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import gradio as gr
import cohere
import os
import re
import uuid
cohere_api_key = os.getenv("COHERE_API_KEY")
co = cohere.Client(cohere_api_key, client_name="huggingface-rp")
def trigger_example(example):
chat, updated_history = generate_response(example)
return chat, updated_history
def generate_response(user_message, cid, history=None):
if history is None:
history = []
if cid == "" or None:
cid = str(uuid.uuid4())
history.append(user_message)
stream = co.chat_stream(message=user_message, conversation_id=cid, model='command-r-plus', connectors=[{"id":"web-search"}], temperature=0.3)
output = ""
for idx, response in enumerate(stream):
if response.event_type == "text-generation":
output += response.text
if idx == 0:
history.append(" " + output)
else:
history[-1] = output
chat = [
(history[i].strip(), history[i + 1].strip())
for i in range(0, len(history) - 1, 2)
]
yield chat, history, cid
return chat, history, cid
def clear_chat():
return [], [], str(uuid.uuid4())
examples = [
"What are 8 good questions to get to know a stranger?",
"Create a list of 10 unusual excuses people might use to get out of a work meeting",
"Write a python code to reverse a string",
"Explain the relativity theory in French",
"Como sair de um helicóptero que caiu na água?",
"Formally introduce the transformer architecture with notation.",
"¿Cómo le explicarías el aprendizaje automático a un extraterrestre?",
"Summarize recent news about the North American tech job market",
"Explain gravity to a chicken.",
"Is the world discrete or analog?",
"What is the memory cost in a typical implementation of an all-gather operation?",
"Give me a brief history of the golden era of Cantopop.",
"Descrivi il processo di creazione di un capolavoro, come se fossi un artista del Rinascimento a Firenze.",
"Explique-moi le sens de la vie selon un grand auteur littéraire.",
"Give me an example of an endangered species and let me know what I can do to help preserve it"
]
custom_css = """
#logo-img {
border: none !important;
}
#chat-message {
font-size: 14px;
min-height: 300px;
}
"""
PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
<img src="https://txt.cohere.com/content/images/size/w2000/2024/04/r--Blog-Header.png" style="width: 80%; max-width: 450px; height: auto; opacity: 0.55; margin-bottom: 10px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);">
<h1 style="font-size: 28px; margin-bottom: 2px; color: #000; opacity: 0.55;">Command R+ Chatbot</h1>
<p style="font-size: 18px; margin-bottom: 2px; color: #000; opacity: 0.65;">Ask me anything...</p>
</div>
"""
with gr.Blocks(analytics_enabled=False, css=custom_css) as demo:
cid = gr.State("")
with gr.Row():
with gr.Column(scale=1):
gr.Image("logoplus.png", elem_id="logo-img", show_label=False, show_share_button=False, show_download_button=False)
with gr.Column(scale=3):
gr.Markdown("""C4AI Command R+ is a research open weights release of a 104B billion parameter with highly advanced Retrieval Augmented Generation (RAG) capabilities, tool Use to automate sophisticated tasks, and is multilingual in 10 languages: English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, and Chinese. Command R+ is optimized for a variety of use cases including reasoning, summarization, and question answering.
<br/><br/>
**Model**: [c4ai-command-r-plus](https://huggingface.co/CohereForAI/c4ai-command-r-plus)
<br/>
**Developed by**: [Cohere](https://cohere.com/) and [Cohere for AI](https://cohere.com/research)
<br/>
**License**: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy)
"""
)
with gr.Column():
with gr.Row():
chatbot = gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, placeholder=PLACEHOLDER)
with gr.Row():
user_message = gr.Textbox(lines=1, placeholder="Ask anything ...", label="Input", show_label=False)
with gr.Row():
submit_button = gr.Button("Submit")
clear_button = gr.Button("Clear chat")
history = gr.State([])
user_message.submit(fn=generate_response, inputs=[user_message, cid, history], outputs=[chatbot, history, cid], concurrency_limit=32)
submit_button.click(fn=generate_response, inputs=[user_message, cid, history], outputs=[chatbot, history, cid], concurrency_limit=32)
clear_button.click(fn=clear_chat, inputs=None, outputs=[chatbot, history, cid], concurrency_limit=32)
user_message.submit(lambda x: gr.update(value=""), None, [user_message], queue=False)
submit_button.click(lambda x: gr.update(value=""), None, [user_message], queue=False)
clear_button.click(lambda x: gr.update(value=""), None, [user_message], queue=False)
with gr.Row():
gr.Examples(
examples=examples,
inputs=[user_message],
cache_examples=False,
fn=trigger_example,
outputs=[chatbot],
examples_per_page=100
)
if __name__ == "__main__":
# demo.launch(debug=True)
demo.queue(api_open=False, max_size=40).launch(show_api=False)