Update backup.txt

backup.txt

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+import time
+import logging
+import gradio as gr
+
+from src.llm_boilers import llm_boiler
+
+
+logging.basicConfig(format="%(asctime)s - %(message)s", level=logging.INFO)
+logging.warning("READY. App started...")
+
+
+class Chat:
+    default_system_prompt = "A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers."
+    system_format = "<|im_start|>system\n{}<|im_end|>\n"
+
+    def __init__(
+        self, system: str = None, user: str = None, assistant: str = None
+    ) -> None:
+        if system is not None:
+            self.set_system_prompt(system)
+        else:
+            self.reset_system_prompt()
+        self.user = user if user else "<|im_start|>user\n{}<|im_end|>\n"
+        self.assistant = (
+            assistant if assistant else "<|im_start|>assistant\n{}<|im_end|>\n"
+        )
+        self.response_prefix = self.assistant.split("{}")[0]
+
+    def set_system_prompt(self, system_prompt):
+        # self.system = self.system_format.format(system_prompt)
+        return system_prompt
+
+    def reset_system_prompt(self):
+        return self.set_system_prompt(self.default_system_prompt)
+
+    def history_as_formatted_str(self, system, history) -> str:
+        system = self.system_format.format(system)
+        text = system + "".join(
+            [
+                "\n".join(
+                    [
+                        self.user.format(item[0]),
+                        self.assistant.format(item[1]),
+                    ]
+                )
+                for item in history[:-1]
+            ]
+        )
+        text += self.user.format(history[-1][0])
+        text += self.response_prefix
+        # stopgap solution to too long sequences
+        if len(text) > 4500:
+            # delete from the middle between <|im_start|> and <|im_end|>
+            # find the middle ones, then expand out
+            start = text.find("<|im_start|>", 139)
+            end = text.find("<|im_end|>", 139)
+            while end < len(text) and len(text) > 4500:
+                end = text.find("<|im_end|>", end + 1)
+                text = text[:start] + text[end + 1 :]
+        if len(text) > 4500:
+            # the nice way didn't work, just truncate
+            # deleting the beginning
+            text = text[-4500:]
+
+        return text
+
+    def clear_history(self, history):
+        return []
+
+    def turn(self, user_input: str):
+        self.user_turn(user_input)
+        return self.bot_turn()
+
+    def user_turn(self, user_input: str, history):
+        history.append([user_input, ""])
+        return user_input, history
+
+    def bot_turn(self, system, history, openai_key):
+        conversation = self.history_as_formatted_str(system, history)
+        assistant_response = call_inf_server(conversation, openai_key)
+        # history[-1][-1] = assistant_response
+        # return history
+        history[-1][1] = ""
+        for chunk in assistant_response:
+            try:
+                decoded_output = chunk["choices"][0]["delta"]["content"]
+                history[-1][1] += decoded_output
+                yield history
+            except KeyError:
+                pass
+
+
+def call_inf_server(prompt, openai_key):
+    model_id = "gpt-3.5-turbo"  # "gpt-3.5-turbo-16k",
+    model = llm_boiler(model_id, openai_key)
+    logging.warning(f'Inf via "{model_id}"" for prompt "{prompt}"')
+
+    try:
+        # run text generation
+        response = model.run(prompt, temperature=1.0)
+        logging.warning(f"Result of text generation: {response}")
+        return response
+
+    except Exception as e:
+        # assume it is our error
+        # just wait and try one more time
+        print(e)
+        time.sleep(2)
+        response = model.run(prompt, temperature=1.0)
+        logging.warning(f"Result of text generation: {response}")
+        return response
+
+
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    css=".disclaimer {font-variant-caps: all-small-caps;}",
+) as demo:
+    gr.Markdown(
+        """<h1><center>Chat with gpt-3.5-turbo</center></h1>
+        This is a lightweight demo of gpt-3.5-turbo conversation completion. It was designed as a template for in-context learning applications to be built on top of.
+"""
+    )
+    conversation = Chat()
+    with gr.Row():
+        with gr.Column():
+            # to do: change to openaikey input for public release
+            openai_key = gr.Textbox(
+                label="OpenAI Key",
+                value="",
+                type="password",
+                placeholder="sk..",
+                info="You have to provide your own OpenAI API key.",
+            )
+    chatbot = gr.Chatbot().style(height=400)
+    with gr.Row():
+        with gr.Column():
+            msg = gr.Textbox(
+                label="Chat Message Box",
+                placeholder="Chat Message Box",
+                show_label=False,
+            ).style(container=False)
+        with gr.Column():
+            with gr.Row():
+                submit = gr.Button("Submit")
+                stop = gr.Button("Stop")
+                clear = gr.Button("Clear")
+    with gr.Row():
+        with gr.Accordion("Advanced Options:", open=False):
+            with gr.Row():
+                with gr.Column(scale=2):
+                    system = gr.Textbox(
+                        label="System Prompt",
+                        value=Chat.default_system_prompt,
+                        show_label=False,
+                    ).style(container=False)
+                with gr.Column():
+                    with gr.Row():
+                        change = gr.Button("Change System Prompt")
+                        reset = gr.Button("Reset System Prompt")
+    with gr.Row():
+        gr.Markdown(
+            "Disclaimer: The gpt-3.5-turbo model can produce factually incorrect output, and should not be solely relied on to produce "
+            "factually accurate information. The gpt-3.5-turbo model was trained on various public datasets; while great efforts "
+            "have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
+            "biased, or otherwise offensive outputs.",
+            elem_classes=["disclaimer"],
+        )
+    with gr.Row():
+        gr.Markdown(
+            "[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)",
+            elem_classes=["disclaimer"],
+        )
+
+    submit_event = msg.submit(
+        fn=conversation.user_turn,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).then(
+        fn=conversation.bot_turn,
+        inputs=[system, chatbot, openai_key],
+        outputs=[chatbot],
+        queue=True,
+    )
+    submit_click_event = submit.click(
+        fn=conversation.user_turn,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).then(
+        fn=conversation.bot_turn,
+        inputs=[system, chatbot, openai_key],
+        outputs=[chatbot],
+        queue=True,
+    )
+    stop.click(
+        fn=None,
+        inputs=None,
+        outputs=None,
+        cancels=[submit_event, submit_click_event],
+        queue=False,
+    )
+    clear.click(lambda: None, None, chatbot, queue=False).then(
+        fn=conversation.clear_history,
+        inputs=[chatbot],
+        outputs=[chatbot],
+        queue=False,
+    )
+    change.click(
+        fn=conversation.set_system_prompt,
+        inputs=[system],
+        outputs=[system],
+        queue=False,
+    )
+    reset.click(
+        fn=conversation.reset_system_prompt,
+        inputs=[],
+        outputs=[system],
+        queue=False,
+    )
+
+
+demo.queue(max_size=36, concurrency_count=14).launch(debug=True)