support tabbed interface and fix instruct to use GPU

README.md

@@ -5,7 +5,7 @@ colorFrom: blue
 colorTo: gray
 sdk: gradio
 sdk_version: 3.29.0
-app_file: instruct.py
+app_file: tabbed.py
 pinned: false
 ---
 
@@ -15,5 +15,3 @@ pinned: false
 - Contribute at [https://github.com/OpenAccess-AI-Collective/ggml-webui](https://github.com/OpenAccess-AI-Collective/ggml-webui)
 
 Brought to you by [OpenAccess AI Collective](https://github.com/OpenAccess-AI-Collective)
-
-

instruct.py

@@ -9,7 +9,7 @@ fp = hf_hub_download(
     repo_id=config["repo"], filename=config["file"],
 )
 
-llm = Llama(model_path=fp)
+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'])

tabbed.py

@@ -0,0 +1,146 @@
+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.
+"""
+
+
+def generate_text_instruct(input_text):
+    output = llm(f"### Instruction: {input_text}\n\n### Response: ",  echo=False, **config['chat'])
+    return output['choices'][0]['text']
+
+
+instruct_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)
+    - Contribute at [https://github.com/OpenAccess-AI-Collective/ggml-webui](https://github.com/OpenAccess-AI-Collective/ggml-webui)
+    """
+
+with gr.Blocks() as demo:
+    with gr.Tab("Instruct"):
+        gr.Markdown("# GGML Spaces Instruct Demo")
+
+        gr.Interface(
+            fn=generate_text_instruct,
+            inputs=gr.inputs.Textbox(lines= 10, label="Enter your input text"),
+            outputs=gr.outputs.Textbox(label="Output text"),
+            title="GGML UI Chatbot Demo",
+            description=instruct_description,
+        )
+    with gr.Tab("Chatbot"):
+        gr.Markdown("# GGML Spaces Chatbot 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)
+                    - 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)
+
+
+demo.queue(max_size=32, concurrency_count=1).launch(debug=True, server_name="0.0.0.0", server_port=7860)