Create app.py

app.py

@@ -0,0 +1,193 @@
+import os
+import re
+from time import sleep
+
+import gradio as gr
+import requests
+import yaml
+
+with open("./config.yml", "r") as f:
+    config = yaml.load(f, Loader=yaml.Loader)
+
+
+def make_prediction(prompt, max_tokens=None, temperature=None, top_p=None, top_k=None, repeat_penalty=None):
+    input = config["llm"].copy()
+    input["prompt"] = prompt
+    input["max_tokens"] = max_tokens
+    input["temperature"] = temperature
+    input["top_p"] = top_p
+    input["top_k"] = top_k
+    input["repeat_penalty"] = repeat_penalty
+
+    if config['runpod']['prefer_async']:
+        url = f"https://api.runpod.ai/v2/{config['runpod']['endpoint_id']}/run"
+    else:
+        url = f"https://api.runpod.ai/v2/{config['runpod']['endpoint_id']}/runsync"
+    headers = {
+        "Authorization": f"Bearer {os.environ['RUNPOD_AI_API_KEY']}"
+    }
+    response = requests.post(url, headers=headers, json={"input": input})
+
+    if response.status_code == 200:
+        data = response.json()
+        status = data.get('status')
+        if status == 'COMPLETED':
+            return data["output"]
+        else:
+            task_id = data.get('id')
+            return poll_for_status(task_id)
+
+
+def poll_for_status(task_id):
+    url = f"https://api.runpod.ai/v2/{config['runpod']['endpoint_id']}/status/{task_id}"
+
+    while True:
+        response = requests.get(url)
+        if response.status_code == 200:
+            data = response.json()
+            if data.get('status') == 'COMPLETED':
+                return data["output"]
+        # Sleep for 3 seconds between each request
+        sleep(3)
+
+
+def delay_typer(words, delay=0.8):
+    tokens = re.findall(r'\s*\S+\s*', words)
+    for s in tokens:
+        yield s
+        sleep(delay)
+
+
+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.strip() + "\n" + \
+               "\n".join(["\n".join(["USER: "+item[0], "ASSISTANT: "+item[1]])
+                          for item in history])
+
+    # remove last space from assistant, some models output a ZWSP if you leave a space
+    messages = messages[:-1]
+
+    history[-1][1] = ""
+    prediction = make_prediction(
+        messages,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        repeat_penalty=repeat_penalty,
+    )
+    tokens = re.findall(r'\s*\S+\s*', prediction)
+    for s in tokens:
+        answer = s
+        history[-1][1] += answer
+        # stream the response
+        yield history, history
+        sleep(config['typer']['delay'])
+
+
+
+def rp_chat(history, system_message, max_tokens, temperature, top_p, top_k, repeat_penalty):
+    history = history or []
+
+    messages = "<|system|>" + system_message.strip() + "\n" + \
+               "\n".join(["\n".join(["<|user|>"+item[0], "<|model|>"+item[1]])
+                          for item in history])
+
+    # remove last space from assistant, some models output a ZWSP if you leave a space
+    messages = messages[:-1]
+
+    history[-1][1] = ""
+    prediction = make_prediction(
+        messages,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        repeat_penalty=repeat_penalty,
+    )
+    tokens = re.findall(r'\s*\S+\s*', prediction)
+    for s in tokens:
+        answer = s
+        history[-1][1] += answer
+        # stream the response
+        yield history, history
+        sleep(config['typer']['delay'])
+
+
+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 demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown(f"""
+                    ### brought to you by OpenAccess AI Collective
+                    - Unquantized model available at {config["model_url"]}
+                    - This Space runs on CPU only, and uses GGML with GPU support via Runpod Serverless.
+                    - Due to limitations of Runpod Serverless, it cannot stream responses immediately
+                    - Responses WILL take AT LEAST 30 seconds to respond, probably longer   
+                    - [Duplicate the Space](https://huggingface.co/spaces/openaccess-ai-collective/ggml-runpod-ui?duplicate=true) to skip the queue and run in a private space or to use your own GGML models. You will need to configure you own runpod serverless endpoint.
+                    - When using your own models, simply update the [config.yml](https://huggingface.co/spaces/openaccess-ai-collective/ggml-runpod-ui/blob/main/config.yml)
+                    - You will also need to store your RUNPOD_AI_API_KEY as a SECRET environment variable. DO NOT STORE THIS IN THE config.yml.
+                    - Contribute at [https://github.com/OpenAccess-AI-Collective/ggml-webui](https://github.com/OpenAccess-AI-Collective/ggml-webui)
+                    - Many thanks to [TheBloke](https://huggingface.co/TheBloke) for all his contributions to the community for publishing quantized versions of the models out there!  
+                    """)
+    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=3,
+            )
+        with gr.Row():
+            submit = gr.Button(value="Send message", variant="secondary").style(full_width=True)
+            roleplay = gr.Button(value="Roleplay", 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():
+                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=0.8)
+                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 K", 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=True, visible=True, placeholder="system prompt, useful for RP", lines=5)
+
+        chat_history_state = gr.State()
+        clear.click(clear_chat, inputs=[chat_history_state, message], outputs=[chat_history_state, message], queue=False)
+        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
+        )
+        roleplay_click_event = roleplay.click(
+            fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
+        ).then(
+            fn=rp_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, roleplay_click_event], queue=False)
+
+demo.queue(**config["queue"]).launch(debug=True, server_name="0.0.0.0", server_port=7860)