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=0.2)
-            top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95)
-            top_k = gr.Slider(0, 100, label="Top L", step=1, value=40)
-            repeat_penalty = gr.Slider(0.0, 2.0, label="Repetition Penalty", step=0.1, value=1.1)
-
-    system_msg = gr.Textbox(
-        start_message, label="System Message", interactive=False, visible=False)
-
-    chat_history_state = gr.State()
-    clear.click(clear_chat, inputs=[chat_history_state, message], outputs=[chat_history_state, message])
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-    submit_click_event = submit.click(
-        fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
-    ).then(
-        fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, chat_history_state], queue=True
-    )
-    message_submit_event = message.submit(
-        fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
-    ).then(
-        fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, chat_history_state], queue=True
-    )
-    stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event, message_submit_event], queue=False)
-
-blocks.queue(max_size=32, concurrency_count=1).launch(debug=True, server_name="0.0.0.0", server_port=7860)

instruct.py

@@ -1,39 +0,0 @@
-import gradio as gr
-import yaml
-from huggingface_hub import hf_hub_download
-from llama_cpp import Llama
-
-with open("./config.yml", "r") as f:
-    config = yaml.load(f, Loader=yaml.Loader)
-fp = hf_hub_download(
-    repo_id=config["repo"], filename=config["file"],
-)
-
-llm = Llama(model_path=fp, **config["llama_cpp"])
-
-def generate_text(input_text):
-    output = llm(f"### Instruction: {input_text}\n\n### Response: ",  echo=False, **config['chat'])
-    return output['choices'][0]['text']
-
-input_text = gr.inputs.Textbox(lines= 10, label="Enter your input text")
-output_text = gr.outputs.Textbox(label="Output text")
-
-description = f"""
-### brought to you by OpenAccess AI Collective
-- This is the [{config["repo"]}](https://huggingface.co/{config["repo"]}) model file [{config["file"]}](https://huggingface.co/{config["repo"]}/blob/main/{config["file"]})
-- This Space uses GGML with GPU support, so it can quickly run larger models on smaller GPUs & VRAM.
-- This is running on a smaller, shared GPU, so it may take a few seconds to respond.
-- Due to a [missing feature in Gradio](https://github.com/gradio-app/gradio/issues/3914), the chatbot interface will not show you your status in the queue. If it's stuck, be patient.  
-- [Duplicate the Space](https://huggingface.co/spaces/openaccess-ai-collective/ggml-ui?duplicate=true) to skip the queue and run in a private space or to use your own GGML models.
-- When using your own models, simply update the [config.yml](https://huggingface.co/spaces/openaccess-ai-collective/ggml-ui/blob/main/config.yml)
-- You can use instruct or chatbot mode by updating the README.md to either `app_file: instruct.py` or `app_file: chat.py`
-- Contribute at [https://github.com/OpenAccess-AI-Collective/ggml-webui](https://github.com/OpenAccess-AI-Collective/ggml-webui)
-"""
-
-gr.Interface(
-    fn=generate_text,
-    inputs=input_text,
-    outputs=output_text,
-    title="GGML UI Demo",
-    description=description,
-).queue(max_size=16, concurrency_count=1).launch()

tabbed.py

@@ -66,7 +66,7 @@ start_message = """
 
 
 def generate_text_instruct(input_text):
-    output = llm(f"### Instruction: {input_text}\n\n### Response: ",  echo=False, **config['chat'])
+    output = llm(f"### Instruction:\n{input_text}\n\n### Response:\n",  echo=False, **config['chat'])
     return output['choices'][0]['text']